ParadisEO-MOEO 1.0 beta version working with autotools

git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@396 331e1502-861f-0410-8da2-ba01fb791d7f
This commit is contained in:
liefooga 2007-06-26 12:39:03 +00:00
commit d3cdc48b64
129 changed files with 11745 additions and 0 deletions

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Arnaud Liefooghe
Thomas Legrand
Sébastien Cahon
Abdelhakim Deneche

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SUBDIRS = doc src tutorial
clean_all: clean_aux clean_doc
-@make clean
-@(rm -rf aclocal.m4 autom4te.cache configure config.* CO* dep* INST* install-sh miss* mkins* Makefile Makefile.in;\
cd doc; rm -rf Makefile Makefile.in *.doxytag; cd ../src ; rm -rf Makefile Makefile.in; cd core ; rm -rf Makefile Makefile.in;\
cd ../../tutorial; rm -rf Makefile Makefile.in; cd Lesson1; rm -rf .deps/ Makefile Makefile.in *.status;\
cd flowshop; rm -rf .deps/ Makefile Makefile.in; cd ../../Lesson2; rm -rf .deps/ Makefile Makefile.in)
clean_aux:
-@find . \( -name "*~" -o -name "*.old" -o -name "#*" -o -name "*.bak" \) -print -exec rm -rf \{\} \;
doc:
-@(cd doc; make doc)
clean_doc:
-@(cd doc; make clean-local)

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* release 1.0

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PARADISEO-MOEO README FILE
=======================================================================
check latest news at http://paradiseo.gforge.inria.fr/
=======================================================================
Welcome to ParadisEO-MOEO, the Multi-Objective Evolving Objects library.
The latest news about ParadisEO-MOEO can be found on the gforge repository at
http://paradiseo.gforge.inria.fr/
In case of any problem, please e-mail us at
paradiseo-help@lists.gforge.inria.fr
==================================================================
BUILDING PARADISEO-MOEO
==================================================================
The basic installation procedure goes the following:
Go to your build-directory and run
> ./autogen.sh --with-EOdir=$(EO_SRC)
> make
> make doc
where $(EO_SRC) is the top-level source directory of PARADISEO-EO.
In case of problems, you can read the INSTALL file - but remember this
is a standard installation file from GNU and that it contains nothing
specific about PARADISEO-MOEO.
To clean everything, the simply run
> make clean_all
===================================================================
DIRECTORY STRUCTURE
===================================================================
After unpacking the archive file, you should end up with the following
structure:
.../ The main PARADISEO-MOEO directory, created when unpacking.
|
+-- src SOURCE directory Contains most PARADISEO-MOEO .h files.
|
+-- docs DOCUMENTATION directory (generated by Doxygen).
| |
| +- html HTML files - start at index.html.
| |
| +- latex latex files - use to generate Postcript doc.
| |
| +- man Unix man format documentation.
|
|
+-- tutorials APPLICATIONS - one directory per separate application.
|
+-- lesson1 A bi-objective flow-shop problem example with main algorithms.
| |
| +- flowshop Flow-shop source files
| |
| +- benchmarks Instance files for testing.
|
|-- lesson2 Implement NSGA-II for the SCH1 problem.
===================================================================
NOTES
===================================================================
Mailing list : paradiseo-help@lists.gforge.inria.fr

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#!/bin/sh
# Run this to generate all the initial makefiles, etc.
srcdir=`dirname $0`
PKG_NAME="the package."
DIE=0
(autoconf --version) < /dev/null > /dev/null 2>&1 || {
echo
echo "**Error**: You must have \`autoconf' installed to."
echo "Download the appropriate package for your distribution,"
echo "or get the source tarball at ftp://ftp.gnu.org/pub/gnu/"
DIE=1
}
(grep "^AM_PROG_LIBTOOL" $srcdir/configure.in >/dev/null) && {
(libtool --version) < /dev/null > /dev/null 2>&1 || {
echo
echo "**Error**: You must have \`libtool' installed."
echo "Get ftp://ftp.gnu.org/pub/gnu/libtool-1.2d.tar.gz"
echo "(or a newer version if it is available)"
DIE=1
}
}
grep "^AM_GNU_GETTEXT" $srcdir/configure.in >/dev/null && {
grep "sed.*POTFILES" $srcdir/configure.in >/dev/null || \
(gettext --version) < /dev/null > /dev/null 2>&1 || {
echo
echo "**Error**: You must have \`gettext' installed."
echo "Get ftp://alpha.gnu.org/gnu/gettext-0.10.35.tar.gz"
echo "(or a newer version if it is available)"
DIE=1
}
}
grep "^AM_GNOME_GETTEXT" $srcdir/configure.in >/dev/null && {
grep "sed.*POTFILES" $srcdir/configure.in >/dev/null || \
(gettext --version) < /dev/null > /dev/null 2>&1 || {
echo
echo "**Error**: You must have \`gettext' installed."
echo "Get ftp://alpha.gnu.org/gnu/gettext-0.10.35.tar.gz"
echo "(or a newer version if it is available)"
DIE=1
}
}
(automake --version) < /dev/null > /dev/null 2>&1 || {
echo
echo "**Error**: You must have \`automake' installed."
echo "Get ftp://ftp.gnu.org/pub/gnu/automake-1.3.tar.gz"
echo "(or a newer version if it is available)"
DIE=1
NO_AUTOMAKE=yes
}
# if no automake, don't bother testing for aclocal
test -n "$NO_AUTOMAKE" || (aclocal --version) < /dev/null > /dev/null 2>&1 || {
echo
echo "**Error**: Missing \`aclocal'. The version of \`automake'"
echo "installed doesn't appear recent enough."
echo "Get ftp://ftp.gnu.org/pub/gnu/automake-1.3.tar.gz"
echo "(or a newer version if it is available)"
DIE=1
}
if test "$DIE" -eq 1; then
exit 1
fi
if test -z "$*"; then
echo "**Warning**: I am going to run \`configure' with no arguments."
echo "If you wish to pass any to it, please specify them on the"
echo \`$0\'" command line."
echo
fi
case $CC in
xlc )
am_opt=--include-deps;;
esac
for coin in `find $srcdir -name configure.in -print`
do
dr=`dirname $coin`
if test -f $dr/NO-AUTO-GEN; then
echo skipping $dr -- flagged as no auto-gen
else
echo processing $dr
macrodirs=`sed -n -e 's,AM_ACLOCAL_INCLUDE(\(.*\)),\1,gp' < $coin`
( cd $dr
aclocalinclude="$ACLOCAL_FLAGS"
for k in $macrodirs; do
if test -d $k; then
aclocalinclude="$aclocalinclude -I $k"
##else
## echo "**Warning**: No such directory \`$k'. Ignored."
fi
done
if grep "^AM_GNU_GETTEXT" configure.in >/dev/null; then
if grep "sed.*POTFILES" configure.in >/dev/null; then
: do nothing -- we still have an old unmodified configure.in
else
echo "Creating $dr/aclocal.m4 ..."
test -r $dr/aclocal.m4 || touch $dr/aclocal.m4
echo "Running gettextize... Ignore non-fatal messages."
echo "no" | gettextize --force --copy
echo "Making $dr/aclocal.m4 writable ..."
test -r $dr/aclocal.m4 && chmod u+w $dr/aclocal.m4
fi
fi
if grep "^AM_GNOME_GETTEXT" configure.in >/dev/null; then
echo "Creating $dr/aclocal.m4 ..."
test -r $dr/aclocal.m4 || touch $dr/aclocal.m4
echo "Running gettextize... Ignore non-fatal messages."
echo "no" | gettextize --force --copy
echo "Making $dr/aclocal.m4 writable ..."
test -r $dr/aclocal.m4 && chmod u+w $dr/aclocal.m4
fi
if grep "^AM_PROG_LIBTOOL" configure.in >/dev/null; then
echo "Running libtoolize..."
libtoolize --force --copy
fi
echo "Running aclocal $aclocalinclude ..."
aclocal $aclocalinclude
if grep "^AM_CONFIG_HEADER" configure.in >/dev/null; then
echo "Running autoheader..."
autoheader
fi
echo "Running automake --gnu $am_opt ..."
automake --add-missing --gnu $am_opt
echo "Running autoconf ..."
autoconf
)
fi
done
#conf_flags="--enable-maintainer-mode --enable-compile-warnings" #--enable-iso-c
if test x$NOCONFIGURE = x; then
echo Running $srcdir/configure $conf_flags "$@" ...
$srcdir/configure $conf_flags "$@" \
&& echo Now type \`make\' to compile $PKG_NAME
else
echo Skipping configure process.
fi

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AC_INIT(configure.in)
AM_INIT_AUTOMAKE(moeo, 0.1)
AC_ISC_POSIX
AC_PROG_CXX
AM_PROG_CC_STDC
AC_HEADER_STDC
AC_PROG_RANLIB
AC_PROG_INSTALL
AC_PROG_LN_S
AC_PROG_MAKE_SET
AC_C_CONST
AC_C_INLINE
AC_TYPE_SIZE_T
AC_SUBST(EO_DIR)
dnl EO
AC_ARG_WITH(EOdir,
--with-EOdir : Giving the path of the EO tree.,
EO_DIR="$withval"
if test ! -d $EO_DIR
then
echo ""
echo "# --with-EOdir Invalid directory $withval"
exit 1
fi,
echo ""
echo "# You forgot to give the path of the EO tree !"
echo "# Please write something like ... './configure --with-EOdir=\$HOME/eo'"
exit 1
)
AC_OUTPUT([
Makefile
src/Makefile
src/core/Makefile
doc/Makefile
tutorial/Makefile
tutorial/Lesson1/Makefile
tutorial/Lesson1/flowshop/Makefile
tutorial/Lesson2/Makefile
])

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doc:
-@doxygen moeo.doxyfile
clean-local:
rm -rf html latex man

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/** @mainpage Welcome to ParadisEO-MOEO
@section intro Introduction
ParadisEO-MOEO is a white-box object-oriented generic framework dedicated to the flexible design of evolutionary multi-objective algorithms.
This paradigm-free software embeds some features and techniques for Pareto-based resolution and
aims to provide a set of classes allowing to ease and speed up the development of computationally efficient programs.
It is based on a clear conceptual distinction between the solution methods and the multi-objective problems they are intended to solve.
This separation confers a maximum design and code reuse.
ParadisEO-MOEO provides a broad range of archive-related features (such as elitism or performance metrics)
and the most common Pareto-based fitness assignment strategies (MOGA, NSGA, SPEA, IBEA and more).
Furthermore, parallel and distributed models as well as hybridization mechanisms can be applied to an algorithm designed within ParadisEO-MOEO
using the whole version of ParadisEO.
@section tutorials Tutorials
Tutorials for ParadisEO-MOEO are available <a href="http://paradiseo.gforge.inria.fr/index.php?n=Paradiseo.UsersGuides">here</a>.
@section install Installation
The installation procedure of the package is detailed in the <a
href="../../README">README</a> file in the top-directory of the source-tree.
@section design Overall Design
For an introduction to the design of ParadisEO-MOEO,
you can look at the <a href="http://paradiseo.gforge.inria.fr">ParadisEO website</a>.
*/
/** @page webpages Related webpages
- ParadisEO <a href="http://paradiseo.gforge.inria.fr">homepage</a>
- INRIA GForge <a href="http://gforge.inria.fr/projects/paradiseo/">project page</a>
- <a href="../../README">README</a>
- <a href="../../NEWS">NEWS</a>
*/

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# Doxyfile 1.5.1
#---------------------------------------------------------------------------
# Project related configuration options
#---------------------------------------------------------------------------
PROJECT_NAME = ParadisEO-MOEO
PROJECT_NUMBER = 1.0
OUTPUT_DIRECTORY =
CREATE_SUBDIRS = NO
OUTPUT_LANGUAGE = English
USE_WINDOWS_ENCODING = NO
BRIEF_MEMBER_DESC = YES
REPEAT_BRIEF = YES
ABBREVIATE_BRIEF = "The $name class" \
"The $name widget" \
"The $name file" \
is \
provides \
specifies \
contains \
represents \
a \
an \
the
ALWAYS_DETAILED_SEC = NO
INLINE_INHERITED_MEMB = NO
FULL_PATH_NAMES = NO
STRIP_FROM_PATH =
STRIP_FROM_INC_PATH =
SHORT_NAMES = NO
JAVADOC_AUTOBRIEF = YES
MULTILINE_CPP_IS_BRIEF = NO
DETAILS_AT_TOP = NO
INHERIT_DOCS = YES
SEPARATE_MEMBER_PAGES = NO
TAB_SIZE = 8
ALIASES =
OPTIMIZE_OUTPUT_FOR_C = NO
OPTIMIZE_OUTPUT_JAVA = NO
BUILTIN_STL_SUPPORT = NO
DISTRIBUTE_GROUP_DOC = NO
SUBGROUPING = YES
#---------------------------------------------------------------------------
# Build related configuration options
#---------------------------------------------------------------------------
EXTRACT_ALL = NO
EXTRACT_PRIVATE = YES
EXTRACT_STATIC = YES
EXTRACT_LOCAL_CLASSES = YES
EXTRACT_LOCAL_METHODS = NO
HIDE_UNDOC_MEMBERS = YES
HIDE_UNDOC_CLASSES = YES
HIDE_FRIEND_COMPOUNDS = NO
HIDE_IN_BODY_DOCS = NO
INTERNAL_DOCS = NO
CASE_SENSE_NAMES = YES
HIDE_SCOPE_NAMES = NO
SHOW_INCLUDE_FILES = YES
INLINE_INFO = YES
SORT_MEMBER_DOCS = NO
SORT_BRIEF_DOCS = NO
SORT_BY_SCOPE_NAME = NO
GENERATE_TODOLIST = YES
GENERATE_TESTLIST = YES
GENERATE_BUGLIST = YES
GENERATE_DEPRECATEDLIST= YES
ENABLED_SECTIONS =
MAX_INITIALIZER_LINES = 30
SHOW_USED_FILES = YES
SHOW_DIRECTORIES = NO
FILE_VERSION_FILTER =
#---------------------------------------------------------------------------
# configuration options related to warning and progress messages
#---------------------------------------------------------------------------
QUIET = YES
WARNINGS = YES
WARN_IF_UNDOCUMENTED = YES
WARN_IF_DOC_ERROR = YES
WARN_NO_PARAMDOC = NO
WARN_FORMAT = "$file:$line: $text"
WARN_LOGFILE =
#---------------------------------------------------------------------------
# configuration options related to the input files
#---------------------------------------------------------------------------
INPUT = . ../src
FILE_PATTERNS = *.cpp \
*.h \
NEWS README
RECURSIVE = YES
EXCLUDE =
EXCLUDE_SYMLINKS = NO
EXCLUDE_PATTERNS =
EXAMPLE_PATH =
EXAMPLE_PATTERNS = *
EXAMPLE_RECURSIVE = NO
IMAGE_PATH =
INPUT_FILTER =
FILTER_PATTERNS =
FILTER_SOURCE_FILES = NO
#---------------------------------------------------------------------------
# configuration options related to source browsing
#---------------------------------------------------------------------------
SOURCE_BROWSER = YES
INLINE_SOURCES = NO
STRIP_CODE_COMMENTS = YES
REFERENCED_BY_RELATION = YES
REFERENCES_RELATION = YES
REFERENCES_LINK_SOURCE = YES
USE_HTAGS = NO
VERBATIM_HEADERS = YES
#---------------------------------------------------------------------------
# configuration options related to the alphabetical class index
#---------------------------------------------------------------------------
ALPHABETICAL_INDEX = YES
COLS_IN_ALPHA_INDEX = 3
IGNORE_PREFIX = moeo
#---------------------------------------------------------------------------
# configuration options related to the HTML output
#---------------------------------------------------------------------------
GENERATE_HTML = YES
HTML_OUTPUT = html
HTML_FILE_EXTENSION = .html
HTML_HEADER =
HTML_FOOTER =
HTML_STYLESHEET =
HTML_ALIGN_MEMBERS = YES
GENERATE_HTMLHELP = NO
CHM_FILE =
HHC_LOCATION =
GENERATE_CHI = NO
BINARY_TOC = NO
TOC_EXPAND = NO
DISABLE_INDEX = NO
ENUM_VALUES_PER_LINE = 4
GENERATE_TREEVIEW = YES
TREEVIEW_WIDTH = 250
#---------------------------------------------------------------------------
# configuration options related to the LaTeX output
#---------------------------------------------------------------------------
GENERATE_LATEX = YES
LATEX_OUTPUT = latex
LATEX_CMD_NAME = latex
MAKEINDEX_CMD_NAME = makeindex
COMPACT_LATEX = NO
PAPER_TYPE = a4wide
EXTRA_PACKAGES =
LATEX_HEADER =
PDF_HYPERLINKS = NO
USE_PDFLATEX = NO
LATEX_BATCHMODE = NO
LATEX_HIDE_INDICES = NO
#---------------------------------------------------------------------------
# configuration options related to the RTF output
#---------------------------------------------------------------------------
GENERATE_RTF = NO
RTF_OUTPUT = rtf
COMPACT_RTF = NO
RTF_HYPERLINKS = NO
RTF_STYLESHEET_FILE =
RTF_EXTENSIONS_FILE =
#---------------------------------------------------------------------------
# configuration options related to the man page output
#---------------------------------------------------------------------------
GENERATE_MAN = YES
MAN_OUTPUT = man
MAN_EXTENSION = .3
MAN_LINKS = NO
#---------------------------------------------------------------------------
# configuration options related to the XML output
#---------------------------------------------------------------------------
GENERATE_XML = NO
XML_OUTPUT = xml
XML_SCHEMA =
XML_DTD =
XML_PROGRAMLISTING = YES
#---------------------------------------------------------------------------
# configuration options for the AutoGen Definitions output
#---------------------------------------------------------------------------
GENERATE_AUTOGEN_DEF = NO
#---------------------------------------------------------------------------
# configuration options related to the Perl module output
#---------------------------------------------------------------------------
GENERATE_PERLMOD = NO
PERLMOD_LATEX = NO
PERLMOD_PRETTY = YES
PERLMOD_MAKEVAR_PREFIX =
#---------------------------------------------------------------------------
# Configuration options related to the preprocessor
#---------------------------------------------------------------------------
ENABLE_PREPROCESSING = YES
MACRO_EXPANSION = NO
EXPAND_ONLY_PREDEF = NO
SEARCH_INCLUDES = YES
INCLUDE_PATH =
INCLUDE_FILE_PATTERNS =
PREDEFINED =
EXPAND_AS_DEFINED =
SKIP_FUNCTION_MACROS = YES
#---------------------------------------------------------------------------
# Configuration::additions related to external references
#---------------------------------------------------------------------------
TAGFILES = ../../paradiseo-eo/doc/eo.doxytag=../../../paradiseo-eo/doc/html
GENERATE_TAGFILE = moeo.doxytag
ALLEXTERNALS = NO
EXTERNAL_GROUPS = YES
PERL_PATH = /usr/bin/perl
#---------------------------------------------------------------------------
# Configuration options related to the dot tool
#---------------------------------------------------------------------------
CLASS_DIAGRAMS = YES
HIDE_UNDOC_RELATIONS = YES
HAVE_DOT = NO
CLASS_GRAPH = YES
COLLABORATION_GRAPH = YES
GROUP_GRAPHS = YES
UML_LOOK = NO
TEMPLATE_RELATIONS = NO
INCLUDE_GRAPH = YES
INCLUDED_BY_GRAPH = YES
CALL_GRAPH = NO
CALLER_GRAPH = NO
GRAPHICAL_HIERARCHY = YES
DIRECTORY_GRAPH = YES
DOT_IMAGE_FORMAT = png
DOT_PATH =
DOTFILE_DIRS =
MAX_DOT_GRAPH_WIDTH = 1024
MAX_DOT_GRAPH_HEIGHT = 1024
MAX_DOT_GRAPH_DEPTH = 0
DOT_TRANSPARENT = NO
DOT_MULTI_TARGETS = NO
GENERATE_LEGEND = YES
DOT_CLEANUP = YES
#---------------------------------------------------------------------------
# Configuration::additions related to the search engine
#---------------------------------------------------------------------------
SEARCHENGINE = YES

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SUBDIRS = core

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoAlgo.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOALGO_H_
#define MOEOALGO_H_
/**
* Abstract class for multi-objective algorithms.
*/
class moeoAlgo {};
#endif /*MOEOALGO_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoCombinedLS.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOCOMBINEDLS_H_
#define MOEOCOMBINEDLS_H_
#include <vector>
#include <algo/moeoLS.h>
#include <archive/moeoArchive.h>
/**
* This class allows to embed a set of local searches that are sequentially applied,
* and so working and updating the same archive of non-dominated solutions.
*/
template < class MOEOT, class Type >
class moeoCombinedLS : public moeoLS < MOEOT, Type >
{
public:
/**
* Ctor
* @param _first_mols the first multi-objective local search to add
*/
moeoCombinedLS(moeoLS < MOEOT, Type > & _first_mols)
{
combinedLS.push_back (& _first_mols);
}
/**
* Adds a new local search to combine
* @param _mols the multi-objective local search to add
*/
void add(moeoLS < MOEOT, Type > & _mols)
{
combinedLS.push_back(& _mols);
}
/**
* Gives a new solution in order to explore the neigborhood.
* The new non-dominated solutions are added to the archive
* @param _type the object to apply the local search to
* @param _arch the archive of non-dominated solutions
*/
void operator () (Type _type, moeoArchive < MOEOT > & _arch)
{
for (unsigned int i=0; i<combinedLS.size(); i++)
combinedLS[i] -> operator()(_type, _arch);
}
private:
/** the vector that contains the combined LS */
std::vector< moeoLS < MOEOT, Type > * > combinedLS;
};
#endif /*MOEOCOMBINEDLS_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoEA.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOEA_H_
#define MOEOEA_H_
#include <eoAlgo.h>
#include <algo/moeoAlgo.h>
/**
* Abstract class for multi-objective evolutionary algorithms.
*/
template < class MOEOT >
class moeoEA : public moeoAlgo, public eoAlgo < MOEOT > {};
#endif /*MOEOEA_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoEasyEA.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef _MOEOEASYEA_H
#define _MOEOEASYEA_H
#include <apply.h>
#include <eoBreed.h>
#include <eoContinue.h>
#include <eoMergeReduce.h>
#include <eoPopEvalFunc.h>
#include <eoSelect.h>
#include <eoTransform.h>
#include <algo/moeoEA.h>
#include <diversity/moeoDiversityAssignment.h>
#include <diversity/moeoDummyDiversityAssignment.h>
#include <fitness/moeoFitnessAssignment.h>
#include <replacement/moeoReplacement.h>
/**
* An easy class to design multi-objective evolutionary algorithms.
*/
template < class MOEOT >
class moeoEasyEA: public moeoEA < MOEOT >
{
public:
/**
* Ctor taking a breed and merge.
* @param _continuator the stopping criteria
* @param _eval the evaluation functions
* @param _breed the breeder
* @param _replace the replacement strategy
* @param _fitnessEval the fitness evaluation scheme
* @param _diversityEval the diversity evaluation scheme
* @param _evalFitAndDivBeforeSelection put this parameter to 'true' if you want to re-evalue the fitness and the diversity of the population before the selection process
*/
moeoEasyEA(eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoBreed < MOEOT > & _breed, moeoReplacement < MOEOT > & _replace,
moeoFitnessAssignment < MOEOT > & _fitnessEval, moeoDiversityAssignment < MOEOT > & _diversityEval, bool _evalFitAndDivBeforeSelection = false)
:
continuator(_continuator), eval (_eval), loopEval(_eval), popEval(loopEval), selectTransform(dummySelect, dummyTransform), breed(_breed), mergeReduce(dummyMerge, dummyReduce), replace(_replace),
fitnessEval(_fitnessEval), diversityEval(_diversityEval), evalFitAndDivBeforeSelection(_evalFitAndDivBeforeSelection)
{}
/**
* Ctor taking a breed, a merge and a eoPopEval.
* @param _continuator the stopping criteria
* @param _popEval the evaluation functions for the whole population
* @param _breed the breeder
* @param _replace the replacement strategy
* @param _fitnessEval the fitness evaluation scheme
* @param _diversityEval the diversity evaluation scheme
* @param _evalFitAndDivBeforeSelection put this parameter to 'true' if you want to re-evalue the fitness and the diversity of the population before the selection process
*/
moeoEasyEA(eoContinue < MOEOT > & _continuator, eoPopEvalFunc < MOEOT > & _popEval, eoBreed < MOEOT > & _breed, moeoReplacement < MOEOT > & _replace,
moeoFitnessAssignment < MOEOT > & _fitnessEval, moeoDiversityAssignment < MOEOT > & _diversityEval, bool _evalFitAndDivBeforeSelection = false)
:
continuator(_continuator), eval (dummyEval), loopEval(dummyEval), popEval(_popEval), selectTransform(dummySelect, dummyTransform), breed(_breed), mergeReduce(dummyMerge, dummyReduce), replace(_replace),
fitnessEval(_fitnessEval), diversityEval(_diversityEval), evalFitAndDivBeforeSelection(_evalFitAndDivBeforeSelection)
{}
/**
* Ctor taking a breed, a merge and a reduce.
* @param _continuator the stopping criteria
* @param _eval the evaluation functions
* @param _breed the breeder
* @param _merge the merge scheme
* @param _reduce the reduce scheme
* @param _fitnessEval the fitness evaluation scheme
* @param _diversityEval the diversity evaluation scheme
* @param _evalFitAndDivBeforeSelection put this parameter to 'true' if you want to re-evalue the fitness and the diversity of the population before the selection process
*/
moeoEasyEA(eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoBreed < MOEOT > & _breed, eoMerge < MOEOT > & _merge, eoReduce< MOEOT > & _reduce,
moeoFitnessAssignment < MOEOT > & _fitnessEval, moeoDiversityAssignment < MOEOT > & _diversityEval, bool _evalFitAndDivBeforeSelection = false)
:
continuator(_continuator), eval(_eval), loopEval(_eval), popEval(loopEval), selectTransform(dummySelect, dummyTransform), breed(_breed), mergeReduce(_merge,_reduce), replace(mergeReduce),
fitnessEval(_fitnessEval), diversityEval(_diversityEval), evalFitAndDivBeforeSelection(_evalFitAndDivBeforeSelection)
{}
/**
* Ctor taking a select, a transform and a replacement.
* @param _continuator the stopping criteria
* @param _eval the evaluation functions
* @param _select the selection scheme
* @param _transform the tranformation scheme
* @param _replace the replacement strategy
* @param _fitnessEval the fitness evaluation scheme
* @param _diversityEval the diversity evaluation scheme
* @param _evalFitAndDivBeforeSelection put this parameter to 'true' if you want to re-evalue the fitness and the diversity of the population before the selection process
*/
moeoEasyEA(eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoSelect < MOEOT > & _select, eoTransform < MOEOT > & _transform, moeoReplacement < MOEOT > & _replace,
moeoFitnessAssignment < MOEOT > & _fitnessEval, moeoDiversityAssignment < MOEOT > & _diversityEval, bool _evalFitAndDivBeforeSelection = false)
:
continuator(_continuator), eval(_eval), loopEval(_eval), popEval(loopEval), selectTransform(_select, _transform), breed(selectTransform), mergeReduce(dummyMerge, dummyReduce), replace(_replace),
fitnessEval(_fitnessEval), diversityEval(_diversityEval), evalFitAndDivBeforeSelection(_evalFitAndDivBeforeSelection)
{}
/**
* Ctor taking a select, a transform, a merge and a reduce.
* @param _continuator the stopping criteria
* @param _eval the evaluation functions
* @param _select the selection scheme
* @param _transform the tranformation scheme
* @param _merge the merge scheme
* @param _reduce the reduce scheme
* @param _fitnessEval the fitness evaluation scheme
* @param _diversityEval the diversity evaluation scheme
* @param _evalFitAndDivBeforeSelection put this parameter to 'true' if you want to re-evalue the fitness and the diversity of the population before the selection process
*/
moeoEasyEA(eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoSelect < MOEOT > & _select, eoTransform < MOEOT > & _transform, eoMerge < MOEOT > & _merge, eoReduce< MOEOT > & _reduce,
moeoFitnessAssignment < MOEOT > & _fitnessEval, moeoDiversityAssignment < MOEOT > & _diversityEval, bool _evalFitAndDivBeforeSelection = false)
:
continuator(_continuator), eval(_eval), loopEval(_eval), popEval(loopEval), selectTransform(_select, _transform), breed(selectTransform), mergeReduce(_merge,_reduce), replace(mergeReduce),
fitnessEval(_fitnessEval), diversityEval(_diversityEval), evalFitAndDivBeforeSelection(_evalFitAndDivBeforeSelection)
{}
/**
* Applies a few generation of evolution to the population _pop.
* @param _pop the population
*/
virtual void operator()(eoPop < MOEOT > & _pop)
{
eoPop < MOEOT > offspring, empty_pop;
popEval(empty_pop, _pop); // A first eval of pop.
bool firstTime = true;
do
{
try
{
unsigned int pSize = _pop.size();
offspring.clear(); // new offspring
// fitness and diversity assignment (if you want to or if it is the first generation)
if (evalFitAndDivBeforeSelection || firstTime)
{
firstTime = false;
fitnessEval(_pop);
diversityEval(_pop);
}
breed(_pop, offspring);
popEval(_pop, offspring); // eval of parents + offspring if necessary
replace(_pop, offspring); // after replace, the new pop. is in _pop
if (pSize > _pop.size())
{
throw std::runtime_error("Population shrinking!");
}
else if (pSize < _pop.size())
{
throw std::runtime_error("Population growing!");
}
}
catch (std::exception& e)
{
std::string s = e.what();
s.append( " in moeoEasyEA");
throw std::runtime_error( s );
}
} while (continuator(_pop));
}
protected:
/** the stopping criteria */
eoContinue < MOEOT > & continuator;
/** the evaluation functions */
eoEvalFunc < MOEOT > & eval;
/** to evaluate the whole population */
eoPopLoopEval < MOEOT > loopEval;
/** to evaluate the whole population */
eoPopEvalFunc < MOEOT > & popEval;
/** breed: a select followed by a transform */
eoSelectTransform < MOEOT > selectTransform;
/** the breeder */
eoBreed < MOEOT > & breed;
/** replacement: a merge followed by a reduce */
eoMergeReduce < MOEOT > mergeReduce;
/** the replacment strategy */
moeoReplacement < MOEOT > & replace;
/** the fitness assignment strategy */
moeoFitnessAssignment < MOEOT > & fitnessEval;
/** the diversity assignment strategy */
moeoDiversityAssignment < MOEOT > & diversityEval;
/** if this parameter is set to 'true', the fitness and the diversity of the whole population will be re-evaluated before the selection process */
bool evalFitAndDivBeforeSelection;
/** a dummy eval */
class eoDummyEval : public eoEvalFunc < MOEOT >
{ public: /** the dummy functor */
void operator()(MOEOT &) {}} dummyEval;
/** a dummy select */
class eoDummySelect : public eoSelect < MOEOT >
{ public: /** the dummy functor */
void operator()(const eoPop < MOEOT > &, eoPop < MOEOT > &) {} } dummySelect;
/** a dummy transform */
class eoDummyTransform : public eoTransform < MOEOT >
{ public: /** the dummy functor */
void operator()(eoPop < MOEOT > &) {} } dummyTransform;
/** a dummy merge */
eoNoElitism < MOEOT > dummyMerge;
/** a dummy reduce */
eoTruncate < MOEOT > dummyReduce;
};
#endif /*MOEOEASYEA_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoHybridLS.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOHYBRIDLS_H_
#define MOEOHYBRIDLS_H_
#include <eoContinue.h>
#include <eoPop.h>
#include <eoSelect.h>
#include <utils/eoUpdater.h>
#include <algo/moeoLS.h>
#include <archive/moeoArchive.h>
/**
* This class allows to apply a multi-objective local search to a number of selected individuals contained in the archive
* at every generation until a stopping criteria is verified.
*/
template < class MOEOT >
class moeoHybridLS : public eoUpdater
{
public:
/**
* Ctor
* @param _term stopping criteria
* @param _select selector
* @param _mols a multi-objective local search
* @param _arch the archive
*/
moeoHybridLS (eoContinue < MOEOT > & _term, eoSelect < MOEOT > & _select, moeoLS < MOEOT, MOEOT > & _mols, moeoArchive < MOEOT > & _arch) :
term(_term), select(_select), mols(_mols), arch(_arch)
{}
/**
* Applies the multi-objective local search to selected individuals contained in the archive if the stopping criteria is not verified
*/
void operator () ()
{
if (! term (arch))
{
// selection of solutions
eoPop < MOEOT > selectedSolutions;
select(arch, selectedSolutions);
// apply the local search to every selected solution
for (unsigned int i=0; i<selectedSolutions.size(); i++)
{
mols(selectedSolutions[i], arch);
}
}
}
private:
/** stopping criteria */
eoContinue < MOEOT > & term;
/** selector */
eoSelect < MOEOT > & select;
/** multi-objective local search */
moeoLS < MOEOT, MOEOT > & mols;
/** archive */
moeoArchive < MOEOT > & arch;
};
#endif /*MOEOHYBRIDLS_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoIBEA.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOIBEA_H_
#define MOEOIBEA_H_
#include <eoBreed.h>
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoGenContinue.h>
#include <eoGeneralBreeder.h>
#include <eoGenOp.h>
#include <eoPopEvalFunc.h>
#include <eoSGAGenOp.h>
#include <algo/moeoEA.h>
#include <diversity/moeoDummyDiversityAssignment.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
#include <replacement/moeoEnvironmentalReplacement.h>
#include <selection/moeoDetTournamentSelect.h>
/**
* IBEA (Indicator-Based Evolutionary Algorithm) as described in:
* E. Zitzler, S. Künzli, "Indicator-Based Selection in Multiobjective Search", Proc. 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp. 832-842, Birmingham, UK (2004).
* This class builds the IBEA algorithm only by using the fine-grained components of the ParadisEO-MOEO framework.
*/
template < class MOEOT >
class moeoIBEA : public moeoEA < MOEOT >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Simple ctor with a eoGenOp.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _op variation operator
* @param _metric metric
* @param _kappa scaling factor kappa
*/
moeoIBEA (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoGenOp < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select(2),
fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, dummyDiversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Simple ctor with a eoTransform.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _op variation operator
* @param _metric metric
* @param _kappa scaling factor kappa
*/
moeoIBEA (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select(2),
fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, dummyDiversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Ctor with a crossover, a mutation and their corresponding rates.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _crossover crossover
* @param _pCross crossover probability
* @param _mutation mutation
* @param _pMut mutation probability
* @param _metric metric
* @param _kappa scaling factor kappa
*/
moeoIBEA (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoQuadOp < MOEOT > & _crossover, double _pCross, eoMonOp < MOEOT > & _mutation, double _pMut, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select (2),
fitnessAssignment(_metric, _kappa), replace (fitnessAssignment, dummyDiversityAssignment), defaultSGAGenOp(_crossover, _pCross, _mutation, _pMut),
genBreed (select, defaultSGAGenOp), breed (genBreed)
{}
/**
* Ctor with a continuator (instead of _maxGen) and a eoGenOp.
* @param _continuator stopping criteria
* @param _eval evaluation function
* @param _op variation operator
* @param _metric metric
* @param _kappa scaling factor kappa
*/
moeoIBEA (eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoGenOp < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
continuator(_continuator), popEval(_eval), select(2),
fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, dummyDiversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Ctor with a continuator (instead of _maxGen) and a eoTransform.
* @param _continuator stopping criteria
* @param _eval evaluation function
* @param _op variation operator
* @param _metric metric
* @param _kappa scaling factor kappa
*/
moeoIBEA (eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
continuator(_continuator), popEval(_eval), select(2),
fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, dummyDiversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Apply a few generation of evolution to the population _pop until the stopping criteria is verified.
* @param _pop the population
*/
virtual void operator () (eoPop < MOEOT > &_pop)
{
eoPop < MOEOT > offspring, empty_pop;
popEval (empty_pop, _pop); // a first eval of _pop
// evaluate fitness and diversity
fitnessAssignment(_pop);
dummyDiversityAssignment(_pop);
do
{
// generate offspring, worths are recalculated if necessary
breed (_pop, offspring);
// eval of offspring
popEval (_pop, offspring);
// after replace, the new pop is in _pop. Worths are recalculated if necessary
replace (_pop, offspring);
} while (continuator (_pop));
}
protected:
/** a continuator based on the number of generations (used as default) */
eoGenContinue < MOEOT > defaultGenContinuator;
/** stopping criteria */
eoContinue < MOEOT > & continuator;
/** evaluation function used to evaluate the whole population */
eoPopLoopEval < MOEOT > popEval;
/** binary tournament selection */
moeoDetTournamentSelect < MOEOT > select;
/** fitness assignment used in IBEA */
moeoIndicatorBasedFitnessAssignment < MOEOT > fitnessAssignment;
/** dummy diversity assignment */
moeoDummyDiversityAssignment < MOEOT > dummyDiversityAssignment;
/** elitist replacement */
moeoEnvironmentalReplacement < MOEOT > replace;
/** an object for genetic operators (used as default) */
eoSGAGenOp < MOEOT > defaultSGAGenOp;
/** general breeder */
eoGeneralBreeder < MOEOT > genBreed;
/** breeder */
eoBreed < MOEOT > & breed;
};
#endif /*MOEOIBEA_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoIBMOLS.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOIBMOLS_H_
#define MOEOIBMOLS_H_
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoPop.h>
#include <moMove.h>
#include <moMoveInit.h>
#include <moNextMove.h>
#include <algo/moeoLS.h>
#include <archive/moeoArchive.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <move/moeoMoveIncrEval.h>
/**
* Indicator-Based Multi-Objective Local Search (IBMOLS) as described in
* Basseur M., Burke K. : "Indicator-Based Multi-Objective Local Search" (2007).
*/
template < class MOEOT, class Move >
class moeoIBMOLS : public moeoLS < MOEOT, eoPop < MOEOT > & >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor.
* @param _moveInit the move initializer
* @param _nextMove the neighborhood explorer
* @param _eval the full evaluation
* @param _moveIncrEval the incremental evaluation
* @param _fitnessAssignment the fitness assignment strategy
* @param _continuator the stopping criteria
*/
moeoIBMOLS(
moMoveInit < Move > & _moveInit,
moNextMove < Move > & _nextMove,
eoEvalFunc < MOEOT > & _eval,
moeoMoveIncrEval < Move > & _moveIncrEval,
moeoIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
eoContinue < MOEOT > & _continuator
) :
moveInit(_moveInit),
nextMove(_nextMove),
eval(_eval),
moveIncrEval(_moveIncrEval),
fitnessAssignment (_fitnessAssignment),
continuator (_continuator)
{}
/**
* Apply the local search until a local archive does not change or
* another stopping criteria is met and update the archive _arch with new non-dominated solutions.
* @param _pop the initial population
* @param _arch the (updated) archive
*/
void operator() (eoPop < MOEOT > & _pop, moeoArchive < MOEOT > & _arch)
{
// evaluation of the objective values
/*
for (unsigned int i=0; i<_pop.size(); i++)
{
eval(_pop[i]);
}
*/
// fitness assignment for the whole population
fitnessAssignment(_pop);
// creation of a local archive
moeoArchive < MOEOT > archive;
// creation of another local archive (for the stopping criteria)
moeoArchive < MOEOT > previousArchive;
// update the archive with the initial population
archive.update(_pop);
do
{
previousArchive.update(archive);
oneStep(_pop);
archive.update(_pop);
} while ( (! archive.equals(previousArchive)) && (continuator(_arch)) );
_arch.update(archive);
}
private:
/** the move initializer */
moMoveInit < Move > & moveInit;
/** the neighborhood explorer */
moNextMove < Move > & nextMove;
/** the full evaluation */
eoEvalFunc < MOEOT > & eval;
/** the incremental evaluation */
moeoMoveIncrEval < Move > & moveIncrEval;
/** the fitness assignment strategy */
moeoIndicatorBasedFitnessAssignment < MOEOT > & fitnessAssignment;
/** the stopping criteria */
eoContinue < MOEOT > & continuator;
/**
* Apply one step of the local search to the population _pop
* @param _pop the population
*/
void oneStep (eoPop < MOEOT > & _pop)
{
////////////////////////////////////////////
int ext_0_idx, ext_1_idx;
ObjectiveVector ext_0_objVec, ext_1_objVec;
///////////////////////////////////////////
// the move
Move move;
// the objective vector and the fitness of the current solution
ObjectiveVector x_objVec;
double x_fitness;
// the index, the objective vector and the fitness of the worst solution in the population (-1 implies that the worst is the newly created one)
int worst_idx;
ObjectiveVector worst_objVec;
double worst_fitness;
// the index current of the current solution to be explored
unsigned int i=0;
// initilization of the move for the first individual
moveInit(move, _pop[i]);
while (i<_pop.size() && continuator(_pop))
{
// x = one neigbour of pop[i]
// evaluate x in the objective space
x_objVec = moveIncrEval(move, _pop[i]);
// update every fitness values to take x into account and compute the fitness of x
x_fitness = fitnessAssignment.updateByAdding(_pop, x_objVec);
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
// qui sont les extremes ? (=> min only !!!)
ext_0_idx = -1;
ext_0_objVec = x_objVec;
ext_1_idx = -1;
ext_1_objVec = x_objVec;
for (unsigned int k=0; k<_pop.size(); k++)
{
// ext_0
if (_pop[k].objectiveVector()[0] < ext_0_objVec[0])
{
ext_0_idx = k;
ext_0_objVec = _pop[k].objectiveVector();
}
else if ( (_pop[k].objectiveVector()[0] == ext_0_objVec[0]) && (_pop[k].objectiveVector()[1] < ext_0_objVec[1]) )
{
ext_0_idx = k;
ext_0_objVec = _pop[k].objectiveVector();
}
// ext_1
else if (_pop[k].objectiveVector()[1] < ext_1_objVec[1])
{
ext_1_idx = k;
ext_1_objVec = _pop[k].objectiveVector();
}
else if ( (_pop[k].objectiveVector()[1] == ext_1_objVec[1]) && (_pop[k].objectiveVector()[0] < ext_1_objVec[0]) )
{
ext_1_idx = k;
ext_1_objVec = _pop[k].objectiveVector();
}
}
// worst init
if (ext_0_idx == -1)
{
unsigned int ind = 0;
while (ind == ext_1_idx)
{
ind++;
}
worst_idx = ind;
worst_objVec = _pop[ind].objectiveVector();
worst_fitness = _pop[ind].fitness();
}
else if (ext_1_idx == -1)
{
unsigned int ind = 0;
while (ind == ext_0_idx)
{
ind++;
}
worst_idx = ind;
worst_objVec = _pop[ind].objectiveVector();
worst_fitness = _pop[ind].fitness();
}
else
{
worst_idx = -1;
worst_objVec = x_objVec;
worst_fitness = x_fitness;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////////////////////
// who is the worst ?
for (unsigned int j=0; j<_pop.size(); j++)
{
if ( (j!=ext_0_idx) && (j!=ext_1_idx) )
{
if (_pop[j].fitness() < worst_fitness)
{
worst_idx = j;
worst_objVec = _pop[j].objectiveVector();
worst_fitness = _pop[j].fitness();
}
}
}
// if the worst solution is the new one
if (worst_idx == -1)
{
// if all its neighbours have been explored,
// let's explore the neighborhoud of the next individual
if (! nextMove(move, _pop[i]))
{
i++;
if (i<_pop.size())
{
// initilization of the move for the next individual
moveInit(move, _pop[i]);
}
}
}
// if the worst solution is located before _pop[i]
else if (worst_idx <= i)
{
// the new solution takes place insteed of _pop[worst_idx]
_pop[worst_idx] = _pop[i];
move(_pop[worst_idx]);
_pop[worst_idx].objectiveVector(x_objVec);
_pop[worst_idx].fitness(x_fitness);
// let's explore the neighborhoud of the next individual
i++;
if (i<_pop.size())
{
// initilization of the move for the next individual
moveInit(move, _pop[i]);
}
}
// if the worst solution is located after _pop[i]
else if (worst_idx > i)
{
// the new solution takes place insteed of _pop[i+1] and _pop[worst_idx] is deleted
_pop[worst_idx] = _pop[i+1];
_pop[i+1] = _pop[i];
move(_pop[i+1]);
_pop[i+1].objectiveVector(x_objVec);
_pop[i+1].fitness(x_fitness);
// let's explore the neighborhoud of the individual _pop[i+2]
i += 2;
if (i<_pop.size())
{
// initilization of the move for the next individual
moveInit(move, _pop[i]);
}
}
// update fitness values
fitnessAssignment.updateByDeleting(_pop, worst_objVec);
}
}
};
#endif /*MOEOIBMOLS_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoIteratedIBMOLS.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOITERATEDIBMOLS_H_
#define MOEOITERATEDIBMOLS_H_
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoOp.h>
#include <eoPop.h>
#include <utils/rnd_generators.h>
#include <moMove.h>
#include <moMoveInit.h>
#include <moNextMove.h>
#include <algo/moeoIBMOLS.h>
#include <algo/moeoLS.h>
#include <archive/moeoArchive.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <move/moeoMoveIncrEval.h>
//#include <rsCrossQuad.h>
/**
* Iterated version of IBMOLS as described in
* Basseur M., Burke K. : "Indicator-Based Multi-Objective Local Search" (2007).
*/
template < class MOEOT, class Move >
class moeoIteratedIBMOLS : public moeoLS < MOEOT, eoPop < MOEOT > & >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor.
* @param _moveInit the move initializer
* @param _nextMove the neighborhood explorer
* @param _eval the full evaluation
* @param _moveIncrEval the incremental evaluation
* @param _fitnessAssignment the fitness assignment strategy
* @param _continuator the stopping criteria
* @param _monOp the monary operator
* @param _randomMonOp the random monary operator (or random initializer)
* @param _nNoiseIterations the number of iterations to apply the random noise
*/
moeoIteratedIBMOLS(
moMoveInit < Move > & _moveInit,
moNextMove < Move > & _nextMove,
eoEvalFunc < MOEOT > & _eval,
moeoMoveIncrEval < Move > & _moveIncrEval,
moeoIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
eoContinue < MOEOT > & _continuator,
eoMonOp < MOEOT > & _monOp,
eoMonOp < MOEOT > & _randomMonOp,
unsigned int _nNoiseIterations=1
) :
ibmols(_moveInit, _nextMove, _eval, _moveIncrEval, _fitnessAssignment, _continuator),
eval(_eval),
continuator(_continuator),
monOp(_monOp),
randomMonOp(_randomMonOp),
nNoiseIterations(_nNoiseIterations)
{}
/**
* Apply the local search iteratively until the stopping criteria is met.
* @param _pop the initial population
* @param _arch the (updated) archive
*/
void operator() (eoPop < MOEOT > & _pop, moeoArchive < MOEOT > & _arch)
{
_arch.update(_pop);
ibmols(_pop, _arch);
while (continuator(_arch))
{
// generate new solutions from the archive
generateNewSolutions(_pop, _arch);
// apply the local search (the global archive is updated in the sub-function)
ibmols(_pop, _arch);
}
}
private:
/** the local search to iterate */
moeoIBMOLS < MOEOT, Move > ibmols;
/** the full evaluation */
eoEvalFunc < MOEOT > & eval;
/** the stopping criteria */
eoContinue < MOEOT > & continuator;
/** the monary operator */
eoMonOp < MOEOT > & monOp;
/** the random monary operator (or random initializer) */
eoMonOp < MOEOT > & randomMonOp;
/** the number of iterations to apply the random noise */
unsigned int nNoiseIterations;
/**
* Creates new population randomly initialized and/or initialized from the archive _arch.
* @param _pop the output population
* @param _arch the archive
*/
void generateNewSolutions(eoPop < MOEOT > & _pop, const moeoArchive < MOEOT > & _arch)
{
// shuffle vector for the random selection of individuals
vector<unsigned int> shuffle;
shuffle.resize(std::max(_pop.size(), _arch.size()));
// init shuffle
for (unsigned int i=0; i<shuffle.size(); i++)
{
shuffle[i] = i;
}
// randomize shuffle
UF_random_generator <unsigned int int> gen;
std::random_shuffle(shuffle.begin(), shuffle.end(), gen);
// start the creation of new solutions
for (unsigned int i=0; i<_pop.size(); i++)
{
if (shuffle[i] < _arch.size())
// the given archive contains the individual i
{
// add it to the resulting pop
_pop[i] = _arch[shuffle[i]];
// then, apply the operator nIterationsNoise times
for (unsigned int j=0; j<nNoiseIterations; j++)
{
monOp(_pop[i]);
}
}
else
// a randomly generated solution needs to be added
{
// random initialization
randomMonOp(_pop[i]);
}
// evaluation of the new individual
_pop[i].invalidate();
eval(_pop[i]);
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////////
// A DEVELOPPER RAPIDEMENT POUR TESTER AVEC CROSSOVER //
/*
void generateNewSolutions2(eoPop < MOEOT > & _pop, const moeoArchive < MOEOT > & _arch)
{
// here, we must have a QuadOp !
//eoQuadOp < MOEOT > quadOp;
rsCrossQuad quadOp;
// shuffle vector for the random selection of individuals
vector<unsigned int> shuffle;
shuffle.resize(_arch.size());
// init shuffle
for (unsigned int i=0; i<shuffle.size(); i++)
{
shuffle[i] = i;
}
// randomize shuffle
UF_random_generator <unsigned int int> gen;
std::random_shuffle(shuffle.begin(), shuffle.end(), gen);
// start the creation of new solutions
unsigned int i=0;
while ((i<_pop.size()-1) && (i<_arch.size()-1))
{
_pop[i] = _arch[shuffle[i]];
_pop[i+1] = _arch[shuffle[i+1]];
// then, apply the operator nIterationsNoise times
for (unsigned int j=0; j<nNoiseIterations; j++)
{
quadOp(_pop[i], _pop[i+1]);
}
eval(_pop[i]);
eval(_pop[i+1]);
i=i+2;
}
// do we have to add some random solutions ?
while (i<_pop.size())
{
randomMonOp(_pop[i]);
eval(_pop[i]);
i++;
}
}
*/
///////////////////////////////////////////////////////////////////////////////////////////////////////
};
#endif /*MOEOITERATEDIBMOLS_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoLS.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOLS_H_
#define MOEOLS_H_
#include <eoFunctor.h>
#include <algo/moeoAlgo.h>
#include <archive/moeoArchive.h>
/**
* Abstract class for local searches applied to multi-objective optimization.
* Starting from a Type (i.e.: an individual, a pop, an archive...), it produces a set of new non-dominated solutions.
*/
template < class MOEOT, class Type >
class moeoLS: public moeoAlgo, public eoBF < Type, moeoArchive < MOEOT > &, void > {};
#endif /*MOEOLS_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoNSGA.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEONSGA_H_
#define MOEONSGA_H_
#include <eoBreed.h>
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoGenContinue.h>
#include <eoGeneralBreeder.h>
#include <eoGenOp.h>
#include <eoPopEvalFunc.h>
#include <eoSGAGenOp.h>
#include <algo/moeoEA.h>
#include <diversity/moeoFrontByFrontSharingDiversityAssignment.h>
#include <fitness/moeoFastNonDominatedSortingFitnessAssignment.h>
#include <replacement/moeoElitistReplacement.h>
#include <selection/moeoDetTournamentSelect.h>
/**
* NSGA (Non-dominated Sorting Genetic Algorithm) as described in:
* N. Srinivas, K. Deb, "Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms".
* Evolutionary Computation, Vol. 2(3), No 2, pp. 221-248 (1994).
* This class builds the NSGA algorithm only by using the fine-grained components of the ParadisEO-MOEO framework.
*/
template < class MOEOT >
class moeoNSGA: public moeoEA < MOEOT >
{
public:
/**
* Simple ctor with a eoGenOp.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _op variation operator
* @param _nicheSize niche size
*/
moeoNSGA (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoGenOp < MOEOT > & _op, double _nicheSize = 0.5) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select(2),
diversityAssignment(_nicheSize), replace(fitnessAssignment, diversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Simple ctor with a eoTransform.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _op variation operator
* @param _nicheSize niche size
*/
moeoNSGA (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _op, double _nicheSize = 0.5) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select(2),
diversityAssignment(_nicheSize), replace(fitnessAssignment, diversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Ctor with a crossover, a mutation and their corresponding rates.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _crossover crossover
* @param _pCross crossover probability
* @param _mutation mutation
* @param _pMut mutation probability
* @param _nicheSize niche size
*/
moeoNSGA (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoQuadOp < MOEOT > & _crossover, double _pCross, eoMonOp < MOEOT > & _mutation, double _pMut, double _nicheSize = 0.5) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select (2),
diversityAssignment(_nicheSize), replace (fitnessAssignment, diversityAssignment),
defaultSGAGenOp(_crossover, _pCross, _mutation, _pMut), genBreed (select, defaultSGAGenOp), breed (genBreed)
{}
/**
* Ctor with a continuator (instead of _maxGen) and a eoGenOp.
* @param _continuator stopping criteria
* @param _eval evaluation function
* @param _op variation operator
* @param _nicheSize niche size
*/
moeoNSGA (eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoGenOp < MOEOT > & _op, double _nicheSize = 0.5) :
continuator(_continuator), popEval(_eval), select(2),
diversityAssignment(_nicheSize), replace(fitnessAssignment, diversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Ctor with a continuator (instead of _maxGen) and a eoTransform.
* @param _continuator stopping criteria
* @param _eval evaluation function
* @param _op variation operator
* @param _nicheSize niche size
*/
moeoNSGA (eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _op, double _nicheSize = 0.5) :
continuator(_continuator), popEval(_eval), select(2),
diversityAssignment(_nicheSize), replace(fitnessAssignment, diversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Apply a few generation of evolution to the population _pop until the stopping criteria is verified.
* @param _pop the population
*/
virtual void operator () (eoPop < MOEOT > &_pop)
{
eoPop < MOEOT > offspring, empty_pop;
popEval (empty_pop, _pop); // a first eval of _pop
// evaluate fitness and diversity
fitnessAssignment(_pop);
diversityAssignment(_pop);
do
{
// generate offspring, worths are recalculated if necessary
breed (_pop, offspring);
// eval of offspring
popEval (_pop, offspring);
// after replace, the new pop is in _pop. Worths are recalculated if necessary
replace (_pop, offspring);
} while (continuator (_pop));
}
protected:
/** a continuator based on the number of generations (used as default) */
eoGenContinue < MOEOT > defaultGenContinuator;
/** stopping criteria */
eoContinue < MOEOT > & continuator;
/** evaluation function used to evaluate the whole population */
eoPopLoopEval < MOEOT > popEval;
/** binary tournament selection */
moeoDetTournamentSelect < MOEOT > select;
/** fitness assignment used in NSGA-II */
moeoFastNonDominatedSortingFitnessAssignment < MOEOT > fitnessAssignment;
/** diversity assignment used in NSGA-II */
moeoFrontByFrontSharingDiversityAssignment < MOEOT > diversityAssignment;
/** elitist replacement */
moeoElitistReplacement < MOEOT > replace;
/** an object for genetic operators (used as default) */
eoSGAGenOp < MOEOT > defaultSGAGenOp;
/** general breeder */
eoGeneralBreeder < MOEOT > genBreed;
/** breeder */
eoBreed < MOEOT > & breed;
};
#endif /*MOEONSGAII_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoNSGAII.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEONSGAII_H_
#define MOEONSGAII_H_
#include <eoBreed.h>
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoGenContinue.h>
#include <eoGeneralBreeder.h>
#include <eoGenOp.h>
#include <eoPopEvalFunc.h>
#include <eoSGAGenOp.h>
#include <algo/moeoEA.h>
#include <diversity/moeoFrontByFrontCrowdingDistanceDiversityAssignment.h>
#include <fitness/moeoFastNonDominatedSortingFitnessAssignment.h>
#include <replacement/moeoElitistReplacement.h>
#include <selection/moeoDetTournamentSelect.h>
/**
* NSGA-II (Non-dominated Sorting Genetic Algorithm II) as described in:
* Deb, K., S. Agrawal, A. Pratap, and T. Meyarivan : "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II".
* In IEEE Transactions on Evolutionary Computation, Vol. 6, No 2, pp 182-197 (April 2002).
* This class builds the NSGA-II algorithm only by using the fine-grained components of the ParadisEO-MOEO framework.
*/
template < class MOEOT >
class moeoNSGAII: public moeoEA < MOEOT >
{
public:
/**
* Simple ctor with a eoGenOp.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _op variation operator
*/
moeoNSGAII (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoGenOp < MOEOT > & _op) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select(2),
replace(fitnessAssignment, diversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Simple ctor with a eoTransform.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _op variation operator
*/
moeoNSGAII (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _op) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select(2),
replace(fitnessAssignment, diversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Ctor with a crossover, a mutation and their corresponding rates.
* @param _maxGen number of generations before stopping
* @param _eval evaluation function
* @param _crossover crossover
* @param _pCross crossover probability
* @param _mutation mutation
* @param _pMut mutation probability
*/
moeoNSGAII (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoQuadOp < MOEOT > & _crossover, double _pCross, eoMonOp < MOEOT > & _mutation, double _pMut) :
defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select (2),
replace (fitnessAssignment, diversityAssignment), defaultSGAGenOp(_crossover, _pCross, _mutation, _pMut),
genBreed (select, defaultSGAGenOp), breed (genBreed)
{}
/**
* Ctor with a continuator (instead of _maxGen) and a eoGenOp.
* @param _continuator stopping criteria
* @param _eval evaluation function
* @param _op variation operator
*/
moeoNSGAII (eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoGenOp < MOEOT > & _op) :
continuator(_continuator), popEval(_eval), select(2),
replace(fitnessAssignment, diversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Ctor with a continuator (instead of _maxGen) and a eoTransform.
* @param _continuator stopping criteria
* @param _eval evaluation function
* @param _op variation operator
*/
moeoNSGAII (eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _op) :
continuator(_continuator), popEval(_eval), select(2),
replace(fitnessAssignment, diversityAssignment), genBreed(select, _op), breed(genBreed)
{}
/**
* Apply a few generation of evolution to the population _pop until the stopping criteria is verified.
* @param _pop the population
*/
virtual void operator () (eoPop < MOEOT > &_pop)
{
eoPop < MOEOT > offspring, empty_pop;
popEval (empty_pop, _pop); // a first eval of _pop
// evaluate fitness and diversity
fitnessAssignment(_pop);
diversityAssignment(_pop);
do
{
// generate offspring, worths are recalculated if necessary
breed (_pop, offspring);
// eval of offspring
popEval (_pop, offspring);
// after replace, the new pop is in _pop. Worths are recalculated if necessary
replace (_pop, offspring);
} while (continuator (_pop));
}
protected:
/** a continuator based on the number of generations (used as default) */
eoGenContinue < MOEOT > defaultGenContinuator;
/** stopping criteria */
eoContinue < MOEOT > & continuator;
/** evaluation function used to evaluate the whole population */
eoPopLoopEval < MOEOT > popEval;
/** binary tournament selection */
moeoDetTournamentSelect < MOEOT > select;
/** fitness assignment used in NSGA-II */
moeoFastNonDominatedSortingFitnessAssignment < MOEOT > fitnessAssignment;
/** diversity assignment used in NSGA-II */
moeoFrontByFrontCrowdingDistanceDiversityAssignment < MOEOT > diversityAssignment;
/** elitist replacement */
moeoElitistReplacement < MOEOT > replace;
/** an object for genetic operators (used as default) */
eoSGAGenOp < MOEOT > defaultSGAGenOp;
/** general breeder */
eoGeneralBreeder < MOEOT > genBreed;
/** breeder */
eoBreed < MOEOT > & breed;
};
#endif /*MOEONSGAII_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoArchive.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOARCHIVE_H_
#define MOEOARCHIVE_H_
#include <eoPop.h>
#include <comparator/moeoObjectiveVectorComparator.h>
#include <comparator/moeoParetoObjectiveVectorComparator.h>
/**
* An archive is a secondary population that stores non-dominated solutions.
*/
template < class MOEOT >
class moeoArchive : public eoPop < MOEOT >
{
public:
using eoPop < MOEOT > :: size;
using eoPop < MOEOT > :: operator[];
using eoPop < MOEOT > :: back;
using eoPop < MOEOT > :: pop_back;
/**
* The type of an objective vector for a solution
*/
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Default ctor.
* The moeoObjectiveVectorComparator used to compare solutions is based on Pareto dominance
*/
moeoArchive() : eoPop < MOEOT >(), comparator(paretoComparator)
{}
/**
* Ctor
* @param _comparator the moeoObjectiveVectorComparator used to compare solutions
*/
moeoArchive(moeoObjectiveVectorComparator < ObjectiveVector > & _comparator) : eoPop < MOEOT >(), comparator(_comparator)
{}
/**
* Returns true if the current archive dominates _objectiveVector according to the moeoObjectiveVectorComparator given in the constructor
* @param _objectiveVector the objective vector to compare with the current archive
*/
bool dominates (const ObjectiveVector & _objectiveVector) const
{
for (unsigned int i = 0; i<size(); i++)
{
// if _objectiveVector is dominated by the ith individual of the archive...
if ( comparator(_objectiveVector, operator[](i).objectiveVector()) )
{
return true;
}
}
return false;
}
/**
* Returns true if the current archive already contains a solution with the same objective values than _objectiveVector
* @param _objectiveVector the objective vector to compare with the current archive
*/
bool contains (const ObjectiveVector & _objectiveVector) const
{
for (unsigned int i = 0; i<size(); i++)
{
if (operator[](i).objectiveVector() == _objectiveVector)
{
return true;
}
}
return false;
}
/**
* Updates the archive with a given individual _moeo
* @param _moeo the given individual
*/
void update (const MOEOT & _moeo)
{
// first step: removing the dominated solutions from the archive
for (unsigned int j=0; j<size();)
{
// if the jth solution contained in the archive is dominated by _moeo
if ( comparator(operator[](j).objectiveVector(), _moeo.objectiveVector()) )
{
operator[](j) = back();
pop_back();
}
else if (_moeo.objectiveVector() == operator[](j).objectiveVector())
{
operator[](j) = back();
pop_back();
}
else
{
j++;
}
}
// second step: is _moeo dominated?
bool dom = false;
for (unsigned int j=0; j<size(); j++)
{
// if _moeo is dominated by the jth solution contained in the archive
if ( comparator(_moeo.objectiveVector(), operator[](j).objectiveVector()) )
{
dom = true;
break;
}
}
if (!dom)
{
push_back(_moeo);
}
}
/**
* Updates the archive with a given population _pop
* @param _pop the given population
*/
void update (const eoPop < MOEOT > & _pop)
{
for (unsigned int i=0; i<_pop.size(); i++)
{
update(_pop[i]);
}
}
/**
* Returns true if the current archive contains the same objective vectors than the given archive _arch
* @param _arch the given archive
*/
bool equals (const moeoArchive < MOEOT > & _arch)
{
for (unsigned int i=0; i<size(); i++)
{
if (! _arch.contains(operator[](i).objectiveVector()))
{
return false;
}
}
for (unsigned int i=0; i<_arch.size() ; i++)
{
if (! contains(_arch[i].objectiveVector()))
{
return false;
}
}
return true;
}
private:
/** The moeoObjectiveVectorComparator used to compare solutions */
moeoObjectiveVectorComparator < ObjectiveVector > & comparator;
/** A moeoObjectiveVectorComparator based on Pareto dominance (used as default) */
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
};
#endif /*MOEOARCHIVE_H_ */

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoAggregativeComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOAGGREGATIVECOMPARATOR_H_
#define MOEOAGGREGATIVECOMPARATOR_H_
#include <comparator/moeoComparator.h>
/**
* Functor allowing to compare two solutions according to their fitness and diversity values, each according to its aggregative value.
*/
template < class MOEOT >
class moeoAggregativeComparator : public moeoComparator < MOEOT >
{
public:
/**
* Ctor.
* @param _weightFitness the weight for fitness
* @param _weightDiversity the weight for diversity
*/
moeoAggregativeComparator(double _weightFitness = 1.0, double _weightDiversity = 1.0) : weightFitness(_weightFitness), weightDiversity(_weightDiversity)
{}
/**
* Returns true if _moeo1 < _moeo2 according to the aggregation of their fitness and diversity values
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
return ( weightFitness * _moeo1.fitness() + weightDiversity * _moeo1.diversity() ) < ( weightFitness * _moeo2.fitness() + weightDiversity * _moeo2.diversity() );
}
private:
/** the weight for fitness */
double weightFitness;
/** the weight for diversity */
double weightDiversity;
};
#endif /*MOEOAGGREGATIVECOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOCOMPARATOR_H_
#define MOEOCOMPARATOR_H_
#include <eoFunctor.h>
/**
* Functor allowing to compare two solutions.
*/
template < class MOEOT >
class moeoComparator : public eoBF < const MOEOT &, const MOEOT &, const bool > {};
#endif /*MOEOCOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoDiversityThenFitnessComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEODIVERSITYTHENFITNESSCOMPARATOR_H_
#define MOEODIVERSITYTHENFITNESSCOMPARATOR_H_
#include <comparator/moeoComparator.h>
/**
* Functor allowing to compare two solutions according to their diversity values, then according to their fitness values.
*/
template < class MOEOT >
class moeoDiversityThenFitnessComparator : public moeoComparator < MOEOT >
{
public:
/**
* Returns true if _moeo1 < _moeo2 according to their diversity values, then according to their fitness values
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
if (_moeo1.diversity() == _moeo2.diversity())
{
return _moeo1.fitness() < _moeo2.fitness();
}
else
{
return _moeo1.diversity() < _moeo2.diversity();
}
}
};
#endif /*MOEODIVERSITYTHENFITNESSCOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoFitnessThenDiversityComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOFITNESSTHENDIVERSITYCOMPARATOR_H_
#define MOEOFITNESSTHENDIVERSITYCOMPARATOR_H_
#include <comparator/moeoComparator.h>
/**
* Functor allowing to compare two solutions according to their fitness values, then according to their diversity values.
*/
template < class MOEOT >
class moeoFitnessThenDiversityComparator : public moeoComparator < MOEOT >
{
public:
/**
* Returns true if _moeo1 < _moeo2 according to their fitness values, then according to their diversity values
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
if (_moeo1.fitness() == _moeo2.fitness())
{
return _moeo1.diversity() < _moeo2.diversity();
}
else
{
return _moeo1.fitness() < _moeo2.fitness();
}
}
};
#endif /*MOEOFITNESSTHENDIVERSITYCOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoGDominanceObjectiveVectorComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOGDOMINANCEOBJECTIVEVECTORCOMPARATOR_H_
#define MOEOGDOMINANCEOBJECTIVEVECTORCOMPARATOR_H_
#include <comparator/moeoObjectiveVectorComparator.h>
/**
* This functor class allows to compare 2 objective vectors according to g-dominance.
* The concept of g-dominance as been introduced in:
* J. Molina, L. V. Santana, A. G. Hernandez-Diaz, C. A. Coello Coello, R. Caballero,
* "g-dominance: Reference point based dominance" (2007)
*/
template < class ObjectiveVector >
class moeoGDominanceObjectiveVectorComparator : public moeoObjectiveVectorComparator < ObjectiveVector >
{
public:
/**
* Ctor.
* @param _ref the reference point
*/
moeoGDominanceObjectiveVectorComparator(ObjectiveVector & _ref) : ref(_ref)
{}
/**
* Returns true if _objectiveVector1 is g-dominated by _objectiveVector2.
* @param _objectiveVector1 the first objective vector
* @param _objectiveVector2 the second objective vector
*/
const bool operator()(const ObjectiveVector & _objectiveVector1, const ObjectiveVector & _objectiveVector2)
{
unsigned int flag1 = flag(_objectiveVector1);
unsigned int flag2 = flag(_objectiveVector2);
if (flag2==0)
{
// cannot dominate
return false;
}
else if ( (flag2==1) && (flag1==0) )
{
// is dominated
return true;
}
else // (flag1==1) && (flag2==1)
{
// both are on the good region, so let's use the classical Pareto dominance
return paretoComparator(_objectiveVector1, _objectiveVector2);
}
}
private:
/** the reference point */
ObjectiveVector & ref;
/** Pareto comparator */
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
/**
* Returns the flag of _objectiveVector according to the reference point
* @param _objectiveVector the first objective vector
*/
unsigned int flag(const ObjectiveVector & _objectiveVector)
{
unsigned int result=1;
for (unsigned int i=0; i<ref.nObjectives(); i++)
{
if (_objectiveVector[i] > ref[i])
{
result=0;
}
}
if (result==0)
{
result=1;
for (unsigned int i=0; i<ref.nObjectives(); i++)
{
if (_objectiveVector[i] < ref[i])
{
result=0;
}
}
}
return result;
}
};
#endif /*MOEOGDOMINANCEOBJECTIVEVECTORCOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoObjectiveObjectiveVectorComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOOBJECTIVEOBJECTIVEVECTORCOMPARATOR_H_
#define MOEOOBJECTIVEOBJECTIVEVECTORCOMPARATOR_H_
#include <comparator/moeoObjectiveVectorComparator.h>
/**
* Functor allowing to compare two objective vectors according to their first objective value, then their second, and so on.
*/
template < class ObjectiveVector >
class moeoObjectiveObjectiveVectorComparator : public moeoObjectiveVectorComparator < ObjectiveVector >
{
public:
/**
* Returns true if _objectiveVector1 < _objectiveVector2 on the first objective, then on the second, and so on
* @param _objectiveVector1 the first objective vector
* @param _objectiveVector2 the second objective vector
*/
const bool operator()(const ObjectiveVector & _objectiveVector1, const ObjectiveVector & _objectiveVector2)
{
for (unsigned int i=0; i<ObjectiveVector::nObjectives(); i++)
{
if ( fabs(_objectiveVector1[i] - _objectiveVector2[i]) > ObjectiveVector::Traits::tolerance() )
{
if (_objectiveVector1[i] < _objectiveVector2[i])
{
return true;
}
else
{
return false;
}
}
}
return false;
}
};
#endif /*MOEOOBJECTIVEOBJECTIVEVECTORCOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoObjectiveVectorComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOOBJECTIVEVECTORCOMPARATOR_H_
#define MOEOOBJECTIVEVECTORCOMPARATOR_H_
#include <math.h>
#include <eoFunctor.h>
/**
* Abstract class allowing to compare 2 objective vectors.
* The template argument ObjectiveVector have to be a moeoObjectiveVector.
*/
template < class ObjectiveVector >
class moeoObjectiveVectorComparator : public eoBF < const ObjectiveVector &, const ObjectiveVector &, const bool > {};
#endif /*MOEOOBJECTIVEVECTORCOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoOneObjectiveComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOONEOBJECTIVECOMPARATOR_H_
#define MOEOONEOBJECTIVECOMPARATOR_H_
#include <comparator/moeoComparator.h>
/**
* Functor allowing to compare two solutions according to one objective.
*/
template < class MOEOT >
class moeoOneObjectiveComparator : public moeoComparator < MOEOT >
{
public:
/**
* Ctor.
* @param _obj the index of objective
*/
moeoOneObjectiveComparator(unsigned int _obj) : obj(_obj)
{
if (obj > MOEOT::ObjectiveVector::nObjectives())
{
throw std::runtime_error("Problem with the index of objective in moeoOneObjectiveComparator");
}
}
/**
* Returns true if _moeo1 < _moeo2 on the obj objective
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
return _moeo1.objectiveVector()[obj] < _moeo2.objectiveVector()[obj];
}
private:
/** the index of objective */
unsigned int obj;
};
#endif /*MOEOONEOBJECTIVECOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoParetoObjectiveVectorComparator.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOPARETOOBJECTIVEVECTORCOMPARATOR_H_
#define MOEOPARETOOBJECTIVEVECTORCOMPARATOR_H_
#include <comparator/moeoObjectiveVectorComparator.h>
/**
* This functor class allows to compare 2 objective vectors according to Pareto dominance.
*/
template < class ObjectiveVector >
class moeoParetoObjectiveVectorComparator : public moeoObjectiveVectorComparator < ObjectiveVector >
{
public:
/**
* Returns true if _objectiveVector1 is dominated by _objectiveVector2
* @param _objectiveVector1 the first objective vector
* @param _objectiveVector2 the second objective vector
*/
const bool operator()(const ObjectiveVector & _objectiveVector1, const ObjectiveVector & _objectiveVector2)
{
bool dom = false;
for (unsigned int i=0; i<ObjectiveVector::nObjectives(); i++)
{
// first, we have to check if the 2 objective values are not equal for the ith objective
if ( fabs(_objectiveVector1[i] - _objectiveVector2[i]) > ObjectiveVector::Traits::tolerance() )
{
// if the ith objective have to be minimized...
if (ObjectiveVector::minimizing(i))
{
if (_objectiveVector1[i] > _objectiveVector2[i])
{
dom = true; //_objectiveVector1[i] is not better than _objectiveVector2[i]
}
else
{
return false; //_objectiveVector2 cannot dominate _objectiveVector1
}
}
// if the ith objective have to be maximized...
else if (ObjectiveVector::maximizing(i))
{
if (_objectiveVector1[i] > _objectiveVector2[i])
{
dom = true; //_objectiveVector1[i] is not better than _objectiveVector2[i]
}
else
{
return false; //_objectiveVector2 cannot dominate _objectiveVector1
}
}
}
}
return dom;
}
};
#endif /*MOEOPARETOOBJECTIVEVECTORCOMPARATOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// MOEO.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEO_H_
#define MOEO_H_
#include <iostream>
#include <stdexcept>
#include <string>
#include <EO.h>
/**
* Base class allowing to represent a solution (an individual) for multi-objective optimization.
* The template argument MOEOObjectiveVector allows to represent the solution in the objective space (it can be a moeoObjectiveVector object).
* The template argument MOEOFitness is an object reflecting the quality of the solution in term of convergence (the fitness of a solution is always to be maximized).
* The template argument MOEODiversity is an object reflecting the quality of the solution in term of diversity (the diversity of a solution is always to be maximized).
* All template arguments must have a void and a copy constructor.
* Using some specific representations, you will have to define a copy constructor if the default one is not what you want.
* In the same cases, you will also have to define the affectation operator (operator=).
* Then, you will explicitly have to call the parent copy constructor and the parent affectation operator at the beginning of the corresponding implementation.
* Besides, note that, contrary to the mono-objective case (and to EO) where the fitness value of a solution is confused with its objective value,
* the fitness value differs of the objectives values in the multi-objective case.
*/
template < class MOEOObjectiveVector, class MOEOFitness, class MOEODiversity >
class MOEO : public EO < MOEOObjectiveVector >
{
public:
/** the objective vector type of a solution */
typedef MOEOObjectiveVector ObjectiveVector;
/** the fitness type of a solution */
typedef MOEOFitness Fitness;
/** the diversity type of a solution */
typedef MOEODiversity Diversity;
/**
* Ctor
*/
MOEO()
{
// default values for every parameters
objectiveVectorValue = ObjectiveVector();
fitnessValue = Fitness();
diversityValue = Diversity();
// invalidate all
invalidate();
}
/**
* Virtual dtor
*/
virtual ~MOEO() {};
/**
* Returns the objective vector of the current solution
*/
ObjectiveVector objectiveVector() const
{
if ( invalidObjectiveVector() )
{
throw std::runtime_error("invalid objective vector in MOEO");
}
return objectiveVectorValue;
}
/**
* Sets the objective vector of the current solution
* @param _objectiveVectorValue the new objective vector
*/
void objectiveVector(const ObjectiveVector & _objectiveVectorValue)
{
objectiveVectorValue = _objectiveVectorValue;
invalidObjectiveVectorValue = false;
}
/**
* Sets the objective vector as invalid
*/
void invalidateObjectiveVector()
{
invalidObjectiveVectorValue = true;
}
/**
* Returns true if the objective vector is invalid, false otherwise
*/
bool invalidObjectiveVector() const
{
return invalidObjectiveVectorValue;
}
/**
* Returns the fitness value of the current solution
*/
Fitness fitness() const
{
if ( invalidFitness() )
{
throw std::runtime_error("invalid fitness in MOEO");
}
return fitnessValue;
}
/**
* Sets the fitness value of the current solution
* @param _fitnessValue the new fitness value
*/
void fitness(const Fitness & _fitnessValue)
{
fitnessValue = _fitnessValue;
invalidFitnessValue = false;
}
/**
* Sets the fitness value as invalid
*/
void invalidateFitness()
{
invalidFitnessValue = true;
}
/**
* Returns true if the fitness value is invalid, false otherwise
*/
bool invalidFitness() const
{
return invalidFitnessValue;
}
/**
* Returns the diversity value of the current solution
*/
Diversity diversity() const
{
if ( invalidDiversity() )
{
throw std::runtime_error("invalid diversity in MOEO");
}
return diversityValue;
}
/**
* Sets the diversity value of the current solution
* @param _diversityValue the new diversity value
*/
void diversity(const Diversity & _diversityValue)
{
diversityValue = _diversityValue;
invalidDiversityValue = false;
}
/**
* Sets the diversity value as invalid
*/
void invalidateDiversity()
{
invalidDiversityValue = true;
}
/**
* Returns true if the diversity value is invalid, false otherwise
*/
bool invalidDiversity() const
{
return invalidDiversityValue;
}
/**
* Sets the objective vector, the fitness value and the diversity value as invalid
*/
void invalidate()
{
invalidateObjectiveVector();
invalidateFitness();
invalidateDiversity();
}
/**
* Returns true if the fitness value is invalid, false otherwise
*/
bool invalid() const
{
return invalidObjectiveVector();
}
/**
* Returns true if the objective vector of the current solution is smaller than the objective vector of _other on the first objective,
* then on the second, and so on (can be usefull for sorting/printing).
* You should implement another function in the sub-class of MOEO to have another sorting mecanism.
* @param _other the other MOEO object to compare with
*/
bool operator<(const MOEO & _other) const
{
return objectiveVector() < _other.objectiveVector();
}
/**
* Return the class id (the class name as a std::string)
*/
virtual std::string className() const
{
return "MOEO";
}
/**
* Writing object
* @param _os output stream
*/
virtual void printOn(std::ostream & _os) const
{
if ( invalidObjectiveVector() )
{
_os << "INVALID\t";
}
else
{
_os << objectiveVectorValue << '\t';
}
}
/**
* Reading object
* @param _is input stream
*/
virtual void readFrom(std::istream & _is)
{
std::string objectiveVector_str;
int pos = _is.tellg();
_is >> objectiveVector_str;
if (objectiveVector_str == "INVALID")
{
invalidateObjectiveVector();
}
else
{
invalidObjectiveVectorValue = false;
_is.seekg(pos); // rewind
_is >> objectiveVectorValue;
}
}
private:
/** the objective vector of this solution */
ObjectiveVector objectiveVectorValue;
/** true if the objective vector is invalid */
bool invalidObjectiveVectorValue;
/** the fitness value of this solution */
Fitness fitnessValue;
/** true if the fitness value is invalid */
bool invalidFitnessValue;
/** the diversity value of this solution */
Diversity diversityValue;
/** true if the diversity value is invalid */
bool invalidDiversityValue;
};
#endif /*MOEO_H_*/

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lib_LIBRARIES = libmoeo.a
libmoeo_a_SOURCES = moeoObjectiveVectorTraits.cpp
pkginclude_HEADERS = moeoObjectiveVectorTraits.h
INCLUDES = -I$(EO_DIR)/src/ -I$(top_srcdir)/src/
AM_CXXFLAGS = -Wall -ansi -pedantic

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoBitVector.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOBITVECTOR_H_
#define MOEOBITVECTOR_H_
#include <core/moeoVector.h>
/**
* This class is an implementationeo of a simple bit-valued moeoVector.
*/
template < class MOEOObjectiveVector, class MOEOFitness, class MOEODiversity >
class moeoBitVector : public moeoVector < MOEOObjectiveVector, MOEOFitness, MOEODiversity, bool >
{
public:
using moeoVector < MOEOObjectiveVector, MOEOFitness, MOEODiversity, bool > :: begin;
using moeoVector < MOEOObjectiveVector, MOEOFitness, MOEODiversity, bool > :: end;
using moeoVector < MOEOObjectiveVector, MOEOFitness, MOEODiversity, bool > :: resize;
using moeoVector < MOEOObjectiveVector, MOEOFitness, MOEODiversity, bool > :: size;
/**
* Ctor
* @param _size Length of vector (default is 0)
* @param _value Initial value of all elements (default is default value of type GeneType)
*/
moeoBitVector(unsigned int _size = 0, bool _value = false) : moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, bool >(_size, _value)
{}
/**
* Writing object
* @param _os output stream
*/
virtual void printOn(std::ostream & _os) const
{
MOEO < MOEOObjectiveVector, MOEOFitness, MOEODiversity >::printOn(_os);
_os << ' ';
_os << size() << ' ';
std::copy(begin(), end(), std::ostream_iterator<bool>(_os));
}
/**
* Reading object
* @param _is input stream
*/
virtual void readFrom(std::istream & _is)
{
MOEO < MOEOObjectiveVector, MOEOFitness, MOEODiversity >::readFrom(_is);
unsigned int s;
_is >> s;
std::string bits;
_is >> bits;
if (_is)
{
resize(bits.size());
std::transform(bits.begin(), bits.end(), begin(), std::bind2nd(std::equal_to<char>(), '1'));
}
}
};
#endif /*MOEOBITVECTOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoEvalFunc.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOEVALFUNC_H_
#define MOEOEVALFUNC_H_
#include <eoEvalFunc.h>
/*
* Functor that evaluates one MOEO by setting all its objective values.
*/
template < class MOEOT >
class moeoEvalFunc : public eoEvalFunc< MOEOT > {};
#endif /*MOEOEVALFUNC_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoObjectiveVector.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOOBJECTIVEVECTOR_H_
#define MOEOOBJECTIVEVECTOR_H_
#include <vector>
/**
* Abstract class allowing to represent a solution in the objective space (phenotypic representation).
* The template argument ObjectiveVectorTraits defaults to moeoObjectiveVectorTraits,
* but it can be replaced at will by any other class that implements the static functions defined therein.
* Some static funtions to access to the traits characteristics are re-defined in order not to write a lot of typedef's.
*/
template < class ObjectiveVectorTraits, class ObjectiveVectorType >
class moeoObjectiveVector : public std::vector < ObjectiveVectorType >
{
public:
/** The traits of objective vectors */
typedef ObjectiveVectorTraits Traits;
/** The type of an objective value */
typedef ObjectiveVectorType Type;
/**
* Ctor
*/
moeoObjectiveVector(Type _value = Type()) : std::vector < Type > (ObjectiveVectorTraits::nObjectives(), _value)
{}
/**
* Ctor from a vector of Type
* @param _v the std::vector < Type >
*/
moeoObjectiveVector(std::vector < Type > & _v) : std::vector < Type > (_v)
{}
/**
* Parameters setting (for the objective vector of any solution)
* @param _nObjectives the number of objectives
* @param _bObjectives the min/max vector (true = min / false = max)
*/
static void setup(unsigned int _nObjectives, std::vector < bool > & _bObjectives)
{
ObjectiveVectorTraits::setup(_nObjectives, _bObjectives);
}
/**
* Returns the number of objectives
*/
static unsigned int nObjectives()
{
return ObjectiveVectorTraits::nObjectives();
}
/**
* Returns true if the _ith objective have to be minimized
* @param _i the index
*/
static bool minimizing(unsigned int _i)
{
return ObjectiveVectorTraits::minimizing(_i);
}
/**
* Returns true if the _ith objective have to be maximized
* @param _i the index
*/
static bool maximizing(unsigned int _i)
{
return ObjectiveVectorTraits::maximizing(_i);
}
};
#endif /*MOEOOBJECTIVEVECTOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoObjectiveVectorDouble.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOOBJECTIVEVECTORDOUBLE_H_
#define MOEOOBJECTIVEVECTORDOUBLE_H_
#include <iostream>
#include <math.h>
#include <comparator/moeoObjectiveObjectiveVectorComparator.h>
#include <comparator/moeoParetoObjectiveVectorComparator.h>
#include <core/moeoObjectiveVector.h>
/**
* This class allows to represent a solution in the objective space (phenotypic representation) by a std::vector of doubles,
* i.e. that an objective value is represented using a double, and this for any objective.
*/
template < class ObjectiveVectorTraits >
class moeoObjectiveVectorDouble : public moeoObjectiveVector < ObjectiveVectorTraits, double >
{
public:
using moeoObjectiveVector < ObjectiveVectorTraits, double >::size;
using moeoObjectiveVector < ObjectiveVectorTraits, double >::operator[];
/**
* Ctor
*/
moeoObjectiveVectorDouble(double _value = 0.0) : moeoObjectiveVector < ObjectiveVectorTraits, double > (_value)
{}
/**
* Ctor from a vector of doubles
* @param _v the std::vector < double >
*/
moeoObjectiveVectorDouble(std::vector < double > & _v) : moeoObjectiveVector < ObjectiveVectorTraits, double > (_v)
{}
/**
* Returns true if the current objective vector dominates _other according to the Pareto dominance relation
* (but it's better to use a moeoObjectiveVectorComparator object to compare solutions)
* @param _other the other moeoObjectiveVectorDouble object to compare with
*/
bool dominates(const moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _other) const
{
moeoParetoObjectiveVectorComparator < moeoObjectiveVectorDouble<ObjectiveVectorTraits> > comparator;
return comparator(_other, *this);
}
/**
* Returns true if the current objective vector is equal to _other (according to a tolerance value)
* @param _other the other moeoObjectiveVectorDouble object to compare with
*/
bool operator==(const moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _other) const
{
for (unsigned int i=0; i < size(); i++)
{
if ( fabs(operator[](i) - _other[i]) > ObjectiveVectorTraits::tolerance() )
{
return false;
}
}
return true;
}
/**
* Returns true if the current objective vector is different than _other (according to a tolerance value)
* @param _other the other moeoObjectiveVectorDouble object to compare with
*/
bool operator!=(const moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _other) const
{
return ! operator==(_other);
}
/**
* Returns true if the current objective vector is smaller than _other on the first objective, then on the second, and so on
* (can be usefull for sorting/printing)
* @param _other the other moeoObjectiveVectorDouble object to compare with
*/
bool operator<(const moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _other) const
{
moeoObjectiveObjectiveVectorComparator < moeoObjectiveVectorDouble < ObjectiveVectorTraits > > cmp;
return cmp(*this, _other);
}
/**
* Returns true if the current objective vector is greater than _other on the first objective, then on the second, and so on
* (can be usefull for sorting/printing)
* @param _other the other moeoObjectiveVectorDouble object to compare with
*/
bool operator>(const moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _other) const
{
return _other < *this;
}
/**
* Returns true if the current objective vector is smaller than or equal to _other on the first objective, then on the second, and so on
* (can be usefull for sorting/printing)
* @param _other the other moeoObjectiveVectorDouble object to compare with
*/
bool operator<=(const moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _other) const
{
return operator==(_other) || operator<(_other);
}
/**
* Returns true if the current objective vector is greater than or equal to _other on the first objective, then on the second, and so on
* (can be usefull for sorting/printing)
* @param _other the other moeoObjectiveVectorDouble object to compare with
*/
bool operator>=(const moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _other) const
{
return operator==(_other) || operator>(_other);
}
};
/**
* Output for a moeoObjectiveVectorDouble object
* @param _os output stream
* @param _objectiveVector the objective vector to write
*/
template < class ObjectiveVectorTraits >
std::ostream & operator<<(std::ostream & _os, const moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _objectiveVector)
{
for (unsigned int i=0; i<_objectiveVector.size(); i++)
{
_os << _objectiveVector[i] << '\t';
}
return _os;
}
/**
* Input for a moeoObjectiveVectorDouble object
* @param _is input stream
* @param _objectiveVector the objective vector to read
*/
template < class ObjectiveVectorTraits >
std::istream & operator>>(std::istream & _is, moeoObjectiveVectorDouble < ObjectiveVectorTraits > & _objectiveVector)
{
_objectiveVector = moeoObjectiveVectorDouble < ObjectiveVectorTraits > ();
for (unsigned int i=0; i<_objectiveVector.size(); i++)
{
_is >> _objectiveVector[i];
}
return _is;
}
#endif /*MOEOOBJECTIVEVECTORDOUBLE_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoObjectiveVectorTraits.cpp
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#include <core/moeoObjectiveVectorTraits.h>
// The static variables of the moeoObjectiveVectorTraits class need to be allocated
unsigned int moeoObjectiveVectorTraits::nObj;
std::vector < bool > moeoObjectiveVectorTraits::bObj;

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoObjectiveVectorTraits.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOOBJECTIVEVECTORTRAITS_H_
#define MOEOOBJECTIVEVECTORTRAITS_H_
#include <iostream>
#include <stdexcept>
#include <vector>
/**
* A traits class for moeoObjectiveVector to specify the number of objectives and which ones have to be minimized or maximized.
*/
class moeoObjectiveVectorTraits
{
public:
/**
* Parameters setting
* @param _nObjectives the number of objectives
* @param _bObjectives the min/max vector (true = min / false = max)
*/
static void setup(unsigned int _nObjectives, std::vector < bool > & _bObjectives)
{
// in case the number of objectives was already set to a different value
if ( nObj && (nObj != _nObjectives) ) {
std::cout << "WARNING\n";
std::cout << "WARNING : the number of objectives are changing\n";
std::cout << "WARNING : Make sure all existing objects are destroyed\n";
std::cout << "WARNING\n";
}
// number of objectives
nObj = _nObjectives;
// min/max vector
bObj = _bObjectives;
// in case the number of objectives and the min/max vector size don't match
if (nObj != bObj.size())
throw std::runtime_error("Number of objectives and min/max size don't match in moeoObjectiveVectorTraits::setup");
}
/**
* Returns the number of objectives
*/
static unsigned int nObjectives()
{
// in case the number of objectives would not be assigned yet
if (! nObj)
throw std::runtime_error("Number of objectives not assigned in moeoObjectiveVectorTraits");
return nObj;
}
/**
* Returns true if the _ith objective have to be minimized
* @param _i the index
*/
static bool minimizing(unsigned int _i)
{
// in case there would be a wrong index
if (_i >= bObj.size())
throw std::runtime_error("Wrong index in moeoObjectiveVectorTraits");
return bObj[_i];
}
/**
* Returns true if the _ith objective have to be maximized
* @param _i the index
*/
static bool maximizing(unsigned int _i) {
return (! minimizing(_i));
}
/**
* Returns the tolerance value (to compare solutions)
*/
static double tolerance()
{
return 1e-6;
}
private:
/** The number of objectives */
static unsigned int nObj;
/** The min/max vector */
static std::vector < bool > bObj;
};
#endif /*MOEOOBJECTIVEVECTORTRAITS_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoRealVector.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOREALVECTOR_H_
#define MOEOREALVECTOR_H_
#include <core/moeoVector.h>
/**
* This class is an implementation of a simple double-valued moeoVector.
*/
template < class MOEOObjectiveVector, class MOEOFitness, class MOEODiversity >
class moeoRealVector : public moeoVector < MOEOObjectiveVector, MOEOFitness, MOEODiversity, double >
{
public:
/**
* Ctor
* @param _size Length of vector (default is 0)
* @param _value Initial value of all elements (default is default value of type GeneType)
*/
moeoRealVector(unsigned int _size = 0, double _value = 0.0) : moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, double >(_size, _value)
{}
};
#endif /*MOEOREALVECTOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoVector.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOVECTOR_H_
#define MOEOVECTOR_H_
#include <iterator>
#include <vector>
#include <core/MOEO.h>
/**
* Base class for fixed length chromosomes, just derives from MOEO and std::vector and redirects the smaller than operator to MOEO (objective vector based comparison).
* GeneType must have the following methods: void ctor (needed for the std::vector<>), copy ctor.
*/
template < class MOEOObjectiveVector, class MOEOFitness, class MOEODiversity, class GeneType >
class moeoVector : public MOEO < MOEOObjectiveVector, MOEOFitness, MOEODiversity >, public std::vector < GeneType >
{
public:
using MOEO < MOEOObjectiveVector, MOEOFitness, MOEODiversity > :: invalidate;
using std::vector < GeneType > :: operator[];
using std::vector < GeneType > :: begin;
using std::vector < GeneType > :: end;
using std::vector < GeneType > :: resize;
using std::vector < GeneType > :: size;
/** the atomic type */
typedef GeneType AtomType;
/** the container type */
typedef std::vector < GeneType > ContainerType;
/**
* Default ctor.
* @param _size Length of vector (default is 0)
* @param _value Initial value of all elements (default is default value of type GeneType)
*/
moeoVector(unsigned int _size = 0, GeneType _value = GeneType()) :
MOEO < MOEOObjectiveVector, MOEOFitness, MOEODiversity >(), std::vector<GeneType>(_size, _value)
{}
/**
* We can't have a Ctor from a std::vector as it would create ambiguity with the copy Ctor.
* @param _v a vector of GeneType
*/
void value(const std::vector < GeneType > & _v)
{
if (_v.size() != size()) // safety check
{
if (size()) // NOT an initial empty std::vector
{
std::cout << "Warning: Changing size in moeoVector assignation"<<std::endl;
resize(_v.size());
}
else
{
throw std::runtime_error("Size not initialized in moeoVector");
}
}
std::copy(_v.begin(), _v.end(), begin());
invalidate();
}
/**
* To avoid conflicts between MOEO::operator< and std::vector<GeneType>::operator<
* @param _moeo the object to compare with
*/
bool operator<(const moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, GeneType> & _moeo) const
{
return MOEO < MOEOObjectiveVector, MOEOFitness, MOEODiversity >::operator<(_moeo);
}
/**
* Writing object
* @param _os output stream
*/
virtual void printOn(std::ostream & _os) const
{
MOEO < MOEOObjectiveVector, MOEOFitness, MOEODiversity >::printOn(_os);
_os << ' ';
_os << size() << ' ';
std::copy(begin(), end(), std::ostream_iterator<AtomType>(_os, " "));
}
/**
* Reading object
* @param _is input stream
*/
virtual void readFrom(std::istream & _is)
{
MOEO < MOEOObjectiveVector, MOEOFitness, MOEODiversity >::readFrom(_is);
unsigned int sz;
_is >> sz;
resize(sz);
unsigned int i;
for (i = 0; i < sz; ++i)
{
AtomType atom;
_is >> atom;
operator[](i) = atom;
}
}
};
/**
* To avoid conflicts between MOEO::operator< and std::vector<double>::operator<
* @param _moeo1 the first object to compare
* @param _moeo2 the second object to compare
*/
template < class MOEOObjectiveVector, class MOEOFitness, class MOEODiversity, class GeneType >
bool operator<(const moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, GeneType> & _moeo1, const moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, GeneType> & _moeo2)
{
return _moeo1.operator<(_moeo2);
}
/**
* To avoid conflicts between MOEO::operator> and std::vector<double>::operator>
* @param _moeo1 the first object to compare
* @param _moeo2 the second object to compare
*/
template < class MOEOObjectiveVector, class MOEOFitness, class MOEODiversity, class GeneType >
bool operator>(const moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, GeneType> & _moeo1, const moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, GeneType> & _moeo2)
{
return _moeo1.operator>(_moeo2);
}
#endif /*MOEOVECTOR_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoDistance.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEODISTANCE_H_
#define MOEODISTANCE_H_
#include <eoFunctor.h>
/**
* The base class for distance computation.
*/
template < class MOEOT , class Type >
class moeoDistance : public eoBF < const MOEOT &, const MOEOT &, const Type >
{
public:
/**
* Nothing to do
* @param _pop the population
*/
virtual void setup(const eoPop < MOEOT > & _pop)
{}
/**
* Nothing to do
* @param _min lower bound
* @param _max upper bound
* @param _obj the objective index
*/
virtual void setup(double _min, double _max, unsigned int _obj)
{}
/**
* Nothing to do
* @param _realInterval the eoRealInterval object
* @param _obj the objective index
*/
virtual void setup(eoRealInterval _realInterval, unsigned int _obj)
{}
};
#endif /*MOEODISTANCE_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoDistanceMatrix.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEODISTANCEMATRIX_H_
#define MOEODISTANCEMATRIX_H_
#include <vector>
#include <eoFunctor.h>
#include <distance/moeoDistance.h>
/**
* A matrix to compute distances between every pair of individuals contained in a population.
*/
template < class MOEOT , class Type >
class moeoDistanceMatrix : public eoUF < const eoPop < MOEOT > &, void > , public std::vector< std::vector < Type > >
{
public:
using std::vector< std::vector < Type > > :: size;
using std::vector< std::vector < Type > > :: operator[];
/**
* Ctor
* @param _size size for every dimension of the matrix
* @param _distance the distance to use
*/
moeoDistanceMatrix (unsigned int _size, moeoDistance < MOEOT , Type > & _distance) : distance(_distance)
{
this->resize(_size);
for (unsigned int i=0; i<_size; i++)
{
this->operator[](i).resize(_size);
}
}
/**
* Sets the distance between every pair of individuals contained in the population _pop
* @param _pop the population
*/
void operator()(const eoPop < MOEOT > & _pop)
{
// 1 - setup the bounds (if necessary)
distance.setup(_pop);
// 2 - compute distances
this->operator[](0).operator[](0) = Type();
for (unsigned int i=0; i<size(); i++)
{
this->operator[](i).operator[](i) = Type();
for (unsigned int j=0; j<i; j++)
{
this->operator[](i).operator[](j) = distance(_pop[i], _pop[j]);
this->operator[](j).operator[](i) = this->operator[](i).operator[](j);
}
}
}
private:
/** the distance to use */
moeoDistance < MOEOT , Type > & distance;
};
#endif /*MOEODISTANCEMATRIX_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoEuclideanDistance.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOEUCLIDEANDISTANCE_H_
#define MOEOEUCLIDEANDISTANCE_H_
#include <math.h>
#include <distance/moeoNormalizedDistance.h>
/**
* A class allowing to compute an euclidian distance between two solutions in the objective space with normalized objective values (i.e. between 0 and 1).
* A distance value then lies between 0 and sqrt(nObjectives).
*/
template < class MOEOT >
class moeoEuclideanDistance : public moeoNormalizedDistance < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Returns the euclidian distance between _moeo1 and _moeo2 in the objective space
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const double operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
double result = 0.0;
double tmp1, tmp2;
for (unsigned int i=0; i<ObjectiveVector::nObjectives(); i++)
{
tmp1 = (_moeo1.objectiveVector()[i] - bounds[i].minimum()) / bounds[i].range();
tmp2 = (_moeo2.objectiveVector()[i] - bounds[i].minimum()) / bounds[i].range();
result += (tmp1-tmp2) * (tmp1-tmp2);
}
return sqrt(result);
}
private:
/** the bounds for every objective */
using moeoNormalizedDistance < MOEOT > :: bounds;
};
#endif /*MOEOEUCLIDEANDISTANCE_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoManhattanDistance.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOMANHATTANDISTANCE_H_
#define MOEOMANHATTANDISTANCE_H_
#include <math.h>
#include <distance/moeoNormalizedDistance.h>
/**
* A class allowing to compute the Manhattan distance between two solutions in the objective space normalized objective values (i.e. between 0 and 1).
* A distance value then lies between 0 and nObjectives.
*/
template < class MOEOT >
class moeoManhattanDistance : public moeoNormalizedDistance < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Returns the Manhattan distance between _moeo1 and _moeo2 in the objective space
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const double operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
double result = 0.0;
double tmp1, tmp2;
for (unsigned int i=0; i<ObjectiveVector::nObjectives(); i++)
{
tmp1 = (_moeo1.objectiveVector()[i] - bounds[i].minimum()) / bounds[i].range();
tmp2 = (_moeo2.objectiveVector()[i] - bounds[i].minimum()) / bounds[i].range();
result += fabs(tmp1-tmp2);
}
return result;
}
private:
/** the bounds for every objective */
using moeoNormalizedDistance < MOEOT > :: bounds;
};
#endif /*MOEOMANHATTANDISTANCE_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoNormalizedDistance.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEONORMALIZEDDISTANCE_H_
#define MOEONORMALIZEDDISTANCE_H_
#include <vector>
#include <utils/eoRealBounds.h>
#include <distance/moeoDistance.h>
/**
* The base class for double distance computation with normalized objective values (i.e. between 0 and 1).
*/
template < class MOEOT , class Type = double >
class moeoNormalizedDistance : public moeoDistance < MOEOT , Type >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Default ctr
*/
moeoNormalizedDistance()
{
bounds.resize(ObjectiveVector::Traits::nObjectives());
// initialize bounds in case someone does not want to use them
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
bounds[i] = eoRealInterval(0,1);
}
}
/**
* Returns a very small value that can be used to avoid extreme cases (where the min bound == the max bound)
*/
static double tiny()
{
return 1e-6;
}
/**
* Sets the lower and the upper bounds for every objective using extremes values for solutions contained in the population _pop
* @param _pop the population
*/
virtual void setup(const eoPop < MOEOT > & _pop)
{
double min, max;
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
min = _pop[0].objectiveVector()[i];
max = _pop[0].objectiveVector()[i];
for (unsigned int j=1; j<_pop.size(); j++)
{
min = std::min(min, _pop[j].objectiveVector()[i]);
max = std::max(max, _pop[j].objectiveVector()[i]);
}
// setting of the bounds for the objective i
setup(min, max, i);
}
}
/**
* Sets the lower bound (_min) and the upper bound (_max) for the objective _obj
* @param _min lower bound
* @param _max upper bound
* @param _obj the objective index
*/
virtual void setup(double _min, double _max, unsigned int _obj)
{
if (_min == _max)
{
_min -= tiny();
_max += tiny();
}
bounds[_obj] = eoRealInterval(_min, _max);
}
/**
* Sets the lower bound and the upper bound for the objective _obj using a eoRealInterval object
* @param _realInterval the eoRealInterval object
* @param _obj the objective index
*/
virtual void setup(eoRealInterval _realInterval, unsigned int _obj)
{
bounds[_obj] = _realInterval;
}
protected:
/** the bounds for every objective (bounds[i] = bounds for the objective i) */
std::vector < eoRealInterval > bounds;
};
#endif /*MOEONORMALIZEDDISTANCE_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoCrowdingDistanceDiversityAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_
#define MOEOCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_
#include <eoPop.h>
#include <comparator/moeoOneObjectiveComparator.h>
#include <diversity/moeoDiversityAssignment.h>
/**
* Diversity assignment sheme based on crowding distance proposed in:
* K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, no. 2 (2002).
*/
template < class MOEOT >
class moeoCrowdingDistanceDiversityAssignment : public moeoDiversityAssignment < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Returns a big value (regarded as infinite)
*/
double inf() const
{
return std::numeric_limits<double>::max();
}
/**
* Returns a very small value that can be used to avoid extreme cases (where the min bound == the max bound)
*/
double tiny() const
{
return 1e-6;
}
/**
* Computes diversity values for every solution contained in the population _pop
* @param _pop the population
*/
void operator()(eoPop < MOEOT > & _pop)
{
if (_pop.size() <= 2)
{
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].diversity(inf());
}
}
else
{
setDistances(_pop);
}
}
/**
* @warning NOT IMPLEMENTED, DO NOTHING !
* Updates the diversity values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
* @warning NOT IMPLEMENTED, DO NOTHING !
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::cout << "WARNING : updateByDeleting not implemented in moeoCrowdingDiversityAssignment" << std::endl;
}
protected:
/**
* Sets the distance values
* @param _pop the population
*/
virtual void setDistances (eoPop < MOEOT > & _pop)
{
double min, max, distance;
unsigned int nObjectives = MOEOT::ObjectiveVector::nObjectives();
// set diversity to 0
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].diversity(0);
}
// for each objective
for (unsigned int obj=0; obj<nObjectives; obj++)
{
// comparator
moeoOneObjectiveComparator < MOEOT > objComp(obj);
// sort
std::sort(_pop.begin(), _pop.end(), objComp);
// min & max
min = _pop[0].objectiveVector()[obj];
max = _pop[_pop.size()-1].objectiveVector()[obj];
// set the diversity value to infiny for min and max
_pop[0].diversity(inf());
_pop[_pop.size()-1].diversity(inf());
for (unsigned int i=1; i<_pop.size()-1; i++)
{
distance = (_pop[i+1].objectiveVector()[obj] - _pop[i-1].objectiveVector()[obj]) / (max-min);
_pop[i].diversity(_pop[i].diversity() + distance);
}
}
}
};
#endif /*MOEOCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoDiversityAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEODIVERSITYASSIGNMENT_H_
#define MOEODIVERSITYASSIGNMENT_H_
#include <eoFunctor.h>
#include <eoPop.h>
/**
* Functor that sets the diversity values of a whole population.
*/
template < class MOEOT >
class moeoDiversityAssignment : public eoUF < eoPop < MOEOT > &, void >
{
public:
/** The type for objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Updates the diversity values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
*/
virtual void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec) = 0;
/**
* Updates the diversity values of the whole population _pop by taking the deletion of the individual _moeo into account.
* @param _pop the population
* @param _moeo the individual
*/
void updateByDeleting(eoPop < MOEOT > & _pop, MOEOT & _moeo)
{
updateByDeleting(_pop, _moeo.objectiveVector());
}
};
#endif /*MOEODIVERSITYASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoDummyDiversityAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEODUMMYDIVERSITYASSIGNMENT_H_
#define MOEODUMMYDIVERSITYASSIGNMENT_H_
#include<diversity/moeoDiversityAssignment.h>
/**
* moeoDummyDiversityAssignment is a moeoDiversityAssignment that gives the value '0' as the individual's diversity for a whole population if it is invalid.
*/
template < class MOEOT >
class moeoDummyDiversityAssignment : public moeoDiversityAssignment < MOEOT >
{
public:
/** The type for objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Sets the diversity to '0' for every individuals of the population _pop if it is invalid
* @param _pop the population
*/
void operator () (eoPop < MOEOT > & _pop)
{
for (unsigned int idx = 0; idx<_pop.size (); idx++)
{
if (_pop[idx].invalidDiversity())
{
// set the diversity to 0
_pop[idx].diversity(0.0);
}
}
}
/**
* Updates the diversity values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
// nothing to do... ;-)
}
};
#endif /*MOEODUMMYDIVERSITYASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoFrontByFrontCrowdingDistanceDiversityAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOFRONTBYFRONTCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_
#define MOEOFRONTBYFRONTCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_
#include <diversity/moeoCrowdingDistanceDiversityAssignment.h>
#include <comparator/moeoFitnessThenDiversityComparator.h>
/**
* Diversity assignment sheme based on crowding distance proposed in:
* K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, no. 2 (2002).
* Tis strategy assigns diversity values FRONT BY FRONT. It is, for instance, used in NSGA-II.
*/
template < class MOEOT >
class moeoFrontByFrontCrowdingDistanceDiversityAssignment : public moeoCrowdingDistanceDiversityAssignment < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* @warning NOT IMPLEMENTED, DO NOTHING !
* Updates the diversity values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
* @warning NOT IMPLEMENTED, DO NOTHING !
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::cout << "WARNING : updateByDeleting not implemented in moeoFrontByFrontCrowdingDistanceDiversityAssignment" << std::endl;
}
private:
using moeoCrowdingDistanceDiversityAssignment < MOEOT >::inf;
using moeoCrowdingDistanceDiversityAssignment < MOEOT >::tiny;
/**
* Sets the distance values
* @param _pop the population
*/
void setDistances (eoPop < MOEOT > & _pop)
{
unsigned int a,b;
double min, max, distance;
unsigned int nObjectives = MOEOT::ObjectiveVector::nObjectives();
// set diversity to 0 for every individual
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].diversity(0.0);
}
// sort the whole pop according to fitness values
moeoFitnessThenDiversityComparator < MOEOT > fitnessComparator;
std::sort(_pop.begin(), _pop.end(), fitnessComparator);
// compute the crowding distance values for every individual "front" by "front" (front : from a to b)
a = 0; // the front starts at a
while (a < _pop.size())
{
b = lastIndex(_pop,a); // the front ends at b
// if there is less than 2 individuals in the front...
if ((b-a) < 2)
{
for (unsigned int i=a; i<=b; i++)
{
_pop[i].diversity(inf());
}
}
// else...
else
{
// for each objective
for (unsigned int obj=0; obj<nObjectives; obj++)
{
// sort in the descending order using the values of the objective 'obj'
moeoOneObjectiveComparator < MOEOT > objComp(obj);
std::sort(_pop.begin()+a, _pop.begin()+b+1, objComp);
// min & max
min = _pop[b].objectiveVector()[obj];
max = _pop[a].objectiveVector()[obj];
// avoid extreme case
if (min == max)
{
min -= tiny();
max += tiny();
}
// set the diversity value to infiny for min and max
_pop[a].diversity(inf());
_pop[b].diversity(inf());
// set the diversity values for the other individuals
for (unsigned int i=a+1; i<b; i++)
{
distance = (_pop[i-1].objectiveVector()[obj] - _pop[i+1].objectiveVector()[obj]) / (max-min);
_pop[i].diversity(_pop[i].diversity() + distance);
}
}
}
// go to the next front
a = b+1;
}
}
/**
* Returns the index of the last individual having the same fitness value than _pop[_start]
* @param _pop the population
* @param _start the index to start from
*/
unsigned int lastIndex (eoPop < MOEOT > & _pop, unsigned int _start)
{
unsigned int i=_start;
while ( (i<_pop.size()-1) && (_pop[i].fitness()==_pop[i+1].fitness()) )
{
i++;
}
return i;
}
};
#endif /*MOEOFRONTBYFRONTCROWDINGDISTANCEDIVERSITYASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoFrontByFrontSharingDiversityAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOFRONTBYFRONTSHARINGDIVERSITYASSIGNMENT_H_
#define MOEOFRONTBYFRONTSHARINGDIVERSITYASSIGNMENT_H_
#include <diversity/moeoSharingDiversityAssignment.h>
/**
* Sharing assignment scheme on the way it is used in NSGA.
*/
template < class MOEOT >
class moeoFrontByFrontSharingDiversityAssignment : public moeoSharingDiversityAssignment < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor
* @param _distance the distance used to compute the neighborhood of solutions (can be related to the decision space or the objective space)
* @param _nicheSize neighborhood size in terms of radius distance (closely related to the way the distances are computed)
* @param _alpha parameter used to regulate the shape of the sharing function
*/
moeoFrontByFrontSharingDiversityAssignment(moeoDistance<MOEOT,double> & _distance, double _nicheSize = 0.5, double _alpha = 2.0) : moeoSharingDiversityAssignment < MOEOT >(_distance, _nicheSize, _alpha)
{}
/**
* Ctor with an euclidean distance (with normalized objective values) in the objective space is used as default
* @param _nicheSize neighborhood size in terms of radius distance (closely related to the way the distances are computed)
* @param _alpha parameter used to regulate the shape of the sharing function
*/
moeoFrontByFrontSharingDiversityAssignment(double _nicheSize = 0.5, double _alpha = 2.0) : moeoSharingDiversityAssignment < MOEOT >(_nicheSize, _alpha)
{}
/**
* @warning NOT IMPLEMENTED, DO NOTHING !
* Updates the diversity values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
* @warning NOT IMPLEMENTED, DO NOTHING !
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::cout << "WARNING : updateByDeleting not implemented in moeoSharingDiversityAssignment" << std::endl;
}
private:
using moeoSharingDiversityAssignment < MOEOT >::distance;
using moeoSharingDiversityAssignment < MOEOT >::nicheSize;
using moeoSharingDiversityAssignment < MOEOT >::sh;
using moeoSharingDiversityAssignment < MOEOT >::operator();
/**
* Sets similarities FRONT BY FRONT for every solution contained in the population _pop
* @param _pop the population
*/
void setSimilarities(eoPop < MOEOT > & _pop)
{
// compute distances between every individuals
moeoDistanceMatrix < MOEOT , double > dMatrix (_pop.size(), distance);
dMatrix(_pop);
// sets the distance to bigger than the niche size for every couple of solutions that do not belong to the same front
for (unsigned int i=0; i<_pop.size(); i++)
{
for (unsigned int j=0; j<i; j++)
{
if (_pop[i].fitness() != _pop[j].fitness())
{
dMatrix[i][j] = nicheSize;
dMatrix[j][i] = nicheSize;
}
}
}
// compute similarities
double sum;
for (unsigned int i=0; i<_pop.size(); i++)
{
sum = 0.0;
for (unsigned int j=0; j<_pop.size(); j++)
{
sum += sh(dMatrix[i][j]);
}
_pop[i].diversity(sum);
}
}
};
#endif /*MOEOFRONTBYFRONTSHARINGDIVERSITYASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoSharingDiversityAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOSHARINGDIVERSITYASSIGNMENT_H_
#define MOEOSHARINGDIVERSITYASSIGNMENT_H_
#include <eoPop.h>
#include <comparator/moeoDiversityThenFitnessComparator.h>
#include <distance/moeoDistance.h>
#include <distance/moeoDistanceMatrix.h>
#include <distance/moeoEuclideanDistance.h>
#include <diversity/moeoDiversityAssignment.h>
/**
* Sharing assignment scheme originally porposed by:
* D. E. Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning", Addision-Wesley, MA, USA (1989).
*/
template < class MOEOT >
class moeoSharingDiversityAssignment : public moeoDiversityAssignment < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor
* @param _distance the distance used to compute the neighborhood of solutions (can be related to the decision space or the objective space)
* @param _nicheSize neighborhood size in terms of radius distance (closely related to the way the distances are computed)
* @param _alpha parameter used to regulate the shape of the sharing function
*/
moeoSharingDiversityAssignment(moeoDistance<MOEOT,double> & _distance, double _nicheSize = 0.5, double _alpha = 1.0) : distance(_distance), nicheSize(_nicheSize), alpha(_alpha)
{}
/**
* Ctor with an euclidean distance (with normalized objective values) in the objective space is used as default
* @param _nicheSize neighborhood size in terms of radius distance (closely related to the way the distances are computed)
* @param _alpha parameter used to regulate the shape of the sharing function
*/
moeoSharingDiversityAssignment(double _nicheSize = 0.5, double _alpha = 1.0) : distance(defaultDistance), nicheSize(_nicheSize), alpha(_alpha)
{}
/**
* Sets diversity values for every solution contained in the population _pop
* @param _pop the population
*/
void operator()(eoPop < MOEOT > & _pop)
{
// 1 - set simuilarities
setSimilarities(_pop);
// 2 - a higher diversity is better, so the values need to be inverted
moeoDiversityThenFitnessComparator < MOEOT > divComparator;
double max = std::max_element(_pop.begin(), _pop.end(), divComparator)->diversity();
for (unsigned int i=0 ; i<_pop.size() ; i++)
{
_pop[i].diversity(max - _pop[i].diversity());
}
}
/**
* @warning NOT IMPLEMENTED, DO NOTHING !
* Updates the diversity values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
* @warning NOT IMPLEMENTED, DO NOTHING !
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::cout << "WARNING : updateByDeleting not implemented in moeoSharingDiversityAssignment" << std::endl;
}
protected:
/** the distance used to compute the neighborhood of solutions */
moeoDistance < MOEOT , double > & distance;
/** euclidean distancein the objective space (can be used as default) */
moeoEuclideanDistance < MOEOT > defaultDistance;
/** neighborhood size in terms of radius distance */
double nicheSize;
/** parameter used to regulate the shape of the sharing function */
double alpha;
/**
* Sets similarities for every solution contained in the population _pop
* @param _pop the population
*/
virtual void setSimilarities(eoPop < MOEOT > & _pop)
{
// compute distances between every individuals
moeoDistanceMatrix < MOEOT , double > dMatrix (_pop.size(), distance);
dMatrix(_pop);
// compute similarities
double sum;
for (unsigned int i=0; i<_pop.size(); i++)
{
sum = 0.0;
for (unsigned int j=0; j<_pop.size(); j++)
{
sum += sh(dMatrix[i][j]);
}
_pop[i].diversity(sum);
}
}
/**
* Sharing function
* @param _dist the distance value
*/
double sh(double _dist)
{
double result;
if (_dist < nicheSize)
{
result = 1.0 - pow(_dist / nicheSize, alpha);
}
else
{
result = 0.0;
}
return result;
}
};
#endif /*MOEOSHARINGDIVERSITYASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// make_checkpoint_moeo.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MAKE_CHECKPOINT_MOEO_H_
#define MAKE_CHECKPOINT_MOEO_H_
#include <stdlib.h>
#include <sstream>
#include <eoContinue.h>
#include <eoEvalFuncCounter.h>
#include <utils/checkpointing>
#include <utils/selectors.h>
#include <utils/eoParser.h>
#include <utils/eoState.h>
#include <metric/moeoContributionMetric.h>
#include <metric/moeoEntropyMetric.h>
#include <utils/moeoArchiveUpdater.h>
#include <utils/moeoArchiveObjectiveVectorSavingUpdater.h>
#include <utils/moeoBinaryMetricSavingUpdater.h>
bool testDirRes(std::string _dirName, bool _erase);
/**
* This functions allows to build an eoCheckPoint for multi-objective optimization from the parser (partly taken from make_checkpoint_pareto.h)
* @param _parser the parser
* @param _state to store allocated objects
* @param _eval the funtions evaluator
* @param _continue the stopping crietria
* @param _pop the population
* @param _archive the archive of non-dominated solutions
*/
template < class MOEOT >
eoCheckPoint < MOEOT > & do_make_checkpoint_moeo (eoParser & _parser, eoState & _state, eoEvalFuncCounter < MOEOT > & _eval, eoContinue < MOEOT > & _continue, eoPop < MOEOT > & _pop, moeoArchive < MOEOT > & _archive)
{
eoCheckPoint < MOEOT > & checkpoint = _state.storeFunctor(new eoCheckPoint < MOEOT > (_continue));
/* the objective vector type */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
///////////////////
// Counters
//////////////////
// is nb Eval to be used as counter?
//bool useEval = _parser.getORcreateParam(true, "useEval", "Use nb of eval. as counter (vs nb of gen.)", '\0', "Output").value();
// Create anyway a generation-counter parameter
eoValueParam<unsigned int> *generationCounter = new eoValueParam<unsigned int>(0, "Gen.");
// Create an incrementor (sub-class of eoUpdater).
eoIncrementor<unsigned int> & increment = _state.storeFunctor( new eoIncrementor<unsigned int>(generationCounter->value()) );
// Add it to the checkpoint
checkpoint.add(increment);
// dir for DISK output
std::string & dirName = _parser.getORcreateParam(std::string("Res"), "resDir", "Directory to store DISK outputs", '\0', "Output").value();
// shoudl we empty it if exists
eoValueParam<bool>& eraseParam = _parser.getORcreateParam(true, "eraseDir", "erase files in dirName if any", '\0', "Output");
bool dirOK = false; // not tested yet
// Dump of the whole population
//-----------------------------
bool printPop = _parser.getORcreateParam(false, "printPop", "Print sorted pop. every gen.", '\0', "Output").value();
eoSortedPopStat<MOEOT> * popStat;
if ( printPop ) // we do want pop dump
{
popStat = & _state.storeFunctor(new eoSortedPopStat<MOEOT>);
checkpoint.add(*popStat);
}
//////////////////////////////////
// State savers
//////////////////////////////
// feed the state to state savers
// save state every N generation
eoValueParam<unsigned int>& saveFrequencyParam = _parser.createParam((unsigned int)(0), "saveFrequency", "Save every F generation (0 = only final state, absent = never)", '\0', "Persistence" );
if (_parser.isItThere(saveFrequencyParam))
{
// first make sure dirName is OK
if (! dirOK )
dirOK = testDirRes(dirName, eraseParam.value()); // TRUE
unsigned int freq = (saveFrequencyParam.value()>0 ? saveFrequencyParam.value() : UINT_MAX );
#ifdef _MSVC
std::string stmp = dirName + "\generations";
#else
std::string stmp = dirName + "/generations";
#endif
eoCountedStateSaver *stateSaver1 = new eoCountedStateSaver(freq, _state, stmp);
_state.storeFunctor(stateSaver1);
checkpoint.add(*stateSaver1);
}
// save state every T seconds
eoValueParam<unsigned int>& saveTimeIntervalParam = _parser.getORcreateParam((unsigned int)(0), "saveTimeInterval", "Save every T seconds (0 or absent = never)", '\0',"Persistence" );
if (_parser.isItThere(saveTimeIntervalParam) && saveTimeIntervalParam.value()>0)
{
// first make sure dirName is OK
if (! dirOK )
dirOK = testDirRes(dirName, eraseParam.value()); // TRUE
#ifdef _MSVC
std::string stmp = dirName + "\time";
#else
std::string stmp = dirName + "/time";
#endif
eoTimedStateSaver *stateSaver2 = new eoTimedStateSaver(saveTimeIntervalParam.value(), _state, stmp);
_state.storeFunctor(stateSaver2);
checkpoint.add(*stateSaver2);
}
///////////////////
// Archive
//////////////////
// update the archive every generation
bool updateArch = _parser.getORcreateParam(true, "updateArch", "Update the archive at each gen.", '\0', "Evolution Engine").value();
if (updateArch)
{
moeoArchiveUpdater < MOEOT > * updater = new moeoArchiveUpdater < MOEOT > (_archive, _pop);
_state.storeFunctor(updater);
checkpoint.add(*updater);
}
// store the objective vectors contained in the archive every generation
bool storeArch = _parser.getORcreateParam(false, "storeArch", "Store the archive's objective vectors at each gen.", '\0', "Output").value();
if (storeArch)
{
if (! dirOK )
dirOK = testDirRes(dirName, eraseParam.value()); // TRUE
#ifdef _MSVC
std::string stmp = dirName + "\arch";
#else
std::string stmp = dirName + "/arch";
#endif
moeoArchiveObjectiveVectorSavingUpdater < MOEOT > * save_updater = new moeoArchiveObjectiveVectorSavingUpdater < MOEOT > (_archive, stmp);
_state.storeFunctor(save_updater);
checkpoint.add(*save_updater);
}
// store the contribution of the non-dominated solutions
bool cont = _parser.getORcreateParam(false, "contribution", "Store the contribution of the archive at each gen.", '\0', "Output").value();
if (cont)
{
if (! dirOK )
dirOK = testDirRes(dirName, eraseParam.value()); // TRUE
#ifdef _MSVC
std::string stmp = dirName + "\contribution";
#else
std::string stmp = dirName + "/contribution";
#endif
moeoContributionMetric < ObjectiveVector > * contribution = new moeoContributionMetric < ObjectiveVector >;
moeoBinaryMetricSavingUpdater < MOEOT > * contribution_updater = new moeoBinaryMetricSavingUpdater < MOEOT > (*contribution, _archive, stmp);
_state.storeFunctor(contribution_updater);
checkpoint.add(*contribution_updater);
}
// store the entropy of the non-dominated solutions
bool ent = _parser.getORcreateParam(false, "entropy", "Store the entropy of the archive at each gen.", '\0', "Output").value();
if (ent)
{
if (! dirOK )
dirOK = testDirRes(dirName, eraseParam.value()); // TRUE
#ifdef _MSVC
std::string stmp = dirName + "\entropy";
#else
std::string stmp = dirName + "/entropy";
#endif
moeoEntropyMetric < ObjectiveVector > * entropy = new moeoEntropyMetric < ObjectiveVector >;
moeoBinaryMetricSavingUpdater < MOEOT > * entropy_updater = new moeoBinaryMetricSavingUpdater < MOEOT > (*entropy, _archive, stmp);
_state.storeFunctor(entropy_updater);
checkpoint.add(*entropy_updater);
}
// and that's it for the (control and) output
return checkpoint;
}
#endif /*MAKE_CHECKPOINT_MOEO_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// make_continue_moeo.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MAKE_CONTINUE_MOEO_H_
#define MAKE_CONTINUE_MOEO_H_
#include <eoCombinedContinue.h>
#include <eoGenContinue.h>
#include <eoEvalContinue.h>
#include <eoFitContinue.h>
#include <eoTimeContinue.h>
#ifndef _MSC_VER
#include <eoCtrlCContinue.h>
#endif
#include <utils/eoParser.h>
#include <utils/eoState.h>
/**
* Helper function
* @param _combined the eoCombinedContinue object
* @param _cont the eoContinue to add
*/
template <class MOEOT>
eoCombinedContinue<MOEOT> * make_combinedContinue(eoCombinedContinue<MOEOT> *_combined, eoContinue<MOEOT> *_cont)
{
if (_combined) // already exists
_combined->add(*_cont);
else
_combined = new eoCombinedContinue<MOEOT>(*_cont);
return _combined;
}
/**
* This functions allows to build a eoContinue for multi-objective optimization from the parser (partly taken from make_continue_pareto.h)
* @param _parser the parser
* @param _state to store allocated objects
* @param _eval the funtions evaluator
*/
template <class MOEOT>
eoContinue<MOEOT> & do_make_continue_moeo(eoParser& _parser, eoState& _state, eoEvalFuncCounter<MOEOT> & _eval)
{
// the combined continue - to be filled
eoCombinedContinue<MOEOT> *continuator = NULL;
// First the eoGenContinue - need a default value so you can run blind
// but we also need to be able to avoid it <--> 0
eoValueParam<unsigned int>& maxGenParam = _parser.createParam((unsigned int)(100), "maxGen", "Maximum number of generations (0 = none)",'G',"Stopping criterion");
if (maxGenParam.value()) // positive: -> define and store
{
eoGenContinue<MOEOT> *genCont = new eoGenContinue<MOEOT>(maxGenParam.value());
_state.storeFunctor(genCont);
// and "add" to combined
continuator = make_combinedContinue<MOEOT>(continuator, genCont);
}
// maxEval
eoValueParam<unsigned long>& maxEvalParam = _parser.getORcreateParam((unsigned long)(0), "maxEval", "Maximum number of evaluations (0 = none)", 'E', "Stopping criterion");
if (maxEvalParam.value())
{
eoEvalContinue<MOEOT> *evalCont = new eoEvalContinue<MOEOT>(_eval, maxEvalParam.value());
_state.storeFunctor(evalCont);
// and "add" to combined
continuator = make_combinedContinue<MOEOT>(continuator, evalCont);
}
// maxTime
eoValueParam<unsigned long>& maxTimeParam = _parser.getORcreateParam((unsigned long)(0), "maxTime", "Maximum running time in seconds (0 = none)", 'T', "Stopping criterion");
if (maxTimeParam.value()) // positive: -> define and store
{
eoTimeContinue<MOEOT> *timeCont = new eoTimeContinue<MOEOT>(maxTimeParam.value());
_state.storeFunctor(timeCont);
// and "add" to combined
continuator = make_combinedContinue<MOEOT>(continuator, timeCont);
}
// CtrlC
#ifndef _MSC_VER
// the CtrlC interception (Linux only I'm afraid)
eoCtrlCContinue<MOEOT> *ctrlCCont;
eoValueParam<bool>& ctrlCParam = _parser.createParam(true, "CtrlC", "Terminate current generation upon Ctrl C",'C', "Stopping criterion");
if (_parser.isItThere(ctrlCParam))
{
ctrlCCont = new eoCtrlCContinue<MOEOT>;
// store
_state.storeFunctor(ctrlCCont);
// add to combinedContinue
continuator = make_combinedContinue<MOEOT>(continuator, ctrlCCont);
}
#endif
// now check that there is at least one!
if (!continuator)
throw std::runtime_error("You MUST provide a stopping criterion");
// OK, it's there: store in the eoState
_state.storeFunctor(continuator);
// and return
return *continuator;
}
#endif /*MAKE_CONTINUE_MOEO_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// make_ea_moeo.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MAKE_EA_MOEO_H_
#define MAKE_EA_MOEO_H_
#include <stdlib.h>
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoGeneralBreeder.h>
#include <eoGenOp.h>
#include <utils/eoParser.h>
#include <utils/eoState.h>
#include <algo/moeoEA.h>
#include <algo/moeoEasyEA.h>
#include <archive/moeoArchive.h>
#include <comparator/moeoAggregativeComparator.h>
#include <comparator/moeoComparator.h>
#include <comparator/moeoDiversityThenFitnessComparator.h>
#include <comparator/moeoFitnessThenDiversityComparator.h>
#include <diversity/moeoDiversityAssignment.h>
#include <diversity/moeoDummyDiversityAssignment.h>
#include <diversity/moeoFrontByFrontCrowdingDistanceDiversityAssignment.h>
#include <diversity/moeoFrontByFrontSharingDiversityAssignment.h>
#include <fitness/moeoFastNonDominatedSortingFitnessAssignment.h>
#include <fitness/moeoDummyFitnessAssignment.h>
#include <fitness/moeoFitnessAssignment.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <metric/moeoAdditiveEpsilonBinaryMetric.h>
#include <metric/moeoHypervolumeBinaryMetric.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
#include <replacement/moeoElitistReplacement.h>
#include <replacement/moeoEnvironmentalReplacement.h>
#include <replacement/moeoGenerationalReplacement.h>
#include <replacement/moeoReplacement.h>
#include <selection/moeoDetTournamentSelect.h>
#include <selection/moeoRandomSelect.h>
#include <selection/moeoStochTournamentSelect.h>
#include <selection/moeoSelectOne.h>
#include <selection/moeoSelectors.h>
/**
* This functions allows to build a moeoEA from the parser
* @param _parser the parser
* @param _state to store allocated objects
* @param _eval the funtions evaluator
* @param _continue the stopping crietria
* @param _op the variation operators
* @param _archive the archive of non-dominated solutions
*/
template < class MOEOT >
moeoEA < MOEOT > & do_make_ea_moeo(eoParser & _parser, eoState & _state, eoEvalFunc < MOEOT > & _eval, eoContinue < MOEOT > & _continue, eoGenOp < MOEOT > & _op, moeoArchive < MOEOT > & _archive)
{
/* the objective vector type */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/* the fitness assignment strategy */
std::string & fitnessParam = _parser.createParam(std::string("FastNonDominatedSorting"), "fitness",
"Fitness assignment scheme: Dummy, FastNonDominatedSorting or IndicatorBased", 'F',
"Evolution Engine").value();
std::string & indicatorParam = _parser.createParam(std::string("Epsilon"), "indicator",
"Binary indicator for IndicatorBased: Epsilon, Hypervolume", 'i',
"Evolution Engine").value();
double rho = _parser.createParam(1.1, "rho", "reference point for the hypervolume indicator", 'r',
"Evolution Engine").value();
double kappa = _parser.createParam(0.05, "kappa", "Scaling factor kappa for IndicatorBased", 'k',
"Evolution Engine").value();
moeoFitnessAssignment < MOEOT > * fitnessAssignment;
if (fitnessParam == std::string("Dummy"))
{
fitnessAssignment = new moeoDummyFitnessAssignment < MOEOT> ();
}
else if (fitnessParam == std::string("FastNonDominatedSorting"))
{
fitnessAssignment = new moeoFastNonDominatedSortingFitnessAssignment < MOEOT> ();
}
else if (fitnessParam == std::string("IndicatorBased"))
{
// metric
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > *metric;
if (indicatorParam == std::string("Epsilon"))
{
metric = new moeoAdditiveEpsilonBinaryMetric < ObjectiveVector >;
}
else if (indicatorParam == std::string("Hypervolume"))
{
metric = new moeoHypervolumeBinaryMetric < ObjectiveVector > (rho);
}
else
{
std::string stmp = std::string("Invalid binary quality indicator: ") + indicatorParam;
throw std::runtime_error(stmp.c_str());
}
fitnessAssignment = new moeoIndicatorBasedFitnessAssignment < MOEOT > (*metric, kappa);
}
else
{
std::string stmp = std::string("Invalid fitness assignment strategy: ") + fitnessParam;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(fitnessAssignment);
/* the diversity assignment strategy */
eoValueParam<eoParamParamType> & diversityParam = _parser.createParam(eoParamParamType("Dummy"), "diversity",
"Diversity assignment scheme: Dummy, Sharing(nicheSize) or Crowding", 'D', "Evolution Engine");
eoParamParamType & diversityParamValue = diversityParam.value();
moeoDiversityAssignment < MOEOT > * diversityAssignment;
if (diversityParamValue.first == std::string("Dummy"))
{
diversityAssignment = new moeoDummyDiversityAssignment < MOEOT> ();
}
else if (diversityParamValue.first == std::string("Sharing"))
{
double nicheSize;
if (!diversityParamValue.second.size()) // no parameter added
{
std::cerr << "WARNING, no niche size given for Sharing, using 0.5" << std::endl;
nicheSize = 0.5;
diversityParamValue.second.push_back(std::string("0.5"));
}
else
{
nicheSize = atoi(diversityParamValue.second[0].c_str());
}
diversityAssignment = new moeoFrontByFrontSharingDiversityAssignment < MOEOT> (nicheSize);
}
else if (diversityParamValue.first == std::string("Crowding"))
{
diversityAssignment = new moeoFrontByFrontCrowdingDistanceDiversityAssignment < MOEOT> ();
}
else
{
std::string stmp = std::string("Invalid diversity assignment strategy: ") + diversityParamValue.first;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(diversityAssignment);
/* the comparator strategy */
std::string & comparatorParam = _parser.createParam(std::string("FitnessThenDiversity"), "comparator",
"Comparator scheme: FitnessThenDiversity, DiversityThenFitness or Aggregative", 'C', "Evolution Engine").value();
moeoComparator < MOEOT > * comparator;
if (comparatorParam == std::string("FitnessThenDiversity"))
{
comparator = new moeoFitnessThenDiversityComparator < MOEOT> ();
}
else if (comparatorParam == std::string("DiversityThenFitness"))
{
comparator = new moeoDiversityThenFitnessComparator < MOEOT> ();
}
else if (comparatorParam == std::string("Aggregative"))
{
comparator = new moeoAggregativeComparator < MOEOT> ();
}
else
{
std::string stmp = std::string("Invalid comparator strategy: ") + comparatorParam;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(comparator);
/* the selection strategy */
eoValueParam < eoParamParamType > & selectionParam = _parser.createParam(eoParamParamType("DetTour(2)"), "selection",
"Selection scheme: DetTour(T), StochTour(t) or Random", 'S', "Evolution Engine");
eoParamParamType & ppSelect = selectionParam.value();
moeoSelectOne < MOEOT > * select;
if (ppSelect.first == std::string("DetTour"))
{
unsigned int tSize;
if (!ppSelect.second.size()) // no parameter added
{
std::cerr << "WARNING, no parameter passed to DetTour, using 2" << std::endl;
tSize = 2;
// put back 2 in parameter for consistency (and status file)
ppSelect.second.push_back(std::string("2"));
}
else // parameter passed by user as DetTour(T)
{
tSize = atoi(ppSelect.second[0].c_str());
}
select = new moeoDetTournamentSelect < MOEOT > (*comparator, tSize);
}
else if (ppSelect.first == std::string("StochTour"))
{
double tRate;
if (!ppSelect.second.size()) // no parameter added
{
std::cerr << "WARNING, no parameter passed to StochTour, using 1" << std::endl;
tRate = 1;
// put back 1 in parameter for consistency (and status file)
ppSelect.second.push_back(std::string("1"));
}
else // parameter passed by user as StochTour(T)
{
tRate = atof(ppSelect.second[0].c_str());
}
select = new moeoStochTournamentSelect < MOEOT > (*comparator, tRate);
}
/*
else if (ppSelect.first == string("Roulette"))
{
// TO DO !
// ...
}
*/
else if (ppSelect.first == std::string("Random"))
{
select = new moeoRandomSelect <MOEOT > ();
}
else
{
std::string stmp = std::string("Invalid selection strategy: ") + ppSelect.first;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(select);
/* the replacement strategy */
std::string & replacementParam = _parser.createParam(std::string("Elitist"), "replacement",
"Replacement scheme: Elitist, Environmental or Generational", 'R', "Evolution Engine").value();
moeoReplacement < MOEOT > * replace;
if (replacementParam == std::string("Elitist"))
{
replace = new moeoElitistReplacement < MOEOT> (*fitnessAssignment, *diversityAssignment, *comparator);
}
else if (replacementParam == std::string("Environmental"))
{
replace = new moeoEnvironmentalReplacement < MOEOT> (*fitnessAssignment, *diversityAssignment, *comparator);
}
else if (replacementParam == std::string("Generational"))
{
replace = new moeoGenerationalReplacement < MOEOT> ();
}
else
{
std::string stmp = std::string("Invalid replacement strategy: ") + replacementParam;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(replace);
/* the number of offspring */
eoValueParam < eoHowMany > & offspringRateParam = _parser.createParam(eoHowMany(1.0), "nbOffspring",
"Number of offspring (percentage or absolute)", 'O', "Evolution Engine");
// the general breeder
eoGeneralBreeder < MOEOT > * breed = new eoGeneralBreeder < MOEOT > (*select, _op, offspringRateParam.value());
_state.storeFunctor(breed);
// the eoEasyEA
moeoEA < MOEOT > * algo = new moeoEasyEA < MOEOT > (_continue, _eval, *breed, *replace, *fitnessAssignment, *diversityAssignment);
_state.storeFunctor(algo);
return *algo;
}
#endif /*MAKE_EA_MOEO_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// make_ls_moeo.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MAKE_LS_MOEO_H_
#define MAKE_LS_MOEO_H_
#include <eoContinue.h>
#include <eoEvalFunc.h>
#include <eoGenOp.h>
#include <utils/eoParser.h>
#include <utils/eoState.h>
#include <algo/moeoIBMOLS.h>
#include <algo/moeoIteratedIBMOLS.h>
#include <algo/moeoLS.h>
#include <archive/moeoArchive.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
#include <move/moeoMoveIncrEval.h>
/**
* This functions allows to build a moeoLS from the parser
* @param _parser the parser
* @param _state to store allocated objects
* @param _eval the funtions evaluator
* @param _moveIncrEval the incremental evaluation
* @param _continue the stopping crietria
* @param _op the variation operators
* @param _opInit the initilization operator
* @param _moveInit the move initializer
* @param _nextMove the move incrementor
* @param _archive the archive of non-dominated solutions
*/
template < class MOEOT, class Move >
moeoLS < MOEOT, eoPop<MOEOT> & > & do_make_ls_moeo (
eoParser & _parser,
eoState & _state,
eoEvalFunc < MOEOT > & _eval,
moeoMoveIncrEval < Move > & _moveIncrEval,
eoContinue < MOEOT > & _continue,
eoMonOp < MOEOT > & _op,
eoMonOp < MOEOT > & _opInit,
moMoveInit < Move > & _moveInit,
moNextMove < Move > & _nextMove,
moeoArchive < MOEOT > & _archive
)
{
/* the objective vector type */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/* the fitness assignment strategy */
std::string & fitnessParam = _parser.getORcreateParam(std::string("IndicatorBased"), "fitness",
"Fitness assignment strategy parameter: IndicatorBased...", 'F',
"Evolution Engine").value();
std::string & indicatorParam = _parser.getORcreateParam(std::string("Epsilon"), "indicator",
"Binary indicator to use with the IndicatorBased assignment: Epsilon, Hypervolume", 'i',
"Evolution Engine").value();
double rho = _parser.getORcreateParam(1.1, "rho", "reference point for the hypervolume indicator",
'r', "Evolution Engine").value();
double kappa = _parser.getORcreateParam(0.05, "kappa", "Scaling factor kappa for IndicatorBased",
'k', "Evolution Engine").value();
moeoIndicatorBasedFitnessAssignment < MOEOT > * fitnessAssignment;
if (fitnessParam == std::string("IndicatorBased"))
{
// metric
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > *metric;
if (indicatorParam == std::string("Epsilon"))
{
metric = new moeoAdditiveEpsilonBinaryMetric < ObjectiveVector >;
}
else if (indicatorParam == std::string("Hypervolume"))
{
metric = new moeoHypervolumeBinaryMetric < ObjectiveVector > (rho);
}
else
{
std::string stmp = std::string("Invalid binary quality indicator: ") + indicatorParam;
throw std::runtime_error(stmp.c_str());
}
fitnessAssignment = new moeoIndicatorBasedFitnessAssignment < MOEOT> (*metric, kappa);
}
else
{
std::string stmp = std::string("Invalid fitness assignment strategy: ") + fitnessParam;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(fitnessAssignment);
// number of iterations
unsigned int n = _parser.getORcreateParam(1, "n", "Number of iterations for population Initialization", 'n', "Evolution Engine").value();
// LS
std::string & lsParam = _parser.getORcreateParam(std::string("I-IBMOLS"), "ls",
"Local Search: IBMOLS, I-IBMOLS (Iterated-IBMOLS)...", 'L',
"Evolution Engine").value();
moeoLS < MOEOT, eoPop<MOEOT> & > * ls;
if (lsParam == std::string("IBMOLS"))
{
ls = new moeoIBMOLS < MOEOT, Move > (_moveInit, _nextMove, _eval, _moveIncrEval, *fitnessAssignment, _continue);;
}
else if (lsParam == std::string("I-IBMOLS"))
{
ls = new moeoIteratedIBMOLS < MOEOT, Move > (_moveInit, _nextMove, _eval, _moveIncrEval, *fitnessAssignment, _continue, _op, _opInit, n);
}
else
{
std::string stmp = std::string("Invalid fitness assignment strategy: ") + fitnessParam;
throw std::runtime_error(stmp.c_str());
}
_state.storeFunctor(ls);
// that's it !
return *ls;
}
#endif /*MAKE_LS_MOEO_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoAchievementFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOACHIEVEMENTFITNESSASSIGNMENT_H_
#define MOEOACHIEVEMENTFITNESSASSIGNMENT_H_
#include <vector>
#include <eoPop.h>
#include <fitness/moeoScalarFitnessAssignment.h>
/**
* Fitness assignment sheme based on the achievement scalarizing function propozed by Wiersbicki (1980).
*/
template < class MOEOT >
class moeoAchievementFitnessAssignment : public moeoScalarFitnessAssignment < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Default ctor
* @param _reference reference point vector
* @param _lambdas weighted coefficients vector
* @param _spn arbitrary small positive number (0 < _spn << 1)
*/
moeoAchievementFitnessAssignment(ObjectiveVector & _reference, std::vector < double > & _lambdas, double _spn=0.0001) : reference(_reference), lambdas(_lambdas), spn(_spn)
{
// consistency check
if ((spn < 0.0) || (spn > 1.0))
{
std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
spn = 0.0001;
}
}
/**
* Ctor with default values for lambdas (1/nObjectives)
* @param _reference reference point vector
* @param _spn arbitrary small positive number (0 < _spn << 1)
*/
moeoAchievementFitnessAssignment(ObjectiveVector & _reference, double _spn=0.0001) : reference(_reference), spn(_spn)
{
// compute the default values for lambdas
lambdas = std::vector < double > (ObjectiveVector::nObjectives());
for (unsigned int i=0 ; i<lambdas.size(); i++)
{
lambdas[i] = 1.0 / ObjectiveVector::nObjectives();
}
// consistency check
if ((spn < 0.0) || (spn > 1.0))
{
std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
spn = 0.0001;
}
}
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
virtual void operator()(eoPop < MOEOT > & _pop)
{
for (unsigned int i=0; i<_pop.size() ; i++)
{
compute(_pop[i]);
}
}
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account (nothing to do).
* @param _pop the population
* @param _objVec the objective vector
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
// nothing to do ;-)
}
/**
* Sets the reference point
* @param _reference the new reference point
*/
void setReference(const ObjectiveVector & _reference)
{
reference = _reference;
}
private:
/** the reference point */
ObjectiveVector reference;
/** the weighted coefficients vector */
std::vector < double > lambdas;
/** an arbitrary small positive number (0 < _spn << 1) */
double spn;
/**
* Returns a big value (regarded as infinite)
*/
double inf() const
{
return std::numeric_limits<double>::max();
}
/**
* Computes the fitness value for a solution
* @param _moeo the solution
*/
void compute(MOEOT & _moeo)
{
unsigned int nobj = MOEOT::ObjectiveVector::nObjectives();
double temp;
double min = inf();
double sum = 0;
for (unsigned int obj=0; obj<nobj; obj++)
{
temp = lambdas[obj] * (reference[obj] - _moeo.objectiveVector()[obj]);
min = std::min(min, temp);
sum += temp;
}
_moeo.fitness(min + spn*sum);
}
};
#endif /*MOEOACHIEVEMENTFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoCriterionBasedFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOCRITERIONBASEDFITNESSASSIGNMENT_H_
#define MOEOCRITERIONBASEDFITNESSASSIGNMENT_H_
#include <fitness/moeoFitnessAssignment.h>
/**
* moeoCriterionBasedFitnessAssignment is a moeoFitnessAssignment for criterion-based strategies.
*/
template < class MOEOT >
class moeoCriterionBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT > {};
#endif /*MOEOCRITERIONBASEDFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoDummyFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEODUMMYFITNESSASSIGNMENT_H_
#define MOEODUMMYFITNESSASSIGNMENT_H_
#include <fitness/moeoFitnessAssignment.h>
/**
* moeoDummyFitnessAssignment is a moeoFitnessAssignment that gives the value '0' as the individual's fitness for a whole population if it is invalid.
*/
template < class MOEOT >
class moeoDummyFitnessAssignment : public moeoFitnessAssignment < MOEOT >
{
public:
/** The type for objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Sets the fitness to '0' for every individuals of the population _pop if it is invalid
* @param _pop the population
*/
void operator () (eoPop < MOEOT > & _pop)
{
for (unsigned int idx = 0; idx<_pop.size (); idx++)
{
if (_pop[idx].invalidFitness())
{
// set the diversity to 0
_pop[idx].fitness(0.0);
}
}
}
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
// nothing to do... ;-)
}
};
#endif /*MOEODUMMYFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoFastNonDominatedSortingFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOFASTNONDOMINATEDSORTINGFITNESSASSIGNMENT_H_
#define MOEOFASTNONDOMINATEDSORTINGFITNESSASSIGNMENT_H_
#include <vector>
#include <eoPop.h>
#include <comparator/moeoObjectiveObjectiveVectorComparator.h>
#include <comparator/moeoObjectiveVectorComparator.h>
#include <comparator/moeoParetoObjectiveVectorComparator.h>
#include <fitness/moeoParetoBasedFitnessAssignment.h>
/**
* Fitness assignment sheme based on Pareto-dominance count proposed in:
* N. Srinivas, K. Deb, "Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms", Evolutionary Computation vol. 2, no. 3, pp. 221-248 (1994)
* and in:
* K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, no. 2 (2002).
* This strategy is, for instance, used in NSGA and NSGA-II.
*/
template < class MOEOT >
class moeoFastNonDominatedSortingFitnessAssignment : public moeoParetoBasedFitnessAssignment < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Default ctor
*/
moeoFastNonDominatedSortingFitnessAssignment() : comparator(paretoComparator)
{}
/**
* Ctor where you can choose your own way to compare objective vectors
* @param _comparator the functor used to compare objective vectors
*/
moeoFastNonDominatedSortingFitnessAssignment(moeoObjectiveVectorComparator < ObjectiveVector > & _comparator) : comparator(_comparator)
{}
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
void operator()(eoPop < MOEOT > & _pop)
{
// number of objectives for the problem under consideration
unsigned int nObjectives = MOEOT::ObjectiveVector::nObjectives();
if (nObjectives == 1)
{
// one objective
oneObjective(_pop);
}
else if (nObjectives == 2)
{
// two objectives (the two objectives function is still to implement)
mObjectives(_pop);
}
else if (nObjectives > 2)
{
// more than two objectives
mObjectives(_pop);
}
else
{
// problem with the number of objectives
throw std::runtime_error("Problem with the number of objectives in moeoNonDominatedSortingFitnessAssignment");
}
// a higher fitness is better, so the values need to be inverted
double max = _pop[0].fitness();
for (unsigned int i=1 ; i<_pop.size() ; i++)
{
max = std::max(max, _pop[i].fitness());
}
for (unsigned int i=0 ; i<_pop.size() ; i++)
{
_pop[i].fitness(max - _pop[i].fitness());
}
}
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
for (unsigned int i=0; i<_pop.size(); i++)
{
// if _pop[i] is dominated by _objVec
if ( comparator(_pop[i].objectiveVector(), _objVec) )
{
_pop[i].fitness(_pop[i].fitness()+1);
}
}
}
private:
/** Functor to compare two objective vectors */
moeoObjectiveVectorComparator < ObjectiveVector > & comparator;
/** Functor to compare two objective vectors according to Pareto dominance relation */
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
/** Functor allowing to compare two solutions according to their first objective value, then their second, and so on. */
class ObjectiveComparator : public moeoComparator < MOEOT >
{
public:
/**
* Returns true if _moeo1 < _moeo2 on the first objective, then on the second, and so on
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
return cmp(_moeo1.objectiveVector(), _moeo2.objectiveVector());
}
private:
/** the corresponding comparator for objective vectors */
moeoObjectiveObjectiveVectorComparator < ObjectiveVector > cmp;
} objComparator;
/**
* Sets the fitness values for mono-objective problems
* @param _pop the population
*/
void oneObjective (eoPop < MOEOT > & _pop)
{
// sorts the population in the ascending order
std::sort(_pop.begin(), _pop.end(), objComparator);
// assign fitness values
unsigned int rank = 1;
_pop[_pop.size()-1].fitness(rank);
for (unsigned int i=_pop.size()-2; i>=0; i--)
{
if (_pop[i].objectiveVector() != _pop[i+1].objectiveVector())
{
rank++;
}
_pop[i].fitness(rank);
}
}
/**
* Sets the fitness values for bi-objective problems with a complexity of O(n log n), where n stands for the population size
* @param _pop the population
*/
void twoObjectives (eoPop < MOEOT > & _pop)
{
//... TO DO !
}
/**
* Sets the fitness values for problems with more than two objectives with a complexity of O(n² log n), where n stands for the population size
* @param _pop the population
*/
void mObjectives (eoPop < MOEOT > & _pop)
{
// S[i] = indexes of the individuals dominated by _pop[i]
std::vector < std::vector<unsigned int> > S(_pop.size());
// n[i] = number of individuals that dominate the individual _pop[i]
std::vector < unsigned int > n(_pop.size(), 0);
// fronts: F[i] = indexes of the individuals contained in the ith front
std::vector < std::vector<unsigned int> > F(_pop.size()+2);
// used to store the number of the first front
F[1].reserve(_pop.size());
for (unsigned int p=0; p<_pop.size(); p++)
{
for (unsigned int q=0; q<_pop.size(); q++)
{
// if q is dominated by p
if ( comparator(_pop[q].objectiveVector(), _pop[p].objectiveVector()) )
{
// add q to the set of solutions dominated by p
S[p].push_back(q);
}
// if p is dominated by q
else if ( comparator(_pop[p].objectiveVector(), _pop[q].objectiveVector()) )
{
// increment the domination counter of p
n[p]++;
}
}
// if no individual dominates p
if (n[p] == 0)
{
// p belongs to the first front
_pop[p].fitness(1);
F[1].push_back(p);
}
}
// front counter
unsigned int counter=1;
unsigned int p,q;
while (! F[counter].empty())
{
// used to store the number of the next front
F[counter+1].reserve(_pop.size());
for (unsigned int i=0; i<F[counter].size(); i++)
{
p = F[counter][i];
for (unsigned int j=0; j<S[p].size(); j++)
{
q = S[p][j];
n[q]--;
// if no individual dominates q anymore
if (n[q] == 0)
{
// q belongs to the next front
_pop[q].fitness(counter+1);
F[counter+1].push_back(q);
}
}
}
counter++;
}
}
};
#endif /*MOEOFASTNONDOMINATEDSORTINGFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOFITNESSASSIGNMENT_H_
#define MOEOFITNESSASSIGNMENT_H_
#include <eoFunctor.h>
#include <eoPop.h>
/**
* Functor that sets the fitness values of a whole population.
*/
template < class MOEOT >
class moeoFitnessAssignment : public eoUF < eoPop < MOEOT > &, void >
{
public:
/** The type for objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
*/
virtual void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec) = 0;
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the individual _moeo into account.
* @param _pop the population
* @param _moeo the individual
*/
void updateByDeleting(eoPop < MOEOT > & _pop, MOEOT & _moeo)
{
updateByDeleting(_pop, _moeo.objectiveVector());
}
};
#endif /*MOEOFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoIndicatorBasedFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
#define MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
#include <math.h>
#include <vector>
#include <eoPop.h>
#include <fitness/moeoFitnessAssignment.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
#include <utils/moeoConvertPopToObjectiveVectors.h>
/**
* Fitness assignment sheme based an Indicator proposed in:
* E. Zitzler, S. Künzli, "Indicator-Based Selection in Multiobjective Search", Proc. 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp. 832-842, Birmingham, UK (2004).
* This strategy is, for instance, used in IBEA.
*/
template < class MOEOT >
class moeoIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor.
* @param _metric the quality indicator
* @param _kappa the scaling factor
*/
moeoIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa)
{}
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
void operator()(eoPop < MOEOT > & _pop)
{
// 1 - setting of the bounds
setup(_pop);
// 2 - computing every indicator values
computeValues(_pop);
// 3 - setting fitnesses
setFitnesses(_pop);
}
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::vector < double > v;
v.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
v[i] = metric(_objVec, _pop[i].objectiveVector());
}
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
}
}
/**
* Updates the fitness values of the whole population _pop by taking the adding of the objective vector _objVec into account
* and returns the fitness value of _objVec.
* @param _pop the population
* @param _objVec the objective vector
*/
double updateByAdding(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
std::vector < double > v;
// update every fitness values to take the new individual into account
v.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
v[i] = metric(_objVec, _pop[i].objectiveVector());
}
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
}
// compute the fitness of the new individual
v.clear();
v.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
v[i] = metric(_pop[i].objectiveVector(), _objVec);
}
double result = 0;
for (unsigned int i=0; i<v.size(); i++)
{
result -= exp(-v[i]/kappa);
}
return result;
}
protected:
/** the quality indicator */
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
/** the scaling factor */
double kappa;
/** the computed indicator values */
std::vector < std::vector<double> > values;
/**
* Sets the bounds for every objective using the min and the max value for every objective vector of _pop
* @param _pop the population
*/
void setup(const eoPop < MOEOT > & _pop)
{
double min, max;
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
min = _pop[0].objectiveVector()[i];
max = _pop[0].objectiveVector()[i];
for (unsigned int j=1; j<_pop.size(); j++)
{
min = std::min(min, _pop[j].objectiveVector()[i]);
max = std::max(max, _pop[j].objectiveVector()[i]);
}
// setting of the bounds for the objective i
metric.setup(min, max, i);
}
}
/**
* Compute every indicator value in values (values[i] = I(_v[i], _o))
* @param _pop the population
*/
void computeValues(const eoPop < MOEOT > & _pop)
{
values.clear();
values.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
values[i].resize(_pop.size());
for (unsigned int j=0; j<_pop.size(); j++)
{
if (i != j)
{
values[i][j] = metric(_pop[i].objectiveVector(), _pop[j].objectiveVector());
}
}
}
}
/**
* Sets the fitness value of the whple population
* @param _pop the population
*/
void setFitnesses(eoPop < MOEOT > & _pop)
{
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness(computeFitness(i));
}
}
/**
* Returns the fitness value of the _idx th individual of the population
* @param _idx the index
*/
double computeFitness(const unsigned int _idx)
{
double result = 0;
for (unsigned int i=0; i<values.size(); i++)
{
if (i != _idx)
{
result -= exp(-values[i][_idx]/kappa);
}
}
return result;
}
};
#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoParetoBasedFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOPARETOBASEDFITNESSASSIGNMENT_H_
#define MOEOPARETOBASEDFITNESSASSIGNMENT_H_
#include <fitness/moeoFitnessAssignment.h>
/**
* moeoParetoBasedFitnessAssignment is a moeoFitnessAssignment for Pareto-based strategies.
*/
template < class MOEOT >
class moeoParetoBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT > {};
#endif /*MOEOPARETOBASEDFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoReferencePointIndicatorBasedFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_
#define MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_
#include <math.h>
#include <eoPop.h>
#include <fitness/moeoFitnessAssignment.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
/**
* Fitness assignment sheme based a Reference Point and a Quality Indicator.
*/
template < class MOEOT >
class moeoReferencePointIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
{
public:
/** The type of objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor
* @param _refPoint the reference point
* @param _metric the quality indicator
*/
moeoReferencePointIndicatorBasedFitnessAssignment (ObjectiveVector & _refPoint, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric) :
refPoint(_refPoint), metric(_metric)
{}
/**
* Sets the fitness values for every solution contained in the population _pop
* @param _pop the population
*/
void operator()(eoPop < MOEOT > & _pop)
{
// 1 - setting of the bounds
setup(_pop);
// 2 - setting fitnesses
setFitnesses(_pop);
}
/**
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
* @param _pop the population
* @param _objVec the objective vector
*/
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
{
// nothing to do ;-)
}
protected:
/** the reference point */
ObjectiveVector & refPoint;
/** the quality indicator */
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
/**
* Sets the bounds for every objective using the min and the max value for every objective vector of _pop (and the reference point)
* @param _pop the population
*/
void setup(const eoPop < MOEOT > & _pop)
{
double min, max;
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
min = refPoint[i];
max = refPoint[i];
for (unsigned int j=0; j<_pop.size(); j++)
{
min = std::min(min, _pop[j].objectiveVector()[i]);
max = std::max(max, _pop[j].objectiveVector()[i]);
}
// setting of the bounds for the objective i
metric.setup(min, max, i);
}
}
/**
* Sets the fitness of every individual contained in the population _pop
* @param _pop the population
*/
void setFitnesses(eoPop < MOEOT > & _pop)
{
for (unsigned int i=0; i<_pop.size(); i++)
{
_pop[i].fitness(- metric(_pop[i].objectiveVector(), refPoint) );
}
}
};
#endif /*MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoScalarFitnessAssignment.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOSCALARFITNESSASSIGNMENT_H_
#define MOEOSCALARFITNESSASSIGNMENT_H_
#include <fitness/moeoFitnessAssignment.h>
/**
* moeoScalarFitnessAssignment is a moeoFitnessAssignment for scalar strategies.
*/
template < class MOEOT >
class moeoScalarFitnessAssignment : public moeoFitnessAssignment < MOEOT > {};
#endif /*MOEOSCALARFITNESSASSIGNMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoAdditiveEpsilonBinaryMetric.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOADDITIVEEPSILONBINARYMETRIC_H_
#define MOEOADDITIVEEPSILONBINARYMETRIC_H_
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
/**
* Additive epsilon binary metric allowing to compare two objective vectors as proposed in
* Zitzler E., Thiele L., Laumanns M., Fonseca C. M., Grunert da Fonseca V.:
* Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), pp.117132 (2003).
*/
template < class ObjectiveVector >
class moeoAdditiveEpsilonBinaryMetric : public moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double >
{
public:
/**
* Returns the minimal distance by which the objective vector _o1 must be translated in all objectives
* so that it weakly dominates the objective vector _o2
* @warning don't forget to set the bounds for every objective before the call of this function
* @param _o1 the first objective vector
* @param _o2 the second objective vector
*/
double operator()(const ObjectiveVector & _o1, const ObjectiveVector & _o2)
{
// computation of the epsilon value for the first objective
double result = epsilon(_o1, _o2, 0);
// computation of the epsilon value for the other objectives
double tmp;
for (unsigned int i=1; i<ObjectiveVector::Traits::nObjectives(); i++)
{
tmp = epsilon(_o1, _o2, i);
result = std::max(result, tmp);
}
// returns the maximum epsilon value
return result;
}
private:
/** the bounds for every objective */
using moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > :: bounds;
/**
* Returns the epsilon value by which the objective vector _o1 must be translated in the objective _obj
* so that it dominates the objective vector _o2
* @param _o1 the first objective vector
* @param _o2 the second objective vector
* @param _obj the index of the objective
*/
double epsilon(const ObjectiveVector & _o1, const ObjectiveVector & _o2, const unsigned int _obj)
{
double result;
// if the objective _obj have to be minimized
if (ObjectiveVector::Traits::minimizing(_obj))
{
// _o1[_obj] - _o2[_obj]
result = ( (_o1[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() ) - ( (_o2[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() );
}
// if the objective _obj have to be maximized
else
{
// _o2[_obj] - _o1[_obj]
result = ( (_o2[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() ) - ( (_o1[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() );
}
return result;
}
};
#endif /*MOEOADDITIVEEPSILONBINARYMETRIC_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoContributionMetric.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOCONTRIBUTIONMETRIC_H_
#define MOEOCONTRIBUTIONMETRIC_H_
#include <comparator/moeoParetoObjectiveVectorComparator.h>
#include <metric/moeoMetric.h>
/**
* The contribution metric evaluates the proportion of non-dominated solutions given by a Pareto set relatively to another Pareto set
* (Meunier, Talbi, Reininger: 'A multiobjective genetic algorithm for radio network optimization', in Proc. of the 2000 Congress on Evolutionary Computation, IEEE Press, pp. 317-324)
*/
template < class ObjectiveVector >
class moeoContributionMetric : public moeoVectorVsVectorBinaryMetric < ObjectiveVector, double >
{
public:
/**
* Returns the contribution of the Pareto set '_set1' relatively to the Pareto set '_set2'
* @param _set1 the first Pareto set
* @param _set2 the second Pareto set
*/
double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
unsigned int c = card_C(_set1, _set2);
unsigned int w1 = card_W(_set1, _set2);
unsigned int n1 = card_N(_set1, _set2);
unsigned int w2 = card_W(_set2, _set1);
unsigned int n2 = card_N(_set2, _set1);
return (double) (c / 2.0 + w1 + n1) / (c + w1 + n1 + w2 + n2);
}
private:
/** Functor to compare two objective vectors according to Pareto dominance relation */
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
/**
* Returns the number of solutions both in '_set1' and '_set2'
* @param _set1 the first Pareto set
* @param _set2 the second Pareto set
*/
unsigned int card_C (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
unsigned int c=0;
for (unsigned int i=0; i<_set1.size(); i++)
for (unsigned int j=0; j<_set2.size(); j++)
if (_set1[i] == _set2[j]) {
c++;
break;
}
return c;
}
/**
* Returns the number of solutions in '_set1' dominating at least one solution of '_set2'
* @param _set1 the first Pareto set
* @param _set2 the second Pareto set
*/
unsigned int card_W (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
unsigned int w=0;
for (unsigned int i=0; i<_set1.size(); i++)
for (unsigned int j=0; j<_set2.size(); j++)
if (paretoComparator(_set2[j], _set1[i]))
{
w++;
break;
}
return w;
}
/**
* Returns the number of solutions in '_set1' having no relation of dominance with those from '_set2'
* @param _set1 the first Pareto set
* @param _set2 the second Pareto set
*/
unsigned int card_N (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
unsigned int n=0;
for (unsigned int i=0; i<_set1.size(); i++) {
bool domin_rel = false;
for (unsigned int j=0; j<_set2.size(); j++)
if ( (paretoComparator(_set2[j], _set1[i])) || (paretoComparator(_set1[i], _set2[j])) )
{
domin_rel = true;
break;
}
if (! domin_rel)
n++;
}
return n;
}
};
#endif /*MOEOCONTRIBUTIONMETRIC_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoEntropyMetric.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOENTROPYMETRIC_H_
#define MOEOENTROPYMETRIC_H_
#include <vector>
#include <comparator/moeoParetoObjectiveVectorComparator.h>
#include <metric/moeoMetric.h>
/**
* The entropy gives an idea of the diversity of a Pareto set relatively to another
* (Basseur, Seynhaeve, Talbi: 'Design of Multi-objective Evolutionary Algorithms: Application to the Flow-shop Scheduling Problem', in Proc. of the 2002 Congress on Evolutionary Computation, IEEE Press, pp. 1155-1156)
*/
template < class ObjectiveVector >
class moeoEntropyMetric : public moeoVectorVsVectorBinaryMetric < ObjectiveVector, double >
{
public:
/**
* Returns the entropy of the Pareto set '_set1' relatively to the Pareto set '_set2'
* @param _set1 the first Pareto set
* @param _set2 the second Pareto set
*/
double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
// normalization
std::vector< ObjectiveVector > set1 = _set1;
std::vector< ObjectiveVector > set2= _set2;
removeDominated (set1);
removeDominated (set2);
prenormalize (set1);
normalize (set1);
normalize (set2);
// making of PO*
std::vector< ObjectiveVector > star; // rotf :-)
computeUnion (set1, set2, star);
removeDominated (star);
// making of PO1 U PO*
std::vector< ObjectiveVector > union_set1_star; // rotf again ...
computeUnion (set1, star, union_set1_star);
unsigned int C = union_set1_star.size();
float omega=0;
float entropy=0;
for (unsigned int i=0 ; i<C ; i++) {
unsigned int N_i = howManyInNicheOf (union_set1_star, union_set1_star[i], star.size());
unsigned int n_i = howManyInNicheOf (set1, union_set1_star[i], star.size());
if (n_i > 0) {
omega += 1.0 / N_i;
entropy += (float) n_i / (N_i * C) * log (((float) n_i / C) / log (2.0));
}
}
entropy /= - log (omega);
entropy *= log (2.0);
return entropy;
}
private:
/** vector of min values */
std::vector<double> vect_min_val;
/** vector of max values */
std::vector<double> vect_max_val;
/** Functor to compare two objective vectors according to Pareto dominance relation */
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
/**
* Removes the dominated individuals contained in _f
* @param _f a Pareto set
*/
void removeDominated(std::vector < ObjectiveVector > & _f) {
for (unsigned int i=0 ; i<_f.size(); i++) {
bool dom = false;
for (unsigned int j=0; j<_f.size(); j++)
if (i != j && paretoComparator(_f[i],_f[j]))
{
dom = true;
break;
}
if (dom) {
_f[i] = _f.back();
_f.pop_back();
i--;
}
}
}
/**
* Prenormalization
* @param _f a Pareto set
*/
void prenormalize (const std::vector< ObjectiveVector > & _f) {
vect_min_val.clear();
vect_max_val.clear();
for (unsigned int i=0 ; i<ObjectiveVector::nObjectives(); i++) {
float min_val = _f.front()[i], max_val = min_val;
for (unsigned int j=1 ; j<_f.size(); j++) {
if (_f[j][i] < min_val)
min_val = _f[j][i];
if (_f[j][i]>max_val)
max_val = _f[j][i];
}
vect_min_val.push_back(min_val);
vect_max_val.push_back (max_val);
}
}
/**
* Normalization
* @param _f a Pareto set
*/
void normalize (std::vector< ObjectiveVector > & _f) {
for (unsigned int i=0 ; i<ObjectiveVector::nObjectives(); i++)
for (unsigned int j=0; j<_f.size(); j++)
_f[j][i] = (_f[j][i] - vect_min_val[i]) / (vect_max_val[i] - vect_min_val[i]);
}
/**
* Computation of the union of _f1 and _f2 in _f
* @param _f1 the first Pareto set
* @param _f2 the second Pareto set
* @param _f the final Pareto set
*/
void computeUnion(const std::vector< ObjectiveVector > & _f1, const std::vector< ObjectiveVector > & _f2, std::vector< ObjectiveVector > & _f) {
_f = _f1 ;
for (unsigned int i=0; i<_f2.size(); i++) {
bool b = false;
for (unsigned int j=0; j<_f1.size(); j ++)
if (_f1[j] == _f2[i]) {
b = true;
break;
}
if (! b)
_f.push_back(_f2[i]);
}
}
/**
* How many in niche
*/
unsigned int howManyInNicheOf (const std::vector< ObjectiveVector > & _f, const ObjectiveVector & _s, unsigned int _size) {
unsigned int n=0;
for (unsigned int i=0 ; i<_f.size(); i++) {
if (euclidianDistance(_f[i], _s) < (_s.size() / (double) _size))
n++;
}
return n;
}
/**
* Euclidian distance
*/
double euclidianDistance (const ObjectiveVector & _set1, const ObjectiveVector & _to, unsigned int _deg = 2) {
double dist=0;
for (unsigned int i=0; i<_set1.size(); i++)
dist += pow(fabs(_set1[i] - _to[i]), (int)_deg);
return pow(dist, 1.0 / _deg);
}
};
#endif /*MOEOENTROPYMETRIC_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoHypervolumeBinaryMetric.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOHYPERVOLUMEBINARYMETRIC_H_
#define MOEOHYPERVOLUMEBINARYMETRIC_H_
#include <stdexcept>
#include <comparator/moeoParetoObjectiveVectorComparator.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
/**
* Hypervolume binary metric allowing to compare two objective vectors as proposed in
* Zitzler E., Künzli S.: Indicator-Based Selection in Multiobjective Search. In Parallel Problem Solving from Nature (PPSN VIII).
* Lecture Notes in Computer Science 3242, Springer, Birmingham, UK pp.832842 (2004).
* This indicator is based on the hypervolume concept introduced in
* Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study.
* Parallel Problem Solving from Nature (PPSN-V), pp.292-301 (1998).
*/
template < class ObjectiveVector >
class moeoHypervolumeBinaryMetric : public moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double >
{
public:
/**
* Ctor
* @param _rho value used to compute the reference point from the worst values for each objective (default : 1.1)
*/
moeoHypervolumeBinaryMetric(double _rho = 1.1) : rho(_rho)
{
// not-a-maximization problem check
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
if (ObjectiveVector::Traits::maximizing(i))
{
throw std::runtime_error("Hypervolume binary metric not yet implemented for a maximization problem in moeoHypervolumeBinaryMetric");
}
}
// consistency check
if (rho < 1)
{
std::cout << "Warning, value used to compute the reference point rho for the hypervolume calculation must not be smaller than 1" << std::endl;
std::cout << "Adjusted to 1" << std::endl;
rho = 1;
}
}
/**
* Returns the volume of the space that is dominated by _o2 but not by _o1 with respect to a reference point computed using rho.
* @warning don't forget to set the bounds for every objective before the call of this function
* @param _o1 the first objective vector
* @param _o2 the second objective vector
*/
double operator()(const ObjectiveVector & _o1, const ObjectiveVector & _o2)
{
double result;
// if _o2 is dominated by _o1
if ( paretoComparator(_o2,_o1) )
{
result = - hypervolume(_o1, _o2, ObjectiveVector::Traits::nObjectives()-1);
}
else
{
result = hypervolume(_o2, _o1, ObjectiveVector::Traits::nObjectives()-1);
}
return result;
}
private:
/** value used to compute the reference point from the worst values for each objective */
double rho;
/** the bounds for every objective */
using moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > :: bounds;
/** Functor to compare two objective vectors according to Pareto dominance relation */
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
/**
* Returns the volume of the space that is dominated by _o2 but not by _o1 with respect to a reference point computed using rho for the objective _obj.
* @param _o1 the first objective vector
* @param _o2 the second objective vector
* @param _obj the objective index
* @param _flag used for iteration, if _flag=true _o2 is not talen into account (default : false)
*/
double hypervolume(const ObjectiveVector & _o1, const ObjectiveVector & _o2, const unsigned int _obj, const bool _flag = false)
{
double result;
double range = rho * bounds[_obj].range();
double max = bounds[_obj].minimum() + range;
// value of _1 for the objective _obj
double v1 = _o1[_obj];
// value of _2 for the objective _obj (if _flag=true, v2=max)
double v2;
if (_flag)
{
v2 = max;
}
else
{
v2 = _o2[_obj];
}
// computation of the volume
if (_obj == 0)
{
if (v1 < v2)
{
result = (v2 - v1) / range;
}
else
{
result = 0;
}
}
else
{
if (v1 < v2)
{
result = ( hypervolume(_o1, _o2, _obj-1, true) * (v2 - v1) / range ) + ( hypervolume(_o1, _o2, _obj-1) * (max - v2) / range );
}
else
{
result = hypervolume(_o1, _o2, _obj-1) * (max - v2) / range;
}
}
return result;
}
};
#endif /*MOEOHYPERVOLUMEBINARYMETRIC_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoMetric.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOMETRIC_H_
#define MOEOMETRIC_H_
#include <vector>
#include <eoFunctor.h>
/**
* Base class for performance metrics (also known as quality indicators).
*/
class moeoMetric : public eoFunctorBase {};
/**
* Base class for unary metrics.
*/
template < class A, class R >
class moeoUnaryMetric : public eoUF < A, R >, public moeoMetric {};
/**
* Base class for binary metrics.
*/
template < class A1, class A2, class R >
class moeoBinaryMetric : public eoBF < A1, A2, R >, public moeoMetric {};
/**
* Base class for unary metrics dedicated to the performance evaluation of a single solution's objective vector.
*/
template < class ObjectiveVector, class R >
class moeoSolutionUnaryMetric : public moeoUnaryMetric < const ObjectiveVector &, R > {};
/**
* Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of objective vectors)
*/
template < class ObjectiveVector, class R >
class moeoVectorUnaryMetric : public moeoUnaryMetric < const std::vector < ObjectiveVector > &, R > {};
/**
* Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors.
*/
template < class ObjectiveVector, class R >
class moeoSolutionVsSolutionBinaryMetric : public moeoBinaryMetric < const ObjectiveVector &, const ObjectiveVector &, R > {};
/**
* Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of objective vectors)
*/
template < class ObjectiveVector, class R >
class moeoVectorVsVectorBinaryMetric : public moeoBinaryMetric < const std::vector < ObjectiveVector > &, const std::vector < ObjectiveVector > &, R > {};
#endif /*MOEOMETRIC_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoNormalizedSolutionVsSolutionBinaryMetric.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_
#define MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_
#include <vector>
#include <utils/eoRealBounds.h>
#include <metric/moeoMetric.h>
/**
* Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors using normalized values.
* Then, indicator values lie in the interval [-1,1].
* Note that you have to set the bounds for every objective before using the operator().
*/
template < class ObjectiveVector, class R >
class moeoNormalizedSolutionVsSolutionBinaryMetric : public moeoSolutionVsSolutionBinaryMetric < ObjectiveVector, R >
{
public:
/**
* Default ctr for any moeoNormalizedSolutionVsSolutionBinaryMetric object
*/
moeoNormalizedSolutionVsSolutionBinaryMetric()
{
bounds.resize(ObjectiveVector::Traits::nObjectives());
// initialize bounds in case someone does not want to use them
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
bounds[i] = eoRealInterval(0,1);
}
}
/**
* Sets the lower bound (_min) and the upper bound (_max) for the objective _obj
* @param _min lower bound
* @param _max upper bound
* @param _obj the objective index
*/
void setup(double _min, double _max, unsigned int _obj)
{
if (_min == _max)
{
_min -= tiny();
_max += tiny();
}
bounds[_obj] = eoRealInterval(_min, _max);
}
/**
* Sets the lower bound and the upper bound for the objective _obj using a eoRealInterval object
* @param _realInterval the eoRealInterval object
* @param _obj the objective index
*/
virtual void setup(eoRealInterval _realInterval, unsigned int _obj)
{
bounds[_obj] = _realInterval;
}
/**
* Returns a very small value that can be used to avoid extreme cases (where the min bound == the max bound)
*/
static double tiny()
{
return 1e-6;
}
protected:
/** the bounds for every objective (bounds[i] = bounds for the objective i) */
std::vector < eoRealInterval > bounds;
};
#endif /*MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeo
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEO_
#define MOEO_
#include <eo>
#include <algo/moeoAlgo.h>
#include <algo/moeoCombinedLS.h>
#include <algo/moeoEA.h>
#include <algo/moeoEasyEA.h>
#include <algo/moeoHybridLS.h>
#include <algo/moeoIBEA.h>
//#include <algo/moeoIBMOLS.h>
//#include <algo/moeoIteratedIBMOLS.h>
#include <algo/moeoLS.h>
#include <algo/moeoNSGA.h>
#include <algo/moeoNSGAII.h>
#include <archive/moeoArchive.h>
#include <comparator/moeoAggregativeComparator.h>
#include <comparator/moeoComparator.h>
#include <comparator/moeoDiversityThenFitnessComparator.h>
#include <comparator/moeoFitnessThenDiversityComparator.h>
#include <comparator/moeoGDominanceObjectiveVectorComparator.h>
#include <comparator/moeoObjectiveVectorComparator.h>
#include <comparator/moeoObjectiveObjectiveVectorComparator.h>
#include <comparator/moeoOneObjectiveComparator.h>
#include <comparator/moeoParetoObjectiveVectorComparator.h>
#include <core/MOEO.h>
#include <core/moeoBitVector.h>
#include <core/moeoEvalFunc.h>
#include <core/moeoObjectiveVector.h>
#include <core/moeoObjectiveVectorDouble.h>
#include <core/moeoObjectiveVectorTraits.h>
#include <core/moeoBitVector.h>
#include <core/moeoRealVector.h>
#include <distance/moeoDistance.h>
#include <distance/moeoDistanceMatrix.h>
#include <distance/moeoEuclideanDistance.h>
#include <distance/moeoManhattanDistance.h>
#include <distance/moeoNormalizedDistance.h>
#include <diversity/moeoCrowdingDistanceDiversityAssignment.h>
#include <diversity/moeoDiversityAssignment.h>
#include <diversity/moeoDummyDiversityAssignment.h>
#include <diversity/moeoFrontByFrontCrowdingDistanceDiversityAssignment.h>
#include <diversity/moeoFrontByFrontSharingDiversityAssignment.h>
#include <diversity/moeoSharingDiversityAssignment.h>
#include <fitness/moeoAchievementFitnessAssignment.h>
#include <fitness/moeoCriterionBasedFitnessAssignment.h>
#include <fitness/moeoDummyFitnessAssignment.h>
#include <fitness/moeoFastNonDominatedSortingFitnessAssignment.h>
#include <fitness/moeoFitnessAssignment.h>
#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
#include <fitness/moeoParetoBasedFitnessAssignment.h>
//#include <fitness/moeoReferencePointIndicatorBasedFitnessAssignment.h>
#include <fitness/moeoScalarFitnessAssignment.h>
#include <metric/moeoAdditiveEpsilonBinaryMetric.h>
#include <metric/moeoContributionMetric.h>
#include <metric/moeoEntropyMetric.h>
#include <metric/moeoHypervolumeBinaryMetric.h>
#include <metric/moeoMetric.h>
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
#include <replacement/moeoElitistReplacement.h>
#include <replacement/moeoEnvironmentalReplacement.h>
#include <replacement/moeoGenerationalReplacement.h>
#include <replacement/moeoReplacement.h>
#include <selection/moeoDetTournamentSelect.h>
#include <selection/moeoRandomSelect.h>
#include <selection/moeoRouletteSelect.h>
#include <selection/moeoSelectFromPopAndArch.h>
#include <selection/moeoSelectOne.h>
#include <selection/moeoSelectors.h>
#include <selection/moeoStochTournamentSelect.h>
#include <utils/moeoArchiveObjectiveVectorSavingUpdater.h>
#include <utils/moeoArchiveUpdater.h>
#include <utils/moeoBinaryMetricSavingUpdater.h>
#include <utils/moeoConvertPopToObjectiveVectors.h>
#endif /*MOEO_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
#ifndef _MOEOMOVEINCREVAL_H
#define _MOEOMOVEINCREVAL_H
#include <eoFunctor.h>
template < class Move >
class moeoMoveIncrEval : public eoBF < const Move &, const typename Move::EOType &, typename Move::EOType::ObjectiveVector > {};
#endif

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoElitistReplacement.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOELITISTREPLACEMENT_H_
#define MOEOELITISTREPLACEMENT_H_
#include <comparator/moeoComparator.h>
#include <comparator/moeoFitnessThenDiversityComparator.h>
#include <diversity/moeoDiversityAssignment.h>
#include <diversity/moeoDummyDiversityAssignment.h>
#include <fitness/moeoFitnessAssignment.h>
#include <replacement/moeoReplacement.h>
/**
* Elitist replacement strategy that consists in keeping the N best individuals.
*/
template < class MOEOT > class moeoElitistReplacement:public moeoReplacement < MOEOT >
{
public:
/**
* Full constructor.
* @param _fitnessAssignment the fitness assignment strategy
* @param _diversityAssignment the diversity assignment strategy
* @param _comparator the comparator (used to compare 2 individuals)
*/
moeoElitistReplacement (moeoFitnessAssignment < MOEOT > & _fitnessAssignment, moeoDiversityAssignment < MOEOT > & _diversityAssignment, moeoComparator < MOEOT > & _comparator) :
fitnessAssignment (_fitnessAssignment), diversityAssignment (_diversityAssignment), comparator (_comparator)
{}
/**
* Constructor without comparator. A moeoFitThenDivComparator is used as default.
* @param _fitnessAssignment the fitness assignment strategy
* @param _diversityAssignment the diversity assignment strategy
*/
moeoElitistReplacement (moeoFitnessAssignment < MOEOT > & _fitnessAssignment, moeoDiversityAssignment < MOEOT > & _diversityAssignment) :
fitnessAssignment (_fitnessAssignment), diversityAssignment (_diversityAssignment), comparator (defaultComparator)
{}
/**
* Constructor without moeoDiversityAssignement. A dummy diversity is used as default.
* @param _fitnessAssignment the fitness assignment strategy
* @param _comparator the comparator (used to compare 2 individuals)
*/
moeoElitistReplacement (moeoFitnessAssignment < MOEOT > & _fitnessAssignment, moeoComparator < MOEOT > & _comparator) :
fitnessAssignment (_fitnessAssignment), diversityAssignment (defaultDiversity), comparator (_comparator)
{}
/**
* Constructor without moeoDiversityAssignement nor moeoComparator.
* A moeoFitThenDivComparator and a dummy diversity are used as default.
* @param _fitnessAssignment the fitness assignment strategy
*/
moeoElitistReplacement (moeoFitnessAssignment < MOEOT > & _fitnessAssignment) :
fitnessAssignment (_fitnessAssignment), diversityAssignment (defaultDiversity), comparator (defaultComparator)
{}
/**
* Replaces the first population by adding the individuals of the second one, sorting with a moeoComparator and resizing the whole population obtained.
* @param _parents the population composed of the parents (the population you want to replace)
* @param _offspring the offspring population
*/
void operator () (eoPop < MOEOT > &_parents, eoPop < MOEOT > &_offspring)
{
unsigned int sz = _parents.size ();
// merges offspring and parents into a global population
_parents.reserve (_parents.size () + _offspring.size ());
std::copy (_offspring.begin (), _offspring.end (), back_inserter (_parents));
// evaluates the fitness and the diversity of this global population
fitnessAssignment (_parents);
diversityAssignment (_parents);
// sorts the whole population according to the comparator
std::sort(_parents.begin(), _parents.end(), comparator);
// finally, resize this global population
_parents.resize (sz);
// and clear the offspring population
_offspring.clear ();
}
protected:
/** the fitness assignment strategy */
moeoFitnessAssignment < MOEOT > & fitnessAssignment;
/** the diversity assignment strategy */
moeoDiversityAssignment < MOEOT > & diversityAssignment;
/** a dummy diversity assignment can be used as default */
moeoDummyDiversityAssignment < MOEOT > defaultDiversity;
/** a fitness then diversity comparator can be used as default */
moeoFitnessThenDiversityComparator < MOEOT > defaultComparator;
/** this object is used to compare solutions in order to sort the population */
class Cmp
{
public:
/**
* Ctor.
* @param _comp the comparator
*/
Cmp(moeoComparator < MOEOT > & _comp) : comp(_comp)
{}
/**
* Returns true if _moeo1 is greater than _moeo2 according to the comparator
* _moeo1 the first individual
* _moeo2 the first individual
*/
bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
return comp(_moeo2,_moeo1);
}
private:
/** the comparator */
moeoComparator < MOEOT > & comp;
} comparator;
};
#endif /*MOEOELITISTREPLACEMENT_H_ */

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoEnvironmentalReplacement.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOENVIRONMENTALREPLACEMENT_H_
#define MOEOENVIRONMENTALREPLACEMENT_H_
#include <comparator/moeoComparator.h>
#include <comparator/moeoFitnessThenDiversityComparator.h>
#include <diversity/moeoDiversityAssignment.h>
#include <fitness/moeoFitnessAssignment.h>
#include <replacement/moeoReplacement.h>
/**
* Environmental replacement strategy that consists in keeping the N best individuals by deleting individuals 1 by 1
* and by updating the fitness and diversity values after each deletion.
*/
template < class MOEOT > class moeoEnvironmentalReplacement:public moeoReplacement < MOEOT >
{
public:
/** The type for objective vector */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Full constructor.
* @param _fitnessAssignment the fitness assignment strategy
* @param _diversityAssignment the diversity assignment strategy
* @param _comparator the comparator (used to compare 2 individuals)
*/
moeoEnvironmentalReplacement (moeoFitnessAssignment < MOEOT > & _fitnessAssignment, moeoDiversityAssignment < MOEOT > & _diversityAssignment, moeoComparator < MOEOT > & _comparator) :
fitnessAssignment (_fitnessAssignment), diversityAssignment (_diversityAssignment), comparator (_comparator)
{}
/**
* Constructor without comparator. A moeoFitThenDivComparator is used as default.
* @param _fitnessAssignment the fitness assignment strategy
* @param _diversityAssignment the diversity assignment strategy
*/
moeoEnvironmentalReplacement (moeoFitnessAssignment < MOEOT > & _fitnessAssignment, moeoDiversityAssignment < MOEOT > & _diversityAssignment) :
fitnessAssignment (_fitnessAssignment), diversityAssignment (_diversityAssignment), comparator (defaultComparator)
{}
/**
* Constructor without moeoDiversityAssignement. A dummy diversity is used as default.
* @param _fitnessAssignment the fitness assignment strategy
* @param _comparator the comparator (used to compare 2 individuals)
*/
moeoEnvironmentalReplacement (moeoFitnessAssignment < MOEOT > & _fitnessAssignment, moeoComparator < MOEOT > & _comparator) :
fitnessAssignment (_fitnessAssignment), diversityAssignment (defaultDiversity), comparator (_comparator)
{}
/**
* Constructor without moeoDiversityAssignement nor moeoComparator.
* A moeoFitThenDivComparator and a dummy diversity are used as default.
* @param _fitnessAssignment the fitness assignment strategy
*/
moeoEnvironmentalReplacement (moeoFitnessAssignment < MOEOT > & _fitnessAssignment) :
fitnessAssignment (_fitnessAssignment), diversityAssignment (defaultDiversity), comparator (defaultComparator)
{}
/**
* Replaces the first population by adding the individuals of the second one, sorting with a moeoComparator and resizing the whole population obtained.
* @param _parents the population composed of the parents (the population you want to replace)
* @param _offspring the offspring population
*/
void operator () (eoPop < MOEOT > &_parents, eoPop < MOEOT > &_offspring)
{
unsigned int sz = _parents.size();
// merges offspring and parents into a global population
_parents.reserve (_parents.size() + _offspring.size());
std::copy (_offspring.begin(), _offspring.end(), back_inserter(_parents));
// evaluates the fitness and the diversity of this global population
fitnessAssignment (_parents);
diversityAssignment (_parents);
// remove individuals 1 by 1 and update the fitness values
unsigned int worstIdx;
ObjectiveVector worstObjVec;
while (_parents.size() > sz)
{
// the individual to delete
worstIdx = std::min_element(_parents.begin(), _parents.end(), comparator) - _parents.begin();
worstObjVec = _parents[worstIdx].objectiveVector();
// remove the woorst individual
_parents[worstIdx] = _parents.back();
_parents.pop_back();
// update of the fitness and diversity values
fitnessAssignment.updateByDeleting(_parents, worstObjVec);
diversityAssignment.updateByDeleting(_parents, worstObjVec);
}
// clear the offspring population
_offspring.clear ();
}
protected:
/** the fitness assignment strategy */
moeoFitnessAssignment < MOEOT > & fitnessAssignment;
/** the diversity assignment strategy */
moeoDiversityAssignment < MOEOT > & diversityAssignment;
/** a dummy diversity assignment can be used as default */
moeoDummyDiversityAssignment < MOEOT > defaultDiversity;
/** a fitness then diversity comparator can be used as default */
moeoFitnessThenDiversityComparator < MOEOT > defaultComparator;
/** this object is used to compare solutions in order to sort the population */
class Cmp
{
public:
/**
* Ctor.
* @param _comp the comparator
*/
Cmp(moeoComparator < MOEOT > & _comp) : comp(_comp)
{}
/**
* Returns true if _moeo1 is greater than _moeo2 according to the comparator
* _moeo1 the first individual
* _moeo2 the first individual
*/
bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
return comp(_moeo1,_moeo2);
}
private:
/** the comparator */
moeoComparator < MOEOT > & comp;
} comparator;
};
#endif /*MOEOENVIRONMENTALREPLACEMENT_H_ */

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoGenerationalReplacement.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOGENERATIONALREPLACEMENT_H_
#define MOEOGENERATIONALREPLACEMENT_H_
#include <eoReplacement.h>
#include <replacement/moeoReplacement.h>
/**
* Generational replacement: only the new individuals are preserved.
*/
template < class MOEOT >
class moeoGenerationalReplacement : public moeoReplacement < MOEOT >, public eoGenerationalReplacement < MOEOT >
{
public:
/**
* Swaps _parents and _offspring
* @param _parents the parents population
* @param _offspring the offspring population
*/
void operator()(eoPop < MOEOT > & _parents, eoPop < MOEOT > & _offspring)
{
eoGenerationalReplacement < MOEOT >::operator ()(_parents, _offspring);
}
};
#endif /*MOEOGENERATIONALREPLACEMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoReplacement.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOREPLACEMENT_H_
#define MOEOREPLACEMENT_H_
#include <eoReplacement.h>
/**
* Replacement strategy for multi-objective optimization.
*/
template < class MOEOT >
class moeoReplacement : public eoReplacement < MOEOT > {};
#endif /*MOEOREPLACEMENT_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoDetTournamentSelect.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEODETTOURNAMENTSELECT_H_
#define MOEODETTOURNAMENTSELECT_H_
#include <comparator/moeoComparator.h>
#include <comparator/moeoFitnessThenDiversityComparator.h>
#include <selection/moeoSelectOne.h>
#include <selection/moeoSelectors.h>
/**
* Selection strategy that selects ONE individual by deterministic tournament.
*/
template < class MOEOT > class moeoDetTournamentSelect:public moeoSelectOne < MOEOT >
{
public:
/**
* Full Ctor.
* @param _comparator the comparator (used to compare 2 individuals)
* @param _tSize the number of individuals in the tournament (default: 2)
*/
moeoDetTournamentSelect (moeoComparator < MOEOT > & _comparator, unsigned int _tSize = 2) : comparator (_comparator), tSize (_tSize)
{
// consistency check
if (tSize < 2)
{
std::
cout << "Warning, Tournament size should be >= 2\nAdjusted to 2\n";
tSize = 2;
}
}
/**
* Ctor without comparator. A moeoFitnessThenDiversityComparator is used as default.
* @param _tSize the number of individuals in the tournament (default: 2)
*/
moeoDetTournamentSelect (unsigned int _tSize = 2) : comparator (defaultComparator), tSize (_tSize)
{
// consistency check
if (tSize < 2)
{
std::
cout << "Warning, Tournament size should be >= 2\nAdjusted to 2\n";
tSize = 2;
}
}
/**
* Apply the tournament to the given population
* @param _pop the population
*/
const MOEOT & operator() (const eoPop < MOEOT > &_pop)
{
// use the selector
return mo_deterministic_tournament (_pop, tSize, comparator);
}
protected:
/** the comparator (used to compare 2 individuals) */
moeoComparator < MOEOT > & comparator;
/** a fitness then diversity comparator can be used as default */
moeoFitnessThenDiversityComparator < MOEOT > defaultComparator;
/** the number of individuals in the tournament */
unsigned int tSize;
};
#endif /*MOEODETTOURNAMENTSELECT_H_ */

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoRandomSelect.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEORANDOMSELECT_H_
#define MOEORANDOMSELECT_H_
#include <eoRandomSelect.h>
#include <selection/moeoSelectOne.h>
/**
* Selection strategy that selects only one element randomly from a whole population.
*/
template < class MOEOT > class moeoRandomSelect:public moeoSelectOne < MOEOT >, public eoRandomSelect <MOEOT >
{
public:
/**
* Ctor.
*/
moeoRandomSelect(){}
/**
* Return one individual at random by using an eoRandomSelect.
*/
const MOEOT & operator () (const eoPop < MOEOT > &_pop)
{
return eoRandomSelect < MOEOT >::operator ()(_pop);
}
};
#endif /*MOEORANDOMSELECT_H_ */

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoRouletteSelect.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOROULETTESELECT_H_
#define MOEOROULETTESELECT_H_
#include <selection/moeoSelectOne.h>
#include <selection/moeoSelectors.h>
/**
* Selection strategy that selects ONE individual by using roulette wheel process.
* @WARNING This selection only uses fitness values (and not diversity values).
*/
template < class MOEOT >
class moeoRouletteSelect:public moeoSelectOne < MOEOT >
{
public:
/**
* Ctor.
* @param _tSize the number of individuals in the tournament (default: 2)
*/
moeoRouletteSelect (unsigned int _tSize = 2) : tSize (_tSize)
{
// consistency check
if (tSize < 2)
{
std::
cout << "Warning, Tournament size should be >= 2\nAdjusted to 2\n";
tSize = 2;
}
}
/**
* Apply the tournament to the given population
* @param _pop the population
*/
const MOEOT & operator () (const eoPop < MOEOT > & _pop)
{
// use the selector
return mo_roulette_wheel(_pop,tSize);
}
protected:
/** size */
double & tSize;
};
#endif /*MOEOROULETTESELECT_H_ */

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoSelectFormPopAndArch.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOSELECTONEFROMPOPANDARCH_H_
#define MOEOSELECTONEFROMPOPANDARCH_H_
#include <eoPop.h>
#include <utils/eoRNG.h>
#include <archive/moeoArchive.h>
#include <selection/moeoSelectOne.h>
#include <selection/moeoRandomSelect.h>
/**
* Elitist selection process that consists in choosing individuals in the archive as well as in the current population.
*/
template < class MOEOT >
class moeoSelectFromPopAndArch : public moeoSelectOne < MOEOT >
{
public:
/**
* Ctor
* @param _popSelectOne the population's selection operator
* @param _archSelectOne the archive's selection operator
* @param _arch the archive
* @param _ratioFromPop the ratio of selected individuals from the population
*/
moeoSelectFromPopAndArch (moeoSelectOne < MOEOT > & _popSelectOne, moeoSelectOne < MOEOT > _archSelectOne, moeoArchive < MOEOT > & _arch, double _ratioFromPop=0.5)
: popSelectOne(_popSelectOne), archSelectOne(_archSelectOne), arch(_arch), ratioFromPop(_ratioFromPop)
{}
/**
* Defaulr ctor - the archive's selection operator is a random selector
* @param _popSelectOne the population's selection operator
* @param _arch the archive
* @param _ratioFromPop the ratio of selected individuals from the population
*/
moeoSelectFromPopAndArch (moeoSelectOne < MOEOT > & _popSelectOne, moeoArchive < MOEOT > & _arch, double _ratioFromPop=0.5)
: popSelectOne(_popSelectOne), archSelectOne(randomSelectOne), arch(_arch), ratioFromPop(_ratioFromPop)
{}
/**
* The selection process
*/
virtual const MOEOT & operator () (const eoPop < MOEOT > & pop)
{
if (arch.size() > 0)
if (rng.flip(ratioFromPop))
return popSelectOne(pop);
else
return archSelectOne(arch);
else
return popSelectOne(pop);
}
/**
* Setups some population stats
*/
virtual void setup (const eoPop < MOEOT > & _pop)
{
popSelectOne.setup(_pop);
}
private:
/** The population's selection operator */
moeoSelectOne < MOEOT > & popSelectOne;
/** The archive's selection operator */
moeoSelectOne < MOEOT > & archSelectOne;
/** The archive */
moeoArchive < MOEOT > & arch;
/** The ratio of selected individuals from the population*/
double ratioFromPop;
/** A random selection operator (used as default for archSelectOne) */
moeoRandomSelect < MOEOT > randomSelectOne;
};
#endif /*MOEOSELECTONEFROMPOPANDARCH_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoSelectOne.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOSELECTONE_H_
#define MOEOSELECTONE_H_
#include <eoSelectOne.h>
/**
* Selection strategy for multi-objective optimization that selects only one element from a whole population.
*/
template < class MOEOT >
class moeoSelectOne : public eoSelectOne < MOEOT > {};
#endif /*MOEOSELECTONE_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoSelectors.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOSELECTORS_H_
#define MOEOSELECTORS_H_
#include <comparator/moeoComparator.h>
template <class It,class MOEOT>
It mo_deterministic_tournament(It _begin, It _end, unsigned int _t_size,moeoComparator<MOEOT>& _comparator ,eoRng& _gen = rng)
{
It best = _begin + _gen.random(_end - _begin);
for (unsigned int i = 0; i < _t_size - 1; ++i)
{
It competitor = _begin + _gen.random(_end - _begin);
// compare the two individuals by using the comparator
if (_comparator(*best, *competitor))
// best "better" than competitor
best=competitor;
}
return best;
}
template <class MOEOT>
const MOEOT& mo_deterministic_tournament(const eoPop<MOEOT>& _pop, unsigned int _t_size,moeoComparator<MOEOT>& _comparator, eoRng& _gen = rng)
{
return *mo_deterministic_tournament(_pop.begin(), _pop.end(),_t_size,_comparator, _gen);
}
template <class MOEOT>
MOEOT& mo_deterministic_tournament(eoPop<MOEOT>& _pop, unsigned int _t_size,moeoComparator<MOEOT>& _comparator,eoRng& _gen = rng)
{
return *mo_deterministic_tournament(_pop.begin(), _pop.end(), _t_size,_comparator, _gen);
}
template <class It,class MOEOT>
It mo_stochastic_tournament(It _begin, It _end, double _t_rate,moeoComparator<MOEOT>& _comparator ,eoRng& _gen = rng)
{
It i1 = _begin + _gen.random(_end - _begin);
It i2 = _begin + _gen.random(_end - _begin);
bool return_better = _gen.flip(_t_rate);
if (_comparator(*i1, *i2))
{
if (return_better) return i2;
// else
return i1;
}
else
{
if (return_better) return i1;
// else
}
// else
return i2;
}
template <class MOEOT>
const MOEOT& mo_stochastic_tournament(const eoPop<MOEOT>& _pop, double _t_rate,moeoComparator<MOEOT>& _comparator, eoRng& _gen = rng)
{
return *mo_stochastic_tournament(_pop.begin(), _pop.end(), _t_rate,_comparator, _gen);
}
template <class MOEOT>
MOEOT& mo_stochastic_tournament(eoPop<MOEOT>& _pop, double _t_rate, eoRng& _gen = rng)
{
return *mo_stochastic_tournament(_pop.begin(), _pop.end(), _t_rate, _gen);
}
template <class It>
It mo_roulette_wheel(It _begin, It _end, double total, eoRng& _gen = rng)
{
float roulette = _gen.uniform(total);
if (roulette == 0.0) // covers the case where total==0.0
return _begin + _gen.random(_end - _begin); // uniform choice
It i = _begin;
while (roulette > 0.0)
{
roulette -= static_cast<double>(*(i++));
}
return --i;
}
template <class MOEOT>
const MOEOT& mo_roulette_wheel(const eoPop<MOEOT>& _pop, double total, eoRng& _gen = rng)
{
float roulette = _gen.uniform(total);
if (roulette == 0.0) // covers the case where total==0.0
return _pop[_gen.random(_pop.size())]; // uniform choice
typename eoPop<MOEOT>::const_iterator i = _pop.begin();
while (roulette > 0.0)
{
roulette -= static_cast<double>((i++)->fitness());
}
return *--i;
}
template <class MOEOT>
MOEOT& mo_roulette_wheel(eoPop<MOEOT>& _pop, double total, eoRng& _gen = rng)
{
float roulette = _gen.uniform(total);
if (roulette == 0.0) // covers the case where total==0.0
return _pop[_gen.random(_pop.size())]; // uniform choice
typename eoPop<MOEOT>::iterator i = _pop.begin();
while (roulette > 0.0)
{
// fitness only
roulette -= static_cast<double>((i++)->fitness());
}
return *--i;
}
#endif /*MOEOSELECTORS_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoStochTournamentSelect.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOSTOCHTOURNAMENTSELECT_H_
#define MOEOSTOCHTOURNAMENTSELECT_H_
#include <comparator/moeoComparator.h>
#include <comparator/moeoFitnessThenDiversityComparator.h>
#include <selection/moeoSelectOne.h>
#include <selection/moeoSelectors.h>
/**
* Selection strategy that selects ONE individual by stochastic tournament.
*/
template < class MOEOT > class moeoStochTournamentSelect:public moeoSelectOne <MOEOT>
{
public:
/**
* Full Ctor
* @param _comparator the comparator (used to compare 2 individuals)
* @param _tRate the tournament rate
*/
moeoStochTournamentSelect (moeoComparator < MOEOT > & _comparator, double _tRate = 1.0) : comparator (_comparator), tRate (_tRate)
{
// consistency checks
if (tRate < 0.5)
{
std::cerr << "Warning, Tournament rate should be > 0.5\nAdjusted to 0.55\n";
tRate = 0.55;
}
if (tRate > 1)
{
std::cerr << "Warning, Tournament rate should be < 1\nAdjusted to 1\n";
tRate = 1;
}
}
/**
* Ctor without comparator. A moeoFitnessThenDiversityComparator is used as default.
* @param _tRate the tournament rate
*/
moeoStochTournamentSelect (double _tRate = 1.0) : comparator (defaultComparator), tRate (_tRate)
{
// consistency checks
if (tRate < 0.5)
{
std::cerr << "Warning, Tournament rate should be > 0.5\nAdjusted to 0.55\n";
tRate = 0.55;
}
if (tRate > 1)
{
std::cerr << "Warning, Tournament rate should be < 1\nAdjusted to 1\n";
tRate = 1;
}
}
/**
* Apply the tournament to the given population
* @param _pop the population
*/
const MOEOT & operator() (const eoPop < MOEOT > &_pop)
{
// use the selector
return mo_stochastic_tournament(_pop,tRate,comparator);
}
protected:
/** the comparator (used to compare 2 individuals) */
moeoComparator < MOEOT > & comparator;
/** a fitness then diversity comparator can be used as default */
moeoFitnessThenDiversityComparator < MOEOT > defaultComparator;
/** the tournament rate */
double tRate;
};
#endif /*MOEOSTOCHTOURNAMENTSELECT_H_ */

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoArchiveObjectiveVectorSavingUpdater.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOARCHIVEOBJECTIVEVECTORSAVINGUPDATER_H_
#define MOEOARCHIVEOBJECTIVEVECTORSAVINGUPDATER_H_
#include <fstream>
#include <string>
#include <eoPop.h>
#include <utils/eoUpdater.h>
#include <archive/moeoArchive.h>
#define MAX_BUFFER_SIZE 1000
/**
* This class allows to save the objective vectors of the solutions contained in an archive into a file at each generation.
*/
template < class MOEOT >
class moeoArchiveObjectiveVectorSavingUpdater : public eoUpdater
{
public:
/**
* Ctor
* @param _arch local archive
* @param _filename target filename
* @param _count put this variable to true if you want a new file to be created each time () is called and to false if you only want the file to be updated
* @param _id own ID
*/
moeoArchiveObjectiveVectorSavingUpdater (moeoArchive<MOEOT> & _arch, const std::string & _filename, bool _count = false, int _id = -1) :
arch(_arch), filename(_filename), count(_count), counter(0), id(_id)
{}
/**
* Saves the fitness of the archive's members into the file
*/
void operator()() {
char buff[MAX_BUFFER_SIZE];
if (count)
{
if (id == -1)
{
sprintf (buff, "%s.%u", filename.c_str(), counter ++);
}
else
{
sprintf (buff, "%s.%u.%u", filename.c_str(), id, counter ++);
}
}
else
{
if (id == -1)
{
sprintf (buff, "%s", filename.c_str());
}
else
{
sprintf (buff, "%s.%u", filename.c_str(), id);
}
counter ++;
}
std::ofstream f(buff);
for (unsigned int i = 0; i < arch.size (); i++)
f << arch[i].objectiveVector() << std::endl;
f.close ();
}
private:
/** local archive */
moeoArchive<MOEOT> & arch;
/** target filename */
std::string filename;
/** this variable is set to true if a new file have to be created each time () is called and to false if the file only HAVE to be updated */
bool count;
/** counter */
unsigned int counter;
/** own ID */
int id;
};
#endif /*MOEOARCHIVEOBJECTIVEVECTORSAVINGUPDATER_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoArchiveUpdater.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOARCHIVEUPDATER_H_
#define MOEOARCHIVEUPDATER_H_
#include <eoPop.h>
#include <utils/eoUpdater.h>
#include <archive/moeoArchive.h>
/**
* This class allows to update the archive at each generation with newly found non-dominated solutions.
*/
template < class MOEOT >
class moeoArchiveUpdater : public eoUpdater
{
public:
/**
* Ctor
* @param _arch an archive of non-dominated solutions
* @param _pop the main population
*/
moeoArchiveUpdater(moeoArchive < MOEOT > & _arch, const eoPop < MOEOT > & _pop) : arch(_arch), pop(_pop)
{}
/**
* Updates the archive with newly found non-dominated solutions contained in the main population
*/
void operator()() {
arch.update(pop);
}
private:
/** the archive of non-dominated solutions */
moeoArchive < MOEOT > & arch;
/** the main population */
const eoPop < MOEOT > & pop;
};
#endif /*MOEOARCHIVEUPDATER_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoBinaryMetricSavingUpdater.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOBINARYMETRICSAVINGUPDATER_H_
#define MOEOBINARYMETRICSAVINGUPDATER_H_
#include <fstream>
#include <string>
#include <vector>
#include <eoPop.h>
#include <utils/eoUpdater.h>
#include <metric/moeoMetric.h>
/**
* This class allows to save the progression of a binary metric comparing the objective vectors of the current population (or archive)
* with the objective vectors of the population (or archive) of the generation (n-1) into a file
*/
template < class MOEOT >
class moeoBinaryMetricSavingUpdater : public eoUpdater
{
public:
/** The objective vector type of a solution */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Ctor
* @param _metric the binary metric comparing two Pareto sets
* @param _pop the main population
* @param _filename the target filename
*/
moeoBinaryMetricSavingUpdater (moeoVectorVsVectorBinaryMetric < ObjectiveVector, double > & _metric, const eoPop < MOEOT > & _pop, std::string _filename) :
metric(_metric), pop(_pop), filename(_filename), counter(1)
{}
/**
* Saves the metric's value for the current generation
*/
void operator()() {
if (pop.size()) {
if (firstGen) {
firstGen = false;
}
else {
// creation of the two Pareto sets
std::vector < ObjectiveVector > from;
std::vector < ObjectiveVector > to;
for (unsigned int i=0; i<pop.size(); i++)
from.push_back(pop[i].objectiveVector());
for (unsigned int i=0 ; i<oldPop.size(); i++)
to.push_back(oldPop[i].objectiveVector());
// writing the result into the file
std::ofstream f (filename.c_str(), std::ios::app);
f << counter++ << ' ' << metric(from,to) << std::endl;
f.close();
}
oldPop = pop;
}
}
private:
/** binary metric comparing two Pareto sets */
moeoVectorVsVectorBinaryMetric < ObjectiveVector, double > & metric;
/** main population */
const eoPop < MOEOT > & pop;
/** (n-1) population */
eoPop< MOEOT > oldPop;
/** target filename */
std::string filename;
/** is it the first generation ? */
bool firstGen;
/** counter */
unsigned int counter;
};
#endif /*MOEOBINARYMETRICSAVINGUPDATER_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// moeoConvertPopToObjectiveVectors.h
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
#ifndef MOEOPOPTOOBJECTIVEVECTORS_H_
#define MOEOPOPTOOBJECTIVEVECTORS_H_
#include <vector>
#include <eoFunctor.h>
/**
* Functor allowing to get a vector of objective vectors from a population
*/
template < class MOEOT, class ObjectiveVector = typename MOEOT::ObjectiveVector >
class moeoConvertPopToObjectiveVectors : public eoUF < const eoPop < MOEOT >, const std::vector < ObjectiveVector > >
{
public:
/**
* Returns a vector of the objective vectors from the population _pop
* @param _pop the population
*/
const std::vector < ObjectiveVector > operator()(const eoPop < MOEOT > _pop)
{
std::vector < ObjectiveVector > result;
result.resize(_pop.size());
for (unsigned int i=0; i<_pop.size(); i++)
{
result.push_back(_pop[i].objectiveVector());
}
return result;
}
};
#endif /*MOEOPOPTOOBJECTIVEVECTORS_H_*/

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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// FlowShopEA.cpp
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
/*
This library...
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
*/
//-----------------------------------------------------------------------------
// moeo general include
#include <moeo>
// for the creation of an evaluator
#include <make_eval_FlowShop.h>
// for the creation of an initializer
#include <make_genotype_FlowShop.h>
// for the creation of the variation operators
#include <make_op_FlowShop.h>
// how to initialize the population
#include <do/make_pop.h>
// the stopping criterion
#include <do/make_continue_moeo.h>
// outputs (stats, population dumps, ...)
#include <do/make_checkpoint_moeo.h>
// evolution engine (selection and replacement)
#include <do/make_ea_moeo.h>
// simple call to the algo
#include <do/make_run.h>
// checks for help demand, and writes the status file and make_help; in libutils
void make_help(eoParser & _parser);
// definition of the representation
#include <FlowShop.h>
using namespace std;
int main(int argc, char* argv[])
{
try
{
eoParser parser(argc, argv); // for user-parameter reading
eoState state; // to keep all things allocated
/*** the representation-dependent things ***/
// The fitness evaluation
eoEvalFuncCounter<FlowShop>& eval = do_make_eval(parser, state);
// the genotype (through a genotype initializer)
eoInit<FlowShop>& init = do_make_genotype(parser, state);
// the variation operators
eoGenOp<FlowShop>& op = do_make_op(parser, state);
/*** the representation-independent things ***/
// initialization of the population
eoPop<FlowShop>& pop = do_make_pop(parser, state, init);
// definition of the archive
moeoArchive<FlowShop> arch;
// stopping criteria
eoContinue<FlowShop>& term = do_make_continue_moeo(parser, state, eval);
// output
eoCheckPoint<FlowShop>& checkpoint = do_make_checkpoint_moeo(parser, state, eval, term, pop, arch);
// algorithm
eoAlgo<FlowShop>& algo = do_make_ea_moeo(parser, state, eval, checkpoint, op, arch);
/*** Go ! ***/
// help ?
make_help(parser);
// first evalution
apply<FlowShop>(eval, pop);
// printing of the initial population
cout << "Initial Population\n";
pop.sortedPrintOn(cout);
cout << endl;
// run the algo
do_run(algo, pop);
// printing of the final population
cout << "Final Population\n";
pop.sortedPrintOn(cout);
cout << endl;
// printing of the final archive
cout << "Final Archive\n";
arch.sortedPrintOn(cout);
cout << endl;
}
catch (exception& e)
{
cout << e.what() << endl;
}
return EXIT_SUCCESS;
}

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###### General ######
--help=0 # -h : Prints this message
--stopOnUnknownParam=1 # Stop if unkown param entered
# --seed=1182849833 # -S : Random number seed
###### Evolution Engine ######
--popSize=20 # -P : Population Size
--updateArch=1 # Update the archive at each gen.
--fitness=FastNonDominatedSorting # -F : Fitness assignment scheme: Dummy, FastNonDominatedSorting or IndicatorBased
--indicator=Epsilon # -i : Binary indicator for IndicatorBased: Epsilon, Hypervolume
--rho=1.1 # -r : reference point for the hypervolume indicator
--kappa=0.05 # -k : Scaling factor kappa for IndicatorBased
--diversity=Dummy # -D : Diversity assignment scheme: Dummy, Sharing(nicheSize) or Crowding
--comparator=FitnessThenDiversity # -C : Comparator scheme: FitnessThenDiversity, DiversityThenFitness or Aggregative
--selection=DetTour(2) # -S : Selection scheme: DetTour(T), StochTour(t) or Random
--replacement=Elitist # -R : Replacement scheme: Elitist, Environmental or Generational
--nbOffspring=100% # -O : Number of offspring (percentage or absolute)
###### Output ######
--resDir=Res # Directory to store DISK outputs
--eraseDir=1 # erase files in dirName if any
--printPop=0 # Print sorted pop. every gen.
--storeArch=0 # Store the archive's objective vectors at each gen.
--contribution=0 # Store the contribution of the archive at each gen.
--entropy=0 # Store the entropy of the archive at each gen.
###### Persistence ######
--Load= # -L : A save file to restart from
--recomputeFitness=0 # -r : Recompute the fitness after re-loading the pop.?
--saveFrequency=0 # Save every F generation (0 = only final state, absent = never)
--saveTimeInterval=0 # Save every T seconds (0 or absent = never)
--status=./FlowShopEA.status # Status file
###### Representation ######
--BenchmarkFile=benchmarks/020_10_01.txt # -B : Benchmark file name (benchmarks are available at www.lifl.fr/~liefooga/benchmarks) REQUIRED
###### Stopping criterion ######
--maxGen=100 # -G : Maximum number of generations (0 = none)
--maxEval=0 # -E : Maximum number of evaluations (0 = none)
--maxTime=0 # -T : Maximum running time in seconds (0 = none)
--CtrlC=1 # -C : Terminate current generation upon Ctrl C
###### Variation Operators ######
--crossRate=1 # Relative rate for the only crossover
--shiftMutRate=0.5 # Relative rate for shift mutation
--exchangeMutRate=0.5 # Relative rate for exchange mutation
--pCross=0.25 # -c : Probability of Crossover
--pMut=0.35 # -m : Probability of Mutation

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SUBDIRS = flowshop
noinst_PROGRAMS = FlowShopEA
FlowShopEA_SOURCES = FlowShopEA.cpp
LDADD = ${EO_DIR}/src/libeo.a ${EO_DIR}/src/utils/libeoutils.a $(top_srcdir)/src/core/libmoeo.a flowshop/libflowshop.a
INCLUDES = -Iflowshop -I${EO_DIR}/src/ -I$(top_srcdir)/src/
AM_CXXFLAGS = -Wall -ansi -pedantic

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@ -0,0 +1,63 @@
20
5
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5
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289
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873
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9
342
87 56 64 85 13
10
764
76 3 7 85 86
11
268
91 61 1 9 72
12
1158
14 73 63 39 8
13
646
29 75 41 41 49
14
1111
12 47 63 56 47
15
965
77 14 47 40 87
16
703
32 21 26 54 58
17
1205
87 86 75 77 18
18
334
68 5 77 51 68
19
1111
94 77 40 31 28

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20
5
379008056
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1221
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5
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1435
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8
1301
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637
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749
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1223
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887
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1330
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16
767
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1179
83 72 48 55 31 3 67 80 86 62

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20
10
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0
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1157
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1391
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763
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5
1456
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6
900
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1047
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1355
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1262
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1329
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562
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684
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567
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999
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18
577
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19
704
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20
20
479340445
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1882
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1117
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1181
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1416
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7
1635
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1723
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1932
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10
1219
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1521
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1322
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1057
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1922
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1130
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1411
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17
1840
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1741
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1377
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@ -0,0 +1,153 @@
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1601
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1628
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483
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285
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1092
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936
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434
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1258
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21
2405
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22
862
89 29 23 21 46
23
298
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24
2014
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25
2608
21 20 91 70 58
26
1737
78 42 67 89 85
27
2214
46 80 91 54 58
28
1910
99 94 4 96 97
29
2245
10 35 60 62 10
30
2211
17 8 38 46 79
31
547
23 41 25 60 93
32
2237
83 65 90 19 2
33
1327
47 4 93 97 87
34
700
86 71 13 13 17
35
1011
18 30 65 7 18
36
2001
67 14 25 44 10
37
1230
46 32 34 7 50
38
1020
4 50 47 73 8
39
1918
14 30 98 15 26
40
1001
4 27 91 66 14
41
1383
20 98 11 70 21
42
1229
88 39 46 97 15
43
2594
50 84 50 33 10
44
403
84 65 77 97 85
45
1515
58 12 5 64 46
46
431
93 58 14 73 42
47
1971
76 45 47 28 18
48
939
50 49 80 4 36
49
2028
30 15 45 87 2

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@ -0,0 +1,153 @@
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10
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1540
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2386
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812
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1370
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1319
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2388
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2076
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15
2519
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16
1981
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17
695
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18
2133
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19
1871
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1495
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21
754
2 10 87 65 91 44 3 98 23 32
22
783
85 63 88 59 38 43 94 90 66 26
23
984
44 96 10 4 25 76 76 36 5 22
24
2004
7 55 32 10 87 99 95 75 15 12
25
2269
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26
1771
19 66 25 62 66 11 4 26 2 34
27
2030
69 94 24 43 54 35 37 24 81 87
28
2603
12 7 90 49 86 52 82 55 12 59
29
2150
73 15 7 54 49 8 57 98 40 2
30
2157
85 11 11 87 3 40 61 86 59 38
31
2187
23 99 49 29 48 62 6 30 32 84
32
937
53 37 2 2 44 25 97 92 16 62
33
728
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34
510
88 56 17 75 37 30 27 66 78 11
35
2786
8 69 32 39 82 1 95 47 41 93
36
2134
26 22 39 77 31 73 46 3 43 57
37
1046
42 56 9 69 59 27 92 41 94 81
38
2849
58 67 83 15 78 16 46 41 1 10
39
1956
63 63 69 78 33 91 52 47 93 40
40
2456
7 96 67 68 36 33 8 89 22 62
41
1105
2 74 28 37 3 11 11 28 93 49
42
1560
44 4 88 22 58 99 7 39 62 90
43
1745
38 42 23 41 10 2 54 80 53 34
44
2216
24 40 91 92 98 60 72 47 30 11
45
1157
76 30 71 67 6 90 57 57 34 81
46
1317
85 93 3 24 44 36 85 74 27 51
47
2372
61 36 26 87 62 62 22 38 30 21
48
777
32 25 41 91 24 15 87 59 54 39
49
972
90 87 96 31 94 3 65 5 77 27

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20
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0
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3096
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3
2950
75 70 95 66 35 62 32 55 77 57 62 77 82 63 22 32 83 34 42 31
4
1249
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2481
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6
3193
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7
3253
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8
2123
84 80 87 37 30 18 26 50 72 70 42 15 78 51 84 48 23 19 46 9
9
2390
62 18 37 21 38 54 98 83 93 32 65 36 69 97 66 49 45 66 41 54
10
1764
91 5 24 3 78 24 17 70 68 31 39 65 76 52 25 66 52 61 78 13
11
2940
14 18 24 76 79 55 25 21 25 64 79 97 2 46 16 22 6 60 3 47
12
1635
95 17 65 67 58 96 21 71 67 11 9 27 14 16 79 37 3 98 72 6
13
3171
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14
2670
4 90 19 47 38 12 68 36 43 32 72 61 22 96 51 82 55 79 53 19
15
2932
95 93 67 72 76 96 24 50 93 58 29 24 26 85 29 59 97 71 59 97
16
2054
2 14 66 66 70 53 62 31 21 98 36 97 44 61 29 88 83 28 34 41
17
3238
97 49 6 56 72 92 89 86 33 95 48 61 1 76 90 77 42 74 66 1
18
2466
68 52 65 95 85 77 60 29 14 25 57 75 4 30 83 19 81 27 42 57
19
2384
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20
3473
33 78 2 47 10 91 38 93 59 45 93 73 55 42 19 52 68 13 27 62
21
3290
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22
2086
98 41 82 81 42 41 87 83 85 87 88 29 87 37 87 96 56 12 8 90
23
2560
8 75 90 56 67 30 1 89 85 31 77 3 35 43 12 23 88 51 65 76
24
3253
85 98 30 76 20 85 99 44 70 1 94 96 76 88 34 64 50 16 34 12
25
3205
86 93 63 66 24 17 34 38 35 96 39 51 98 91 23 22 13 49 6 89
26
2534
73 33 5 36 75 23 9 62 2 22 74 26 78 14 44 37 23 83 42 37
27
2039
4 75 93 53 23 60 22 45 76 95 46 44 81 63 30 3 13 48 39 35
28
2273
40 68 53 26 33 76 74 22 46 73 17 56 48 65 82 52 49 13 2 91
29
2062
98 33 85 52 60 39 14 85 72 77 30 31 25 74 83 44 18 78 7 69
30
1909
12 60 81 29 20 85 14 39 69 30 62 64 81 71 42 11 50 96 85 55
31
2432
59 82 73 36 75 10 84 98 46 88 77 38 27 8 56 21 94 77 32 48
32
2624
44 24 34 68 83 65 75 56 3 14 43 44 84 39 89 85 71 68 14 56
33
3325
46 99 74 21 26 15 37 68 57 22 98 46 59 95 38 6 64 88 74 84
34
3427
2 4 13 71 92 55 32 84 71 93 48 66 98 82 96 40 31 77 59 22
35
1318
41 97 78 61 29 41 29 77 77 48 14 31 14 17 10 68 21 76 95 51
36
1539
28 24 35 71 39 28 32 67 33 10 45 48 32 38 3 30 2 73 48 43
37
3223
83 50 20 69 14 93 89 53 49 7 25 27 95 69 53 35 63 92 37 50
38
1703
28 55 16 28 74 88 12 46 59 14 98 82 30 17 97 58 58 72 59 62
39
1051
21 91 48 86 66 27 47 24 82 91 30 51 13 24 11 31 36 87 4 61
40
1512
80 46 12 27 86 77 19 52 59 5 90 90 68 66 65 11 64 66 42 10
41
2589
71 58 11 41 10 81 97 96 70 43 92 63 19 75 47 11 52 98 93 87
42
3248
4 17 80 86 27 19 7 2 76 30 35 85 57 52 76 6 8 40 32 99
43
2047
60 47 9 55 8 76 12 88 10 79 13 36 65 59 22 59 94 31 30 40
44
3333
34 82 24 17 7 55 43 33 65 39 75 69 13 4 17 64 51 75 16 91
45
1760
55 6 76 62 97 67 89 27 19 34 55 67 63 73 14 65 36 45 95 64
46
2333
53 15 32 96 84 65 14 49 77 77 80 81 26 56 11 23 82 98 58 62
47
2400
96 91 35 59 56 8 33 78 86 81 67 18 96 19 69 80 30 90 12 53
48
2142
37 74 66 53 61 18 56 82 21 11 3 81 53 39 91 75 17 4 95 33
49
1231
37 42 48 93 9 56 57 65 75 10 93 72 94 51 53 63 21 23 21 16

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