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# ignore html files
*.html
# ignore all textual files
*.txt
# ignore object and archive files
*.[oa]
# ignore auto-saved files
*~
\#*\#
# excepted these manually configured files
!CMakeLists.txt
!README.txt
!application/
!build/
!doc/
!lib/
!src/
!test/
!eompi.html
build/

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ForRelease Normal file
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In order to create a new release from the current repository, perform the
following steps:
- If necessary, create a branch named "eo_x.y.z"
- Set version number in eo-conf.cmake
- Check/update NEWS file, set release date and version in NEWS.
- use the "archive_current.sh" script to create the source archive
- Put source archive and packages files at SourceForge
- Update the documentation on the website
- Post news on SourceForge project-page
- Send announcement to mailing lists
- Bump version number to next "x.y.z-edge" in eo-conf.cmake
When reaching stable versions:
- prepare a message with the following template:
-----8<-----
A new version of the "Evolving Objects" framework is available.
EO is a template-based, C++ evolutionary computation library which
helps you to write your own stochastic optimization algorithms
insanely fast.
Learn more about EO on the official website:
http://eodev.sourceforge.net/
You will find the release x.y.z at the following address:
https://sourceforge.net/projects/eodev/files/eo/
Here is a summary of the change log:
- XXXXX
- and more…
Do not hesitate to submit the bugs you will face:
https://sourceforge.net/apps/trac/eodev/wiki/WikiStart
Happy evolutionary hacking.
-----8<-----
- Post the message to:
- EO news https://sourceforge.net/news/?group_id=9775
- EO mailing list: eodev-main@lists.sourceforge.net
- ParadisEO mailing list: paradiseo-users@lists.gforge.inria.fr
- EC-digest maling list: ec-digest-l@listserv.gmu.edu
- JET mailing list: jet@inria.fr

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today=`date --iso-8601`
git archive --format zip master > EO-${today}.zip

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edo/AUTHORS Normal file
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The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
Authors:
N:Johann Dréo
P:nojhan
E:johann.dreo@thalesgroup.com
D:2010-07-01
C:original design and code
N:Caner Candan
P:
E:caner.candan@thalesgroup.com
D:2010-07-01
C:original design and code
As of 2011-01-25, Thales SA disclaims all copyright interest in the Evolving Distribution Objects (EDO) framework.

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############################################################################
##########
### 1) If you want to set your own variables in install.cmake and avoid the cmd line
######################################################################################
INCLUDE(install.cmake OPTIONAL)
######################################################################################
######################################################################################
### 2) Project properties
######################################################################################
# Checks cmake version compatibility
CMAKE_MINIMUM_REQUIRED(VERSION 2.6)
PROJECT(EDO)
SET(PROJECT_VERSION_MAJOR 0)
SET(PROJECT_VERSION_MINOR 1)
SET(PROJECT_VERSION_PATCH 0)
SET(PROJECT_VERSION "${PROJECT_VERSION_MAJOR}.${PROJECT_VERSION_MINOR}.${PROJECT_VERSION_PATCH}")
######################################################################################
######################################################################################
### 3) Include useful features
######################################################################################
# include useful features for cmake
SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR} ${CMAKE_SOURCE_DIR}/cmake/modules)
INCLUDE(FindDoxygen)
INCLUDE(FindPkgConfig)
IF( WITH_BOOST AND WITH_EIGEN )
MESSAGE( "ERROR: You have to choose between Boost:ublas and Eigen, you cannot compile with both libraries" )
SET(IS_FATAL 1)
ELSEIF( NOT WITH_BOOST AND NOT WITH_EIGEN )
#MESSAGE( "WARNING: Boost:ublas and Eigen are both deactivated, some features may lack." )
# FIXME ideally, we would have a minimal implementation with STL vectors…
MESSAGE( "FIXME: Boost:ublas and Eigen are both deactivated, too much features will lack, you should choose one." )
SET(IS_FATAL 1)
ENDIF()
IF(WITH_BOOST)
FIND_PACKAGE(Boost 1.33.0)
IF( Boost_FOUND )
INCLUDE_DIRECTORIES( ${Boost_INCLUDE_DIRS} )
ADD_DEFINITIONS( -DWITH_BOOST )
ELSE()
MESSAGE( "ERROR: You asked for Boost:ublas but it has nost been found." )
SET(IS_FATAL 1)
ENDIF()
ELSEIF( WITH_EIGEN )
# FIXME FindEigen3.cmake does not work
#find_package(Eigen3)
#include_directories(EIGEN3_INCLUDE_DIR)
SET( EIGEN3_FOUND 1)
SET( EIGEN3_INCLUDE_DIR "/usr/include/eigen3/" )
IF( EIGEN3_FOUND )
INCLUDE_DIRECTORIES( ${EIGEN3_INCLUDE_DIR} )
ADD_DEFINITIONS( -DWITH_EIGEN )
ELSE()
MESSAGE( "ERROR: You asked for Eigen but it has nost been found." )
SET(IS_FATAL 1)
ENDIF()
ENDIF()
FIND_PACKAGE(EO)
INCLUDE_DIRECTORIES(
${EO_INCLUDE_DIRS}
${MO_INCLUDE_DIRS}
)
LINK_DIRECTORIES(
${EO_LIBRARY_DIRS}
)
######################################################################################
######################################################################################
### 4) Include header files path
######################################################################################
INCLUDE_DIRECTORIES(
${CMAKE_CURRENT_SOURCE_DIR}/src
)
######################################################################################
######################################################################################
### 5) Set compiler definitions
######################################################################################
IF(UNIX)
# enable warnings
ADD_DEFINITIONS( -Wall -W -Wextra )
# ADD_DEFINITIONS( -Weffc++)
# ADD_DEFINITIONS( -g3 )
ENDIF()
######################################################################################
######################################################################################
### 6) Prepare some variables for CMAKE usage
######################################################################################
# Empty source files, because we want to build a library
SET(SAMPLE_SRCS)
######################################################################################
######################################################################################
### 7) Now where we go ?
######################################################################################
ADD_SUBDIRECTORY(src)
ADD_SUBDIRECTORY(application)
ADD_SUBDIRECTORY(test)
ADD_SUBDIRECTORY(doc)
######################################################################################
######################################################################################
### 8) Create executable, link libraries and prepare target
######################################################################################
SET(LIBRARY_OUTPUT_PATH ${CMAKE_BINARY_DIR}/lib)
LINK_DIRECTORIES(${LIBRARY_OUTPUT_PATH})
ADD_LIBRARY(edo STATIC ${SAMPLE_SRCS})
INSTALL(TARGETS edo ARCHIVE DESTINATION lib COMPONENT libraries)
######################################################################################
######################################################################################
### 9) Install pkg-config config file for EO
######################################################################################
INSTALL(FILES edo.pc DESTINATION lib/pkgconfig COMPONENT headers)
######################################################################################
######################################################################################
### 10) Include packaging
######################################################################################
INCLUDE(Packaging.cmake)
######################################################################################

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redistribution under these terms (or, alternatively, under the terms of the
ordinary General Public License).
To apply these terms, attach the following notices to the library. It is
safest to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least the
"copyright" line and a pointer to where the full notice is found.
<one line to give the library's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Also add information on how to contact you by electronic and paper mail.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the library, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the
library `Frob' (a library for tweaking knobs) written by James Random Hacker.
<signature of Ty Coon>, 1 April 1990
Ty Coon, President of Vice
That's all there is to it!

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* current release:
* release 1.3.0 (2011-07-24)
- alternative implementation of the multi-normal operators using the Eigen3 library
* release 0.0 (2011-09-15)
- basic design for estimation of distribution algorithms and, more generally for randomized search heuristics
- continuous EDA example
- EDA using multi-normal distribution, implementation using the boost::ublas library

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######################################################################################
### 1) Check dependencies
######################################################################################
IF (NOT DEFINED PROJECT_NAME OR
NOT DEFINED PROJECT_VERSION_MAJOR OR
NOT DEFINED PROJECT_VERSION_MINOR OR
NOT DEFINED PROJECT_VERSION_PATCH OR
NOT DEFINED PROJECT_VERSION)
MESSAGE(FATAL_ERROR "Be sure you have defined PROJECT_NAME and PROJECT_VERSION*.")
ENDIF()
######################################################################################
######################################################################################
### 2) Set up components
######################################################################################
SET(CPACK_COMPONENTS_ALL libraries)
SET(CPACK_ALL_INSTALL_TYPES Full)
SET(CPACK_COMPONENT_LIBRARIES_DISPLAY_NAME "Distribution Objects")
SET(CPACK_COMPONENT_LIBRARIES_DESCRIPTION "Distribution Objects library")
SET(CPACK_COMPONENT_LIBRARIES_GROUP "Devel")
SET(CPACK_COMPONENT_LIBRARIES_INSTALL_TYPES Full)
######################################################################################
######################################################################################
### 3) Set up general information about packaging
######################################################################################
# For more details: http://www.cmake.org/Wiki/CMake:Component_Install_With_CPack
#cpack package information
SET(CPACK_PACKAGE_DESCRIPTION_FILE "${CMAKE_CURRENT_SOURCE_DIR}/README")
SET(CPACK_PACKAGE_DESCRIPTION "Distribution Objects")
SET(CPACK_RESOURCE_FILE_LICENSE "${CMAKE_CURRENT_SOURCE_DIR}/COPYING")
SET(CPACK_PACKAGE_DESCRIPTION_SUMMARY "Distribution Objects")
SET(CPACK_PACKAGE_VENDOR "Thales")
SET(CPACK_PACKAGE_CONTACT "caner.candan@thalesgroup.com")
SET(CPACK_PACKAGE_VERSION ${PROJECT_VERSION})
SET(CPACK_STRIP_FILES ${PROJECT_NAME})
SET(CPACK_SOURCE_STRIP_FILES "bin/${PROJECT_NAME}")
SET(CPACK_PACKAGE_EXECUTABLES "${PROJECT_NAME}" "${PROJECT_NAME}")
SET(CPACK_PACKAGE_VERSION_MAJOR "${PROJECT_VERSION_MAJOR}")
SET(CPACK_PACKAGE_VERSION_MINOR "${PROJECT_VERSION_MINOR}")
SET(CPACK_PACKAGE_VERSION_PATCH "${PROJECT_VERSION_PATCH}")
SET(CPACK_PACKAGE_INSTALL_DIRECTORY "${PROJECT_NAME} ${PROJECT_VERSION_MAJOR}.${PROJECT_VERSION_MINOR}")
######################################################################################
######################################################################################
### 4) Set up debian packaging information
######################################################################################
SET(CPACK_DEBIAN_PACKAGE_DEPENDS "libstdc++6, libgcc1, libc6, libxml2, libmpich2-1.2, eo, mo")
SET(CPACK_DEBIAN_PACKAGE_SECTION "devel")
SET(CPACK_DEBIAN_PACKAGE_PRIORITY "optional")
######################################################################################
######################################################################################
### 5) And finally, include cpack, this is the last thing to do.
######################################################################################
INCLUDE(CPack)
######################################################################################

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This package contains the source code for EDO.
# Step 1 - Configuration
------------------------
Rename the "install.cmake-dist" file as "install.cmake" and edit it, inserting the FULL PATH
to your ParadisEO distribution.
On Windows write your path with double antislash (ex: C:\\Users\\...)
# Step 2 - Build process
------------------------
ParadisEO is assumed to be compiled. To download ParadisEO, please visit http://paradiseo.gforge.inria.fr/.
Go to the DO/build/ directory and lunch cmake:
(Unix) > cmake ..
(Windows) > cmake .. -G"Visual Studio 9 2008"
Note for windows users: if you don't use VisualStudio 9, enter the name of your generator instead of "VisualStudio 9 2008".
# Step 3 - Compilation
----------------------
In the edo/build/ directory:
(Unix) > make
(Windows) Open the VisualStudio solution and compile it, compile also the target install.
You can refer to this tutorial if you don't know how to compile a solution: http://paradiseo.gforge.inria.fr/index.php?n=Paradiseo.VisualCTutorial
# Step 4 - Execution
---------------------
A toy example is given to test the components. You can run these tests as following.
To define problem-related components for your own problem, please refer to the tutorials available on the website : http://paradiseo.gforge.inria.fr/.
In the edo/build/ directory:
(Unix) > ctest
Windows users, please refer to this tutorial: http://paradiseo.gforge.inria.fr/index.php?n=Paradiseo.VisualCTutorial
In the directory "application", there are several directory such as eda which instantiate EDA solver.
(Unix) After compilation you can run the binary "build/eda" and see results. Parameters can be modified from command line.
(Windows) Add argument "eda.param" and execute the corresponding algorithms.
Windows users, please refer to this tutorial: http://paradiseo.gforge.inria.fr/index.php?n=Paradiseo.VisualCTutorial
# Documentation
---------------
The API-documentation is available in doc/html/index.html
# Things to keep in mind when using EDO
----------------------------------------
* By default, the EO random generator's seed is initialized by the number of seconds since the epoch (with time(0)). It is available in the status file dumped at each execution. Please, keep in mind that if you start two run at the same second without modifying the seed, you will get exactly the same results.
* Execution times are measured with the boost:timer, that measure wallclock time. Additionaly, it could not measure times larger than approximatively 596.5 hours (or even less). See http://www.boost.org/doc/libs/1_33_1/libs/timer/timer.htm
* The q-quantile computation use averaging at discontinuities (in R, it correspond to the R-2 method, in SAS, SAS-5). For more explanations, see http://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population and http://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html
* You can send a SIGUSR1 to a process to get some information (written down in the log file) on the current state of the search.

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######################################################################################
### 1) Where do we go now ?!?
######################################################################################
INCLUDE_DIRECTORIES(
${CMAKE_CURRENT_SOURCE_DIR}/common
)
ADD_SUBDIRECTORY(common)
#ADD_SUBDIRECTORY(eda_sa)
ADD_SUBDIRECTORY(eda)
ADD_SUBDIRECTORY(cmaes)
######################################################################################

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PROJECT(cmaes)
#find_package(Eigen3 REQUIRED)
#include_directories(EIGEN3_INCLUDE_DIR)
INCLUDE_DIRECTORIES( ${EIGEN3_INCLUDE_DIR} )
MESSAGE( "MESSAGE:" ${EIGEN3_INCLUDE_DIR} )
#FIND_PACKAGE(Boost 1.33.0)
INCLUDE_DIRECTORIES(${Boost_INCLUDE_DIRS})
LINK_DIRECTORIES(${Boost_LIBRARY_DIRS})
INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
SET(RESOURCES
${PROJECT_NAME}.param
)
FOREACH(file ${RESOURCES})
EXECUTE_PROCESS(
COMMAND ${CMAKE_COMMAND} -E copy_if_different
${CMAKE_CURRENT_SOURCE_DIR}/${file}
${EDO_BINARY_DIR}/${file}
)
ENDFOREACH(file)
#FILE(GLOB SOURCES *.cpp)
SET(EXECUTABLE_OUTPUT_PATH ${EDO_BINARY_DIR})
ADD_EXECUTABLE(${PROJECT_NAME} main.cpp)
TARGET_LINK_LIBRARIES(${PROJECT_NAME} edo edoutils ${EO_LIBRARIES} ${Boost_LIBRARIES})

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <eo>
//#include <mo>
#include <eoEvalFuncCounterBounder.h>
#include <do/make_pop.h>
#include <do/make_run.h>
#include <do/make_continue.h>
#include <do/make_checkpoint.h>
#include <edo>
#include "Rosenbrock.h"
#include "Sphere.h"
typedef eoReal<eoMinimizingFitness> RealVec;
typedef edoNormalAdaptive< RealVec > Distrib;
int main(int ac, char** av)
{
eoParser parser(ac, av);
eoState state;
// Letters used by the following declarations:
unsigned long max_eval = parser.getORcreateParam((unsigned long)0, "maxEval", "Maximum number of evaluations (0 = none)", 'E', "Stopping criterion").value(); // E
unsigned int dim = parser.createParam((unsigned int)10, "dimension-size", "Dimension size", 'd', "Problem").value(); // d
double mu = dim / 2;
edoNormalAdaptive<RealVec> distribution(dim);
eoSelect< RealVec >* selector = new eoRankMuSelect< RealVec >( mu );
state.storeFunctor(selector);
edoEstimator< Distrib >* estimator = new edoEstimatorNormalAdaptive<RealVec>( distribution );
state.storeFunctor(estimator);
eoEvalFunc< RealVec >* plainEval = new Rosenbrock< RealVec >();
state.storeFunctor(plainEval);
eoEvalFuncCounterBounder< RealVec > eval(*plainEval, max_eval);
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
eoInitFixedLength< RealVec >* init = new eoInitFixedLength< RealVec >( dim, *gen );
state.storeFunctor(init);
// (1) Population init and sampler
// Generation of population from do_make_pop (creates parameters, manages persistance and so on...)
// ... and creates the parameters: L P r S
// this first sampler creates a uniform distribution independently from our distribution (it does not use edoUniform).
eoPop< RealVec >& pop = do_make_pop(parser, state, *init);
// (2) First evaluation before starting the research algorithm
apply(eval, pop);
// Prepare bounder class to set bounds of sampling.
// This is used by edoSampler.
edoBounder< RealVec >* bounder =
new edoBounderRng< RealVec >( RealVec(dim, -5), RealVec(dim, 5), *gen); // FIXME do not use hard-coded bounds
state.storeFunctor(bounder);
// Prepare sampler class with a specific distribution
edoSampler< Distrib >* sampler = new edoSamplerNormalAdaptive< RealVec >( *bounder );
state.storeFunctor(sampler);
// stopping criteria
// ... and creates the parameter letters: C E g G s T
eoContinue< RealVec >& eo_continue = do_make_continue(parser, state, eval);
// population output
eoCheckPoint< RealVec >& pop_continue = do_make_checkpoint(parser, state, eval, eo_continue);
// keep the best solution found so far in an eoStat
// thus, if the population's best individual fitness decreases during the search, we could
// still keep the best found since the beginning, while avoiding the bias of elitism on the sample
eoBestIndividualStat<RealVec> best_indiv;
pop_continue.add( best_indiv );
// distribution output
edoDummyContinue< Distrib >* dummy_continue = new edoDummyContinue< Distrib >();
state.storeFunctor(dummy_continue);
edoCheckPoint< Distrib >* distribution_continue = new edoCheckPoint< Distrib >( *dummy_continue );
state.storeFunctor(distribution_continue);
eoReplacement< RealVec >* replacor = new eoCommaReplacement< RealVec >();
state.storeFunctor(replacor);
// Help + Verbose routines
make_verbose(parser);
make_help(parser);
// Some stuff to display helper when we are using -h option
if (parser.userNeedsHelp())
{
parser.printHelp(std::cout);
exit(1);
}
eoPopLoopEval<RealVec> popEval( eval );
// CMA-ES algorithm configuration
edoAlgo< Distrib >* algo = new edoAlgoAdaptive< Distrib >
(distribution, popEval, *selector, *estimator, *sampler, *replacor,
pop_continue, *distribution_continue );
// Use the best solution of the random first pop to start the search
// That is, center the distribution's mean on it.
distribution.mean( pop.best_element() );
// Beginning of the algorithm call
try {
eo::log << eo::progress << "Best solution after random init: " << pop.best_element().fitness() << std::endl;
do_run(*algo, pop);
} catch (eoEvalFuncCounterBounderException& e) {
eo::log << eo::warnings << "warning: " << e.what() << std::endl;
}
// use the stat instead of the pop, to get the best solution of the whole search
std::cout << best_indiv.value() << std::endl;
return 0;
}

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PROJECT(common)
SET(RESOURCES
gplot.py
ggobi.py
boxplot_eda_n_edasa.py
)
FOREACH(file ${RESOURCES})
EXECUTE_PROCESS(
COMMAND ${CMAKE_COMMAND} -E copy_if_different
${CMAKE_CURRENT_SOURCE_DIR}/${file}
${EDO_BINARY_DIR}/${file}
)
ENDFOREACH(file)

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#ifndef _Rosenbrock_h
#define _Rosenbrock_h
#include <eo>
#include <es.h>
#include <es/eoRealInitBounded.h>
#include <es/eoRealOp.h>
#include <es/eoEsChromInit.h>
#include <es/eoRealOp.h>
#include <es/make_real.h>
#include <apply.h>
#include <eoProportionalCombinedOp.h>
template < typename EOT >
class Rosenbrock : public eoEvalFunc< EOT >
{
public:
typedef typename EOT::AtomType AtomType;
virtual void operator()( EOT& p )
{
if (!p.invalid())
return;
p.fitness( _evaluate( p ) );
}
private:
AtomType _evaluate( EOT& p )
{
AtomType r = 0.0;
for (unsigned int i = 0; i < p.size() - 1; ++i)
{
r += p[i] * p[i];
}
return r;
}
};
#endif // !_Rosenbrock_h

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#ifndef _Sphere_h
#define _Sphere_h
#include <eo>
#include <es.h>
#include <es/eoRealInitBounded.h>
#include <es/eoRealOp.h>
#include <es/eoEsChromInit.h>
#include <es/eoRealOp.h>
#include <es/make_real.h>
#include <apply.h>
#include <eoProportionalCombinedOp.h>
template < typename EOT >
class Sphere : public eoEvalFunc< EOT >
{
public:
typedef typename EOT::AtomType AtomType;
virtual void operator()( EOT& p )
{
if (!p.invalid())
return;
p.fitness( _evaluate( p ) );
}
private:
AtomType _evaluate( EOT& p )
{
AtomType r = 0.0;
for (unsigned int i = 0; i < p.size() - 1; ++i)
{
r += p[i] * p[i];
}
return r;
}
};
#endif // !_Sphere_h

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#!/usr/bin/env python
from pylab import *
#from pprint import pprint
FILE_LOCATIONS = 'EDA_ResPop/list_of_files.txt'
data = []
locations = [ line.split()[0] for line in open( FILE_LOCATIONS ) ]
#pprint( locations )
for cur_file in locations:
fitnesses = [ float(line.split()[0]) for line in open( cur_file ).readlines()[1:-1] ]
data.append( fitnesses[1:] )
#pprint( data )
boxplot( data )
# FILE_LOCATIONS = 'EDASA_ResPop/list_of_files.txt'
# data = []
# locations = [ line.split()[0] for line in open( FILE_LOCATIONS ) ]
# #pprint( locations )
# for cur_file in locations:
# fitnesses = [ float(line.split()[0]) for line in open( cur_file ).readlines()[1:-1] ]
# data.append( fitnesses[1:] )
# #pprint( data )
# boxplot( data )
show()

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#!/usr/bin/env python
from pprint import *
import sys, os
if __name__ == '__main__':
# parameter phase
if len(sys.argv) < 2:
print 'Usage: %s [FILE]' % sys.argv[0]
sys.exit()
filename = sys.argv[1]
lines = open(filename).readlines()
# formatting phase
try:
results = [ x.split() for x in lines[1:-1] ]
except IOError, e:
print 'Error: %s' % e
sys.exit()
# dimension estimating phase
popsize = int(lines[0].split()[0])
dimsize = int(results[0][1])
# printing phase
print 'popsize: %d' % popsize
print 'dimsize: %d' % dimsize
print
pprint( results )
# cvs converting phase
i = 1
for x in results:
x.insert(0, '"%d"' % i)
i += 1
header = ['""', '"fitness"', '"dimsize"']
for i in range(0, dimsize):
header.append( '"dim%d"' % i )
results.insert(0, header)
# cvs printing phase
file_results = '\n'.join( [ ','.join( x ) for x in results ] )
print
print file_results
try:
open('%s.csv' % filename, 'w').write(file_results + '\n')
except IOError, e:
print 'Error: %s' % e
sys.exit()
# ggobi plotting phase
os.system('ggobi %s.csv' % filename)

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#!/usr/bin/env python
"""plot.py -- Plot EDA-SA results file"""
import os, time, math, tempfile
import numpy
try:
import Gnuplot, Gnuplot.PlotItems, Gnuplot.funcutils
except ImportError:
# kludge in case Gnuplot hasn't been installed as a module yet:
import __init__
Gnuplot = __init__
import PlotItems
Gnuplot.PlotItems = PlotItems
import funcutils
Gnuplot.funcutils = funcutils
import optparse, logging, sys
LEVELS = {'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL}
def logger(level_name, filename='plot.log'):
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
filename=filename, filemode='a'
)
console = logging.StreamHandler()
console.setLevel(LEVELS.get(level_name, logging.NOTSET))
console.setFormatter(logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s'))
logging.getLogger('').addHandler(console)
def parser(parser=optparse.OptionParser()):
parser.add_option('-v', '--verbose', choices=LEVELS.keys(), default='warning', help='set a verbose level')
parser.add_option('-f', '--files', help='give some input sample files separated by comma (cf. gen1,gen2,...)', default='')
parser.add_option('-r', '--respop', help='define the population results containing folder', default='./ResPop')
parser.add_option('-o', '--output', help='give an output filename for logging', default='plot.log')
parser.add_option('-d', '--dimension', help='give a dimension size', default=2)
parser.add_option('-m', '--multiplot', action="store_true", help='plot all graphics in one window', dest="multiplot", default=True)
parser.add_option('-p', '--plot', action="store_false", help='plot graphics separetly, one by window', dest="multiplot")
parser.add_option('-w', '--windowid', help='give the window id you want to display, 0 means we display all ones, this option should be combined with -p', default=0)
parser.add_option('-G', '--graphicsdirectory', help='give a directory name for graphics, this option should be combined with -u', default='plot')
parser.add_option('-g', '--graphicsprefixname', help='give a prefix name for graphics, this option should be combined with -u', default='plot')
parser.add_option('-t', '--terminal', action="store_true", help='display graphics on gnuplot windows', dest="terminal", default=True)
parser.add_option('-u', '--png', action="store_false", help='display graphics on png files', dest="terminal")
options, args = parser.parse_args()
logger(options.verbose, options.output)
return options
options = parser()
def wait(str=None, prompt='Press return to show results...\n'):
if str is not None:
print str
raw_input(prompt)
def draw2DRect(min=(0,0), max=(1,1), color='black', state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
xmin, ymin = min
xmax, ymax = max
cmd = 'set arrow from %s,%s to %s,%s nohead lc rgb "%s"'
g(cmd % (xmin, ymin, xmin, ymax, color))
g(cmd % (xmin, ymax, xmax, ymax, color))
g(cmd % (xmax, ymax, xmax, ymin, color))
g(cmd % (xmax, ymin, xmin, ymin, color))
return g
def draw3DRect(min=(0,0,0), max=(1,1,1), state=None, g=None):
if g == None: g = Gnuplot.Gnuplot()
if state != None: state.append(g)
# TODO
return g
def getSortedFiles(path):
assert path != None
if options.files == '':
filelist = os.listdir(path)
filelist.sort()
else:
filelist = options.files.split(',')
checkFileErrors(path, filelist)
return filelist
def checkFileErrors(path, filelist):
for filename in filelist:
for line in open('%s/%s' % (path, filename)):
if '-nan' in line:
logging.warning("checkFileErrors: %s/%s file contains bad value, it is going to be skipped" % (path, filename))
filelist.remove(filename)
break
def plotXPointYFitness(path, fields='3:1', state=None, g=None):
if g == None:
g = Gnuplot.Gnuplot()
if not options.terminal:
g('set terminal png')
g('set output \'%s/%s_%s.png\'' % (options.graphicsdirectory, options.graphicsprefixname, 'plotXPointYFitness'))
if state != None: state.append(g)
g.title('Fitness observation')
g.xlabel('Coordinates')
g.ylabel('Fitness (Quality)')
files=[]
for filename in getSortedFiles(path):
files.append(Gnuplot.File(path + '/' + filename, using=fields,
with_='points',
#title='distribution \'' + filename + '\''
title=""
)
)
if len(files) > 0:
g.plot(*files)
return g
def plotXYPointZFitness(path, fields='4:3:1', state=None, g=None):
if g == None:
g = Gnuplot.Gnuplot()
if not options.terminal:
g('set terminal png')
g('set output \'%s/%s_%s.png\'' % (options.graphicsdirectory, options.graphicsprefixname, 'plotXYPointZFitness'))
if state != None: state.append(g)
g.title('Fitness observation in 3-D')
g.xlabel('x-axes')
g.ylabel('y-axes')
g.zlabel('Fitness (Quality)')
files=[]
for filename in getSortedFiles(path):
files.append(Gnuplot.File(path + '/' + filename, using=fields,
with_='points',
#title='distribution \'' + filename + '\''
title=""
)
)
if len(files) > 0:
g.splot(*files)
return g
def plotXYPoint(path, fields='3:4', state=None, g=None):
if g == None:
g = Gnuplot.Gnuplot()
if not options.terminal:
g('set terminal png')
g('set output \'%s/%s_%s.png\'' % (options.graphicsdirectory, options.graphicsprefixname, 'plotXYPoint'))
if state != None: state.append(g)
g.title('Points observation in 2-D')
g.xlabel('x-axes')
g.ylabel('y-axes')
files=[]
for filename in getSortedFiles(path):
files.append(Gnuplot.File(path + '/' + filename, using=fields,
with_='points',
#title='distribution \'' + filename + '\''
title=""
)
)
if len(files) > 0:
g.plot(*files)
return g
def plotXYZPoint(path, fields='3:4:5', state=None, g=None):
if g == None:
g = Gnuplot.Gnuplot()
if not options.terminal:
g('set terminal png')
g('set output \'%s/%s_%s.png\'' % (options.graphicsdirectory, options.graphicsprefixname, 'plotXYZPoint'))
if state != None: state.append(g)
g.title('Points observation in 3-D')
g.xlabel('x-axes')
g.ylabel('y-axes')
g.zlabel('z-axes')
files=[]
for filename in getSortedFiles(path):
files.append(Gnuplot.File(path + '/' + filename, using=fields,
with_='points',
#title='distribution \'' + filename + '\''
title=""
)
)
if len(files) > 0:
g.splot(*files)
return g
def plotParams(path, field='1', state=None, g=None):
if g == None:
g = Gnuplot.Gnuplot()
if not options.terminal:
g('set terminal png')
g('set output \'%s/%s_%s.png\'' % (options.graphicsdirectory, options.graphicsprefixname, 'plotXYZPoint'))
if state != None: state.append(g)
g.title('Hyper-volume comparaison through all dimensions')
g.xlabel('Iterations')
g.ylabel('Hyper-volume')
g.plot(Gnuplot.File(path, with_='lines', using=field,
title='multivariate distribution narrowing'))
return g
def plot2DRectFromFiles(path, state=None, g=None, plot=True):
if g == None:
g = Gnuplot.Gnuplot()
if not options.terminal:
g('set terminal png')
g('set output \'%s_%s.png\'' % (options.graphicsprefixname, 'plot2DRectFromFiles'))
if state != None: state.append(g)
g.title('Rectangle drawing observation')
g.xlabel('x-axes')
g.ylabel('y-axes')
x1,x2,y1,y2 = 0,0,0,0
colors = ['red', 'orange', 'blue', 'green', 'gold', 'yellow', 'gray']
#colors = open('rgb.txt', 'r').readlines()
colors_size = len(colors)
i = 0 # for color
for filename in getSortedFiles(path):
line = open(path + '/' + filename, 'r').readline()
fields = line.split(' ')
if not fields[0] == '2':
print 'plot2DRectFromFiles: higher than 2 dimensions not possible to draw'
return
xmin,ymin,xmax,ymax = fields[1:5]
#print xmin,ymin,xmax,ymax
cur_color = colors[i % colors_size]
draw2DRect((xmin,ymin), (xmax,ymax), cur_color, g=g)
g('set obj rect from %s,%s to %s,%s back lw 1.0 fc rgb "%s" fillstyle solid 1.00 border -1'
% (xmin,ymin,xmax,ymax,cur_color)
)
if plot:
if float(xmin) < x1: x1 = float(xmin)
if float(ymin) < y1: y1 = float(ymin)
if float(xmax) > x2: x2 = float(xmax)
if float(ymax) > y2: y2 = float(ymax)
#print x1,y1,x2,y2
i += 1
#print x1,y1,x2,y2
if plot:
g.plot('[%s:%s][%s:%s] -9999 notitle' % (x1, x2, y1, y2))
return g
def main():
gstate = []
n = int(options.dimension)
w = int(options.windowid)
r = options.respop
if not options.terminal:
try:
os.mkdir(options.graphicsdirectory)
except OSError:
pass
if options.multiplot:
g = Gnuplot.Gnuplot()
if not options.terminal:
g('set terminal png')
g('set output \'%s/%s_%s.png\'' % (options.graphicsdirectory, options.graphicsprefixname, 'multiplot'))
g('set parametric')
g('set nokey')
g('set noxtic')
g('set noytic')
g('set noztic')
g('set size 1.0, 1.0')
g('set origin 0.0, 0.0')
g('set multiplot')
g('set size 0.5, 0.5')
g('set origin 0.0, 0.5')
if n >= 1:
plotXPointYFitness(r, state=gstate, g=g)
g('set size 0.5, 0.5')
g('set origin 0.0, 0.0')
if n >= 2:
plotXPointYFitness(r, '4:1', state=gstate, g=g)
g('set size 0.5, 0.5')
g('set origin 0.5, 0.5')
if n >= 2:
plotXYPointZFitness(r, state=gstate, g=g)
g('set size 0.5, 0.5')
g('set origin 0.5, 0.0')
if n >= 2:
plotXYPoint(r, state=gstate, g=g)
elif n >= 3:
plotXYZPoint(r, state=gstate, g=g)
g('set nomultiplot')
else:
if n >= 1 and w in [0, 1]:
plotXPointYFitness(r, state=gstate)
if n >= 2 and w in [0, 2]:
plotXPointYFitness(r, '4:1', state=gstate)
if n >= 2 and w in [0, 3]:
plotXYPointZFitness(r, state=gstate)
if n >= 3 and w in [0, 4]:
plotXYZPoint(r, state=gstate)
if n >= 2 and w in [0, 5]:
plotXYPoint(r, state=gstate)
# if n >= 1:
# plotParams('./ResParams.txt', state=gstate)
# if n >= 2:
# plot2DRectFromFiles('./ResBounds', state=gstate)
# plotXYPoint(r, state=gstate)
# g = plot2DRectFromFiles('./ResBounds', state=gstate, plot=False)
# plotXYPoint(r, g=g)
if options.terminal:
wait(prompt='Press return to end the plot.\n')
# when executed, just run main():
if __name__ == '__main__':
logging.debug('### plotting started ###')
main()
logging.debug('### plotting ended ###')

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PROJECT(eda)
FIND_PACKAGE(Boost 1.33.0)
INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
INCLUDE_DIRECTORIES(${Boost_INCLUDE_DIRS})
LINK_DIRECTORIES(${Boost_LIBRARY_DIRS})
SET(RESOURCES
${PROJECT_NAME}.param
)
FOREACH(file ${RESOURCES})
EXECUTE_PROCESS(
COMMAND ${CMAKE_COMMAND} -E copy_if_different
${CMAKE_CURRENT_SOURCE_DIR}/${file}
${EDO_BINARY_DIR}/${file}
)
ENDFOREACH(file)
FILE(GLOB SOURCES *.cpp)
SET(EXECUTABLE_OUTPUT_PATH ${EDO_BINARY_DIR})
ADD_EXECUTABLE(${PROJECT_NAME} ${SOURCES})
TARGET_LINK_LIBRARIES(${PROJECT_NAME} edo edoutils ${EO_LIBRARIES} ${Boost_LIBRARIES})

7
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--rho=0 # -p : <etropolis sample size
--alpha=0 # -a : Temperature dicrease rate
--threshold=0.1 # -t : Temperature threshold stopping criteria
--sample-size=10 # -P : Sample size
--dimension-size=10 # -d : Dimension size
--temperature=100 # -T : Initial temperature
#--verbose # Enable verbose mode

180
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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <eo>
// #include <mo>
#include <eoEvalFuncCounterBounder.h>
#include <do/make_pop.h>
#include <do/make_run.h>
#include <do/make_continue.h>
#include <do/make_checkpoint.h>
#include <edo>
#include "Rosenbrock.h"
#include "Sphere.h"
typedef eoReal<eoMinimizingFitness> EOT;
typedef edoNormalMulti< EOT > Distrib;
int main(int ac, char** av)
{
eoParser parser(ac, av);
// Letters used by the following declarations:
// a d i p t
std::string section("Algorithm parameters");
eoState state;
// Instantiate all needed parameters for EDA algorithm
double selection_rate = parser.createParam((double)0.5, "selection_rate", "Selection Rate", 'R', section).value(); // R
eoSelect< EOT >* selector = new eoDetSelect< EOT >( selection_rate );
state.storeFunctor(selector);
edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
state.storeFunctor(estimator);
eoEvalFunc< EOT >* plainEval = new Rosenbrock< EOT >();
state.storeFunctor(plainEval);
unsigned long max_eval = parser.getORcreateParam((unsigned long)0, "maxEval", "Maximum number of evaluations (0 = none)", 'E', "Stopping criterion").value(); // E
eoEvalFuncCounterBounder< EOT > eval(*plainEval, max_eval);
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
unsigned int dimension_size = parser.createParam((unsigned int)10, "dimension-size", "Dimension size", 'd', section).value(); // d
eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( dimension_size, *gen );
state.storeFunctor(init);
// (1) Population init and sampler
// Generation of population from do_make_pop (creates parameters, manages persistance and so on...)
// ... and creates the parameters: L P r S
// this first sampler creates a uniform distribution independently from our distribution (it does not use edoUniform).
eoPop< EOT >& pop = do_make_pop(parser, state, *init);
// (2) First evaluation before starting the research algorithm
apply(eval, pop);
// Prepare bounder class to set bounds of sampling.
// This is used by edoSampler.
edoBounder< EOT >* bounder =
new edoBounderRng< EOT >( EOT(dimension_size, -5), EOT(dimension_size, 5), *gen); // FIXME do not use hard-coded bounds
state.storeFunctor(bounder);
// Prepare sampler class with a specific distribution
edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
state.storeFunctor(sampler);
// stopping criteria
// ... and creates the parameter letters: C E g G s T
eoContinue< EOT >& eo_continue = do_make_continue(parser, state, eval);
// population output
eoCheckPoint< EOT >& pop_continue = do_make_checkpoint(parser, state, eval, eo_continue);
// distribution output
edoDummyContinue< Distrib >* dummy_continue = new edoDummyContinue< Distrib >();
state.storeFunctor(dummy_continue);
edoCheckPoint< Distrib >* distribution_continue = new edoCheckPoint< Distrib >( *dummy_continue );
state.storeFunctor(distribution_continue);
// eoEPRemplacement causes the using of the current and previous
// sample for sampling.
eoReplacement< EOT >* replacor = new eoEPReplacement< EOT >(pop.size());
state.storeFunctor(replacor);
// Help + Verbose routines
make_verbose(parser);
make_help(parser);
// Some stuff to display helper when we are using -h option
if (parser.userNeedsHelp())
{
parser.printHelp(std::cout);
exit(1);
}
// population output (after helper)
//
// FIXME: theses objects are instanciated there in order to avoid a folder
// removing as edoFileSnapshot does within ctor.
edoPopStat< EOT >* popStat = new edoPopStat<EOT>;
state.storeFunctor(popStat);
pop_continue.add(*popStat);
edoFileSnapshot* fileSnapshot = new edoFileSnapshot("EDA_ResPop");
state.storeFunctor(fileSnapshot);
fileSnapshot->add(*popStat);
pop_continue.add(*fileSnapshot);
// distribution output (after helper)
edoDistribStat< Distrib >* distrib_stat = new edoStatNormalMulti< EOT >();
state.storeFunctor(distrib_stat);
distribution_continue->add( *distrib_stat );
// eoMonitor* stdout_monitor = new eoStdoutMonitor();
// state.storeFunctor(stdout_monitor);
// stdout_monitor->add(*distrib_stat);
// distribution_continue->add( *stdout_monitor );
eoFileMonitor* file_monitor = new eoFileMonitor("eda_distribution_bounds.txt");
state.storeFunctor(file_monitor);
file_monitor->add(*distrib_stat);
distribution_continue->add( *file_monitor );
eoPopLoopEval<EOT> popEval( eval );
// EDA algorithm configuration
edoAlgo< Distrib >* algo = new edoAlgoStateless< Distrib >
(popEval, *selector, *estimator, *sampler, *replacor,
pop_continue, *distribution_continue );
// Beginning of the algorithm call
try {
do_run(*algo, pop);
} catch (eoEvalFuncCounterBounderException& e) {
eo::log << eo::warnings << "warning: " << e.what() << std::endl;
} catch (std::exception& e) {
eo::log << eo::errors << "error: " << e.what() << std::endl;
exit(EXIT_FAILURE);
}
return 0;
}

27
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PROJECT(eda_sa)
FIND_PACKAGE(Boost 1.33.0)
INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
INCLUDE_DIRECTORIES(${Boost_INCLUDE_DIRS})
LINK_DIRECTORIES(${Boost_LIBRARY_DIRS})
SET(RESOURCES
${PROJECT_NAME}.param
)
FOREACH(file ${RESOURCES})
EXECUTE_PROCESS(
COMMAND ${CMAKE_COMMAND} -E copy_if_different
${CMAKE_CURRENT_SOURCE_DIR}/${file}
${EDO_BINARY_DIR}/${file}
)
ENDFOREACH(file)
FILE(GLOB SOURCES *.cpp)
SET(EXECUTABLE_OUTPUT_PATH ${EDO_BINARY_DIR})
ADD_EXECUTABLE(${PROJECT_NAME} ${SOURCES})
TARGET_LINK_LIBRARIES(${PROJECT_NAME} edo edoutils ${EO_LIBRARIES} ${MO_LIBRARIES} ${Boost_LIBRARIES})

7
edo/application/eda_sa/eda_sa.param Normal file
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--rho=0 # -p : <etropolis sample size
--alpha=0 # -a : Temperature dicrease rate
--threshold=0.1 # -t : Temperature threshold stopping criteria
--sample-size=10 # -P : Sample size
--dimension-size=10 # -d : Dimension size
--temperature=100 # -T : Initial temperature
#--verbose # Enable verbose mode

284
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#include <eo>
#include <mo>
#include <eoEvalFuncCounterBounder.h>
#include <do/make_pop.h>
#include <do/make_run.h>
#include <do/make_continue.h>
#include <do/make_checkpoint.h>
#include <edo>
#include "Rosenbrock.h"
#include "Sphere.h"
typedef eoReal<eoMinimizingFitness> EOT;
typedef edoNormalMulti< EOT > Distrib;
int main(int ac, char** av)
{
eoParser parser(ac, av);
// Letters used by the following declarations:
// a d i p t
std::string section("Algorithm parameters");
// FIXME: default value to check
double initial_temperature = parser.createParam((double)10e5, "temperature", "Initial temperature", 'i', section).value(); // i
eoState state;
//-----------------------------------------------------------------------------
// Instantiate all needed parameters for EDASA algorithm
//-----------------------------------------------------------------------------
double selection_rate = parser.createParam((double)0.5, "selection_rate", "Selection Rate", 'R', section).value(); // R
eoSelect< EOT >* selector = new eoDetSelect< EOT >( selection_rate );
state.storeFunctor(selector);
edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
state.storeFunctor(estimator);
eoSelectOne< EOT >* selectone = new eoDetTournamentSelect< EOT >( 2 );
state.storeFunctor(selectone);
edoModifierMass< Distrib >* modifier = new edoNormalMultiCenter< EOT >();
state.storeFunctor(modifier);
eoEvalFunc< EOT >* plainEval = new Rosenbrock< EOT >();
state.storeFunctor(plainEval);
unsigned long max_eval = parser.getORcreateParam((unsigned long)0, "maxEval", "Maximum number of evaluations (0 = none)", 'E', "Stopping criterion").value(); // E
eoEvalFuncCounterBounder< EOT > eval(*plainEval, max_eval);
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
unsigned int dimension_size = parser.createParam((unsigned int)10, "dimension-size", "Dimension size", 'd', section).value(); // d
eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( dimension_size, *gen );
state.storeFunctor(init);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (1) Population init and sampler
//-----------------------------------------------------------------------------
// Generation of population from do_make_pop (creates parameters, manages persistance and so on...)
// ... and creates the parameters: L P r S
// this first sampler creates a uniform distribution independently from our distribution (it does not use doUniform).
eoPop< EOT >& pop = do_make_pop(parser, state, *init);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// (2) First evaluation before starting the research algorithm
//-----------------------------------------------------------------------------
apply(eval, pop);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// Prepare bounder class to set bounds of sampling.
// This is used by doSampler.
//-----------------------------------------------------------------------------
edoBounder< EOT >* bounder = new edoBounderRng< EOT >(EOT(pop[0].size(), -5),
EOT(pop[0].size(), 5),
*gen);
state.storeFunctor(bounder);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// Prepare sampler class with a specific distribution
//-----------------------------------------------------------------------------
edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
state.storeFunctor(sampler);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// Metropolis sample parameters
//-----------------------------------------------------------------------------
unsigned int popSize = parser.getORcreateParam((unsigned int)20, "popSize", "Population Size", 'P', "Evolution Engine").value();
moContinuator< moDummyNeighbor<EOT> >* sa_continue = new moIterContinuator< moDummyNeighbor<EOT> >( popSize );
state.storeFunctor(sa_continue);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// SA parameters
//-----------------------------------------------------------------------------
double threshold_temperature = parser.createParam((double)0.1, "threshold", "Minimal temperature at which stop", 't', section).value(); // t
double alpha = parser.createParam((double)0.1, "alpha", "Temperature decrease rate", 'a', section).value(); // a
moCoolingSchedule<EOT>* cooling_schedule = new moSimpleCoolingSchedule<EOT>(initial_temperature, alpha, 0, threshold_temperature);
state.storeFunctor(cooling_schedule);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// stopping criteria
// ... and creates the parameter letters: C E g G s T
//-----------------------------------------------------------------------------
eoContinue< EOT >& eo_continue = do_make_continue(parser, state, eval);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// population output
//-----------------------------------------------------------------------------
eoCheckPoint< EOT >& pop_continue = do_make_checkpoint(parser, state, eval, eo_continue);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// distribution output
//-----------------------------------------------------------------------------
edoDummyContinue< Distrib >* dummy_continue = new edoDummyContinue< Distrib >();
state.storeFunctor(dummy_continue);
edoCheckPoint< Distrib >* distribution_continue = new edoCheckPoint< Distrib >( *dummy_continue );
state.storeFunctor(distribution_continue);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// eoEPRemplacement causes the using of the current and previous
// sample for sampling.
//-----------------------------------------------------------------------------
eoReplacement< EOT >* replacor = new eoEPReplacement< EOT >(pop.size());
// Below, use eoGenerationalReplacement to sample only on the current sample
//eoReplacement< EOT >* replacor = new eoGenerationalReplacement< EOT >(); // FIXME: to define the size
state.storeFunctor(replacor);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// Some stuff to display helper when we are using -h option
//-----------------------------------------------------------------------------
if (parser.userNeedsHelp())
{
parser.printHelp(std::cout);
exit(1);
}
// Help + Verbose routines
make_verbose(parser);
make_help(parser);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// population output (after helper)
//
// FIXME: theses objects are instanciate there in order to avoid a folder
// removing as edoFileSnapshot does within ctor.
//-----------------------------------------------------------------------------
edoPopStat< EOT >* popStat = new edoPopStat<EOT>;
state.storeFunctor(popStat);
pop_continue.add(*popStat);
edoFileSnapshot* fileSnapshot = new edoFileSnapshot("EDASA_ResPop");
state.storeFunctor(fileSnapshot);
fileSnapshot->add(*popStat);
pop_continue.add(*fileSnapshot);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// distribution output (after helper)
//-----------------------------------------------------------------------------
edoDistribStat< Distrib >* distrib_stat = new edoStatNormalMulti< EOT >();
state.storeFunctor(distrib_stat);
distribution_continue->add( *distrib_stat );
// eoMonitor* stdout_monitor = new eoStdoutMonitor();
// state.storeFunctor(stdout_monitor);
// stdout_monitor->add(*distrib_stat);
// distribution_continue->add( *stdout_monitor );
eoFileMonitor* file_monitor = new eoFileMonitor("eda_sa_distribution_bounds.txt");
state.storeFunctor(file_monitor);
file_monitor->add(*distrib_stat);
distribution_continue->add( *file_monitor );
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// EDASA algorithm configuration
//-----------------------------------------------------------------------------
edoAlgo< Distrib >* algo = new edoEDASA< Distrib >
(*selector, *estimator, *selectone, *modifier, *sampler,
pop_continue, *distribution_continue,
eval, *sa_continue, *cooling_schedule,
initial_temperature, *replacor);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// Beginning of the algorithm call
//-----------------------------------------------------------------------------
try
{
do_run(*algo, pop);
}
catch (eoEvalFuncCounterBounderException& e)
{
eo::log << eo::warnings << "warning: " << e.what() << std::endl;
}
catch (std::exception& e)
{
eo::log << eo::errors << "error: " << e.what() << std::endl;
exit(EXIT_FAILURE);
}
//-----------------------------------------------------------------------------
return 0;
}

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#!/usr/bin/env sh
mkdir -p debug
cd debug
cmake -DCMAKE_BUILD_TYPE=Debug -DWITH_EIGEN=1 ..
make
cd ..

7
edo/build_gcc_linux_eigen_debug Executable file
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#!/usr/bin/env sh
mkdir -p debug
cd debug
cmake -DCMAKE_BUILD_TYPE=Debug -DWITH_EIGEN=1 ..
make
cd ..

9
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#!/usr/bin/env sh
mkdir -p release
cd release
cmake -DWITH_EIGEN=1 ..
#cmake -DWITH_BOOST=1 ..
make
cd ..

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# File: FindEO.cmake
# CMAKE commands to actually use the EO library
# Version: 0.0.1
#
# The following variables are filled out:
# - EO_INCLUDE_DIRS
# - EO_LIBRARY_DIRS
# - EO_LIBRARIES
# - EO_FOUND
#
# Here are the components:
# - PyEO
# - es
# - ga
# - cma
#
# You can use FIND_PACKAGE( EO COMPONENTS ... ) to enable one or several components.
#
# Default enabled components
SET(EO_LIBRARIES_TO_FIND eo eoutils)
# Use FIND_PACKAGE( EO COMPONENTS ... ) to enable modules
IF(EO_FIND_COMPONENTS)
FOREACH(component ${EO_FIND_COMPONENTS})
STRING(TOUPPER ${component} _COMPONENT)
SET(EO_USE_${_COMPONENT} 1)
ENDFOREACH(component)
# To make sure we don't use PyEO, ES, GA, CMA when not in COMPONENTS
IF(NOT EO_USE_PYEO)
SET(EO_DONT_USE_PYEO 1)
ELSE(NOT EO_USE_PYEO)
SET(EO_LIBRARIES_TO_FIND ${EO_LIBRARIES_TO_FIND} PyEO)
ENDIF(NOT EO_USE_PYEO)
IF(NOT EO_USE_ES)
SET(EO_DONT_USE_ES 1)
ELSE(NOT EO_USE_ES)
SET(EO_LIBRARIES_TO_FIND ${EO_LIBRARIES_TO_FIND} es)
ENDIF(NOT EO_USE_ES)
IF(NOT EO_USE_GA)
SET(EO_DONT_USE_GA 1)
ELSE(NOT EO_USE_GA)
SET(EO_LIBRARIES_TO_FIND ${EO_LIBRARIES_TO_FIND} ga)
ENDIF(NOT EO_USE_GA)
IF(NOT EO_USE_CMA)
SET(EO_DONT_USE_CMA 1)
ELSE(NOT EO_USE_CMA)
SET(EO_LIBRARIES_TO_FIND ${EO_LIBRARIES_TO_FIND} cma)
ENDIF(NOT EO_USE_CMA)
ENDIF(EO_FIND_COMPONENTS)
IF(NOT EO_INCLUDE_DIRS)
FIND_PATH(
EO_INCLUDE_DIRS
EO.h
PATHS
/usr/include/eo
/usr/local/include/eo
)
ENDIF(NOT EO_INCLUDE_DIRS)
IF(NOT EO_LIBRARY_DIRS)
FIND_PATH(
EO_LIBRARY_DIRS
libeo.a
PATHS
/usr/lib
/usr/local/lib
)
ENDIF(NOT EO_LIBRARY_DIRS)
IF(NOT EO_LIBRARIES)
SET(EO_LIBRARIES)
FOREACH(component ${EO_LIBRARIES_TO_FIND})
FIND_LIBRARY(
EO_${component}_LIBRARY
NAMES ${component}
PATHS
/usr/lib
/usr/local/lib
)
IF(EO_${component}_LIBRARY)
SET(EO_LIBRARIES ${EO_LIBRARIES} ${EO_${component}_LIBRARY})
ELSE(EO_${component}_LIBRARY)
MESSAGE(FATAL_ERROR "${component} component not found.")
ENDIF(EO_${component}_LIBRARY)
ENDFOREACH(component)
ENDIF(NOT EO_LIBRARIES)
IF(EO_INCLUDE_DIRS AND EO_LIBRARY_DIRS AND EO_LIBRARIES)
SET(EO_FOUND 1)
MARK_AS_ADVANCED(EO_FOUND)
MARK_AS_ADVANCED(EO_INCLUDE_DIRS)
MARK_AS_ADVANCED(EO_LIBRARY_DIRS)
MARK_AS_ADVANCED(EO_LIBRARIES)
ENDIF(EO_INCLUDE_DIRS AND EO_LIBRARY_DIRS AND EO_LIBRARIES)

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#!/usr/bin/env sh
rm -rf debug
rm -rf release

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#######################################################################################
### Doc generation using Doxygen
#######################################################################################
IF (DOXYGEN_FOUND)
SET(DOC_DIR ${CMAKE_BINARY_DIR}/doc CACHE PATH "documentation directory")
SET(DOC_CONFIG_FILE "doxyfile" CACHE PATH "documentation configuration file")
# define the doc target
IF (DOXYGEN_EXECUTABLE)
ADD_CUSTOM_TARGET(doc
COMMAND ${DOXYGEN_EXECUTABLE} ${DOC_CONFIG_FILE}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
)
ENDIF (DOXYGEN_EXECUTABLE)
# configure doxyfile file
CONFIGURE_FILE(
"${CMAKE_CURRENT_SOURCE_DIR}/${DOC_CONFIG_FILE}.cmake"
"${CMAKE_CURRENT_BINARY_DIR}/${DOC_CONFIG_FILE}"
)
INSTALL(
DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
DESTINATION share/edo COMPONENT libraries
PATTERN "CMakeFiles" EXCLUDE
PATTERN "cmake_install.cmake" EXCLUDE
PATTERN "Makefile" EXCLUDE
PATTERN "doxyfile" EXCLUDE
)
ELSE (DOXYGEN_FOUND)
MESSAGE(STATUS "Unable to generate the documentation, Doxygen package not found")
ENDIF (DOXYGEN_FOUND)
#######################################################################################

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/** @mainpage Welcome to Evolving Distribution Objects
@section shortcuts In one word
%EDO is an extension of %EO oriented toward Estimation-of-Distribution-like
Algorithms.
You can search for advanced features by browsing the <a
href="modules.html">modules</a> page.
@section intro Introduction
%EDO is an extension of %EO, that facilitate the design and implementation of
stochastic search metaheuristics. It is based on the assumption that those
algorithms are updating a probability distribution, that is used to generate
a sample (a population, in %EO) of solutions (individuals, in %EO).
Basically, EDO decompose the <em>variation</em> operators of %EO in a set of
sub-operators that are binded by a <em>distribution</em>. Thus, most of the
representation-independent operators of %EO can be used in %EDO algorithms.
Apart from choosing which distribution he want to use as a model, the user is
not supposed to directly manipulate it. Using the same approach than within %EO,
the user has just to indicate what he want to use, without having to bother how
he want to use it.
On the designer side, it is still possible to implement specific operators
without having to change other ones.
<img src="edo_design.png" />
The two main operators are the <em>Estimators</em>, that builds a given
distribution according to a population and the <em>Samplers</em> that builds a
population according to a distribution. There is also <em>Modifiers</em> that
are here to change arbitrarily the parameters of a distribution, if necessary.
<img src="edo_distrib.png" />
*/

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# Package Information for pkg-config
prefix=/usr
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir=${prefix}/include/edo
Name: Evolving Distribution Objects
Description: Evolving Distribution Objects
Version: 1.0
Libs: -L${libdir} -ledo -ledoutils
Cflags: -I${includedir}

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# Variables to set
# directory we need to build project
SET(EO_DIR "<<PATH_TO_EO>>" CACHE PATH "EO directory" FORCE)
# automagically set parameters, do not edit
SET(EO_INCLUDE_DIRS "${EO_DIR}/src" CACHE PATH "EO include directory" FORCE)
SET(EO_LIBRARY_DIRS "${EO_DIR}/release/lib" CACHE PATH "EO library directory" FORCE)
SET(EO_LIBRARIES eoutils eo es ga cma gcov) # do not use quotes around this list or it will fail

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#!/usr/bin/env sh
cd release
cpack -G DEB
cd ..

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#!/usr/bin/env sh
cd release
cpack -G RPM
cd ..

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######################################################################################
### 1) Set all needed source files for the project
######################################################################################
FILE(GLOB HDRS *.h edo)
INSTALL(FILES ${HDRS} DESTINATION include/edo COMPONENT headers)
FILE(GLOB SOURCES *.cpp)
SET(SAMPLE_SRCS ${SOURCES} PARENT_SCOPE)
######################################################################################
######################################################################################
### 2) Where must cmake go now ?
######################################################################################
ADD_SUBDIRECTORY(utils)
######################################################################################

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edo_
#define _edo_
#include "edoAlgo.h"
//#include "edoEDASA.h"
#include "edoAlgoAdaptive.h"
#include "edoAlgoStateless.h"
#include "edoDistrib.h"
#include "edoUniform.h"
#include "edoNormalMono.h"
#include "edoNormalMulti.h"
#include "edoNormalAdaptive.h"
#include "edoEstimator.h"
#include "edoEstimatorUniform.h"
#include "edoEstimatorNormalMono.h"
#include "edoEstimatorNormalMulti.h"
#include "edoEstimatorAdaptive.h"
#include "edoEstimatorNormalAdaptive.h"
#include "edoModifier.h"
#include "edoModifierDispersion.h"
#include "edoModifierMass.h"
#include "edoUniformCenter.h"
#include "edoNormalMonoCenter.h"
#include "edoNormalMultiCenter.h"
#include "edoSampler.h"
#include "edoSamplerUniform.h"
#include "edoSamplerNormalMono.h"
#include "edoSamplerNormalMulti.h"
#include "edoSamplerNormalAdaptive.h"
#include "edoVectorBounds.h"
#include "edoRepairer.h"
#include "edoRepairerDispatcher.h"
#include "edoRepairerRound.h"
#include "edoRepairerModulo.h"
#include "edoBounder.h"
#include "edoBounderNo.h"
#include "edoBounderBound.h"
#include "edoBounderRng.h"
#include "edoBounderUniform.h"
#include "edoContinue.h"
#include "utils/edoCheckPoint.h"
#include "utils/edoStat.h"
#include "utils/edoStatUniform.h"
#include "utils/edoStatNormalMono.h"
#include "utils/edoStatNormalMulti.h"
#include "utils/edoFileSnapshot.h"
#include "utils/edoPopStat.h"
#endif // !_edo_
// Local Variables:
// mode: C++
// End:

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Caner Candan <caner.candan@thalesgroup.com>
*/
#include "edo"

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoAlgo_h
#define _edoAlgo_h
#include <eoAlgo.h>
/**
@defgroup Algorithms Algorithms
In EDO, as in EO, an algorithm is a functor that takes one or several
solutions to an optimization problem as arguments, and iteratively modify
them with the help of operators.It differs from a canonical EO algorithm
because it is templatized on a edoDistrib rather than just an EOT.
@see eoAlgo
*/
/** An EDO algorithm differs from a canonical EO algorithm because it is
* templatized on a Distribution rather than just an EOT.
*
* Derivating from an eoAlgo, it should define an operator()( EOT sol )
*
* @ingroup Algorithms
*/
template < typename D >
class edoAlgo : public eoAlgo< typename D::EOType >
{
//! Alias for the type
typedef typename D::EOType EOType;
// virtual R operator()(A1) = 0; (defined in eoUF)
public:
virtual ~edoAlgo(){}
};
#endif // !_edoAlgo_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoAlgoAdaptive_h
#define _edoAlgoAdaptive_h
#include <eo>
#include <utils/eoRNG.h>
#include "edoAlgo.h"
#include "edoEstimator.h"
#include "edoModifierMass.h"
#include "edoSampler.h"
#include "edoContinue.h"
/** A generic stochastic search template for algorithms that need a distribution parameter.
*
* An adaptive algorithm will directly updates a distribution, it must thus be instanciated
* with an edoDistrib at hand. Thus, this distribution object should be instanciated appart.
* The reference to this distribution is generally also needed by at least one of the
* algorithm's operator, generally for algorithms that shares the same algorithms across
* operators and/or iterations.
*
* If you no operator needs to update the distribution, then it is simpler to use an
* edoAlgoStateless .
*
* @ingroup Algorithms
*/
template < typename D >
class edoAlgoAdaptive : public edoAlgo< D >
{
public:
//! Alias for the type EOT
typedef typename D::EOType EOType;
//! Alias for the atom type
typedef typename EOType::AtomType AtomType;
//! Alias for the fitness
typedef typename EOType::Fitness Fitness;
public:
/*!
Takes algo operators, all are mandatory
\param distrib A distribution to use, if you want to update this parameter (e.gMA-ES) instead of replacing it (e.g. an EDA)
\param evaluator Evaluate a population
\param selector Selection of the best candidate solutions in the population
\param estimator Estimation of the distribution parameters
\param sampler Generate feasible solutions using the distribution
\param replacor Replace old solutions by new ones
\param pop_continuator Stopping criterion based on the population features
\param distribution_continuator Stopping criterion based on the distribution features
*/
edoAlgoAdaptive(
D & distrib,
eoPopEvalFunc < EOType > & evaluator,
eoSelect< EOType > & selector,
edoEstimator< D > & estimator,
edoSampler< D > & sampler,
eoReplacement< EOType > & replacor,
eoContinue< EOType > & pop_continuator,
edoContinue< D > & distribution_continuator
) :
_distrib(distrib),
_evaluator(evaluator),
_selector(selector),
_estimator(estimator),
_sampler(sampler),
_replacor(replacor),
_pop_continuator(pop_continuator),
_dummy_continue(),
_distribution_continuator(distribution_continuator)
{}
//! constructor without an edoContinue
/*!
Takes algo operators, all are mandatory
\param distrib A distribution to use, if you want to update this parameter (e.gMA-ES) instead of replacing it (e.g. an EDA)
\param evaluator Evaluate a population
\param selector Selection of the best candidate solutions in the population
\param estimator Estimation of the distribution parameters
\param sampler Generate feasible solutions using the distribution
\param replacor Replace old solutions by new ones
\param pop_continuator Stopping criterion based on the population features
*/
edoAlgoAdaptive (
D & distrib,
eoPopEvalFunc < EOType > & evaluator,
eoSelect< EOType > & selector,
edoEstimator< D > & estimator,
edoSampler< D > & sampler,
eoReplacement< EOType > & replacor,
eoContinue< EOType > & pop_continuator
) :
_distrib( distrib ),
_evaluator(evaluator),
_selector(selector),
_estimator(estimator),
_sampler(sampler),
_replacor(replacor),
_pop_continuator(pop_continuator),
_dummy_continue(),
_distribution_continuator( _dummy_continue )
{}
/** Call the algorithm
*
* \param pop the population of candidate solutions
* \return void
*/
void operator ()(eoPop< EOType > & pop)
{
assert(pop.size() > 0);
eoPop< EOType > current_pop;
eoPop< EOType > selected_pop;
// update the extern distribution passed to the estimator (cf. CMA-ES)
// OR replace the dummy distribution for estimators that do not need extern distributions (cf. EDA)
_distrib = _estimator(pop);
// Evaluating a first time the candidate solutions
// The first pop is not supposed to be evaluated (@see eoPopLoopEval).
// _evaluator( current_pop, pop );
do {
// (1) Selection of the best points in the population
_selector(pop, selected_pop);
assert( selected_pop.size() > 0 );
// (2) Estimation of the distribution parameters
_distrib = _estimator(selected_pop);
// (3) sampling
// The sampler produces feasible solutions (@see edoSampler that
// encapsulate an edoBounder)
current_pop.clear();
for( unsigned int i = 0; i < pop.size(); ++i ) {
current_pop.push_back( _sampler(_distrib) );
}
// (4) Evaluate new solutions
_evaluator( pop, current_pop );
// (5) Replace old solutions by new ones
_replacor(pop, current_pop); // e.g. copy current_pop in pop
} while( _distribution_continuator( _distrib ) && _pop_continuator( pop ) );
} // operator()
protected:
//! The distribution that you want to update
D & _distrib;
//! A full evaluation function.
eoPopEvalFunc<EOType> & _evaluator;
//! A EOType selector
eoSelect<EOType> & _selector;
//! A EOType estimator. It is going to estimate distribution parameters.
edoEstimator<D> & _estimator;
//! A D sampler
edoSampler<D> & _sampler;
//! A EOType replacor
eoReplacement<EOType> & _replacor;
//! A EOType population continuator
eoContinue<EOType> & _pop_continuator;
//! A D continuator that always return true
edoDummyContinue<D> _dummy_continue;
//! A D continuator
edoContinue<D> & _distribution_continuator;
};
#endif // !_edoAlgoAdaptive_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoAlgoStateless_h
#define _edoAlgoStateless_h
#include "edoAlgoAdaptive.h"
/** A generic stochastic search template for algorithms that need a distribution parameter but replace it rather than update it
*
* This use a default dummy distribution, for algorithms willing to replace it instead of updating
* Thus we can instanciate _distrib on this and replace it at the first iteration with an estimator.
* This is why an edoDistrib must have an empty constructor.
*
* @ingroup Algorithms
*/
template < typename D >
class edoAlgoStateless : public edoAlgoAdaptive< D >
{
public:
//! Alias for the type EOT
typedef typename D::EOType EOType;
//! Alias for the atom type
typedef typename EOType::AtomType AtomType;
//! Alias for the fitness
typedef typename EOType::Fitness Fitness;
public:
/** Full constructor
\param evaluator Evaluate a population
\param selector Selection of the best candidate solutions in the population
\param estimator Estimation of the distribution parameters
\param sampler Generate feasible solutions using the distribution
\param replacor Replace old solutions by new ones
\param pop_continuator Stopping criterion based on the population features
\param distribution_continuator Stopping criterion based on the distribution features
*/
edoAlgoStateless(
eoPopEvalFunc < EOType > & evaluator,
eoSelect< EOType > & selector,
edoEstimator< D > & estimator,
edoSampler< D > & sampler,
eoReplacement< EOType > & replacor,
eoContinue< EOType > & pop_continuator,
edoContinue< D > & distribution_continuator
) :
edoAlgoAdaptive<D>( *(new D), evaluator, selector, estimator, sampler, replacor, pop_continuator, distribution_continuator)
{}
/** Constructor without an edoContinue
\param evaluator Evaluate a population
\param selector Selection of the best candidate solutions in the population
\param estimator Estimation of the distribution parameters
\param sampler Generate feasible solutions using the distribution
\param replacor Replace old solutions by new ones
\param pop_continuator Stopping criterion based on the population features
*/
edoAlgoStateless (
eoPopEvalFunc < EOType > & evaluator,
eoSelect< EOType > & selector,
edoEstimator< D > & estimator,
edoSampler< D > & sampler,
eoReplacement< EOType > & replacor,
eoContinue< EOType > & pop_continuator
) :
edoAlgoAdaptive<D>( *(new D), evaluator, selector, estimator, sampler, replacor, pop_continuator)
{}
~edoAlgoStateless()
{
// delete the temporary distrib allocated in constructors
delete &(this->_distrib);
}
};
#endif // !_edoAlgoStateless_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoBounder_h
#define _edoBounder_h
#include <edoRepairer.h>
/** The interface of a set of classes that modifies a solution so as to respect
* a given set of bounds (typically an hypercube).
*
* @ingroup Repairers
* @ingroup Core
*/
template < typename EOT >
class edoBounder : public edoRepairer< EOT >
{
public:
edoBounder()
{}
edoBounder( EOT min/* = EOT(1, 0)*/, EOT max/* = EOT(1, 1)*/ )
: _min(min), _max(max)
{
assert(_min.size() > 0);
assert(_min.size() == _max.size());
}
// virtual void operator()( EOT& ) = 0 (provided by eoUF< A1, R >)
EOT& min(){return _min;}
EOT& max(){return _max;}
private:
EOT _min;
EOT _max;
};
#endif // !_edoBounder_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoBounderBound_h
#define _edoBounderBound_h
#include "edoBounder.h"
/** A bounder that correct an incorrect variable by setting it to the min/max
*
* @ingroup Repairers
*/
template < typename EOT >
class edoBounderBound : public edoBounder< EOT >
{
public:
edoBounderBound( EOT min, EOT max )
: edoBounder< EOT >( min, max )
{}
void operator()( EOT& x )
{
unsigned int size = x.size();
assert(size > 0);
for (unsigned int d = 0; d < size; ++d) // browse all dimensions
{
if (x[d] < this->min()[d])
{
x[d] = this->min()[d];
continue;
}
if (x[d] > this->max()[d])
{
x[d] = this->max()[d];
}
}
}
};
#endif // !_edoBounderBound_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoBounderNo_h
#define _edoBounderNo_h
#include "edoBounder.h"
/** A bounder that does nothing.
*
* @ingroup Repairers
*/
template < typename EOT >
class edoBounderNo : public edoBounder< EOT >
{
public:
void operator()( EOT& ) {}
};
#endif // !_edoBounderNo_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoBounderRng_h
#define _edoBounderRng_h
#include "edoBounder.h"
/** A bounder that randomly draw new values for variables going out bounds,
* using an eoRng to do so.
*
* @ingroup Repairers
*/
template < typename EOT >
class edoBounderRng : public edoBounder< EOT >
{
public:
edoBounderRng( EOT min, EOT max, eoRndGenerator< double > & rng )
: edoBounder< EOT >( min, max ), _rng(rng)
{}
void operator()( EOT& x )
{
unsigned int size = x.size();
assert(size > 0);
for (unsigned int d = 0; d < size; ++d) // browse all dimensions
{
// FIXME: attention: les bornes RNG ont les memes bornes quelque soit les dimensions idealement on voudrait avoir des bornes differentes pour chaque dimensions.
if (x[d] < this->min()[d] || x[d] > this->max()[d])
{
x[d] = _rng();
}
}
}
private:
eoRndGenerator< double> & _rng;
};
#endif // !_edoBounderRng_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
*/
#ifndef _edoBounderUniform_h
#define _edoBounderUniform_h
#include "edoBounder.h"
/** A bounder that randomly draw new values for variables going out bounds,
* in a given uniform distribution.
*
* @ingroup Repairers
*/
template < typename EOT >
class edoBounderUniform : public edoBounder< EOT >
{
public:
edoBounderUniform( EOT min, EOT max )
: edoBounder< EOT >( min, max )
{
}
void operator()( EOT& sol )
{
assert( this->min().size() > 0 );
assert( this->max().size() > 0 );
assert( sol.size() > 0);
assert( sol.size() == this->min().size() );
eo::log << eo::debug << "BounderUniform: from sol = " << sol;
eo::log.flush();
unsigned int size = sol.size();
for (unsigned int d = 0; d < size; ++d) {
if ( sol[d] < this->min()[d] || sol[d] > this->max()[d]) {
// use EO's global "rng"
sol[d] = rng.uniform( this->min()[d], this->max()[d] );
}
} // for d in size
eo::log << eo::debug << "\tto sol = " << sol << std::endl;
}
};
#endif // !_edoBounderUniform_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _doContinue_h
#define _doContinue_h
#include <eoFunctor.h>
#include <eoPersistent.h>
/** A continuator that check the state of an edoDistrib
*
* @see eoContinue
*
* @ingroup Continuators
* @ingroup Core
*/
template < typename D >
class edoContinue : public eoUF< const D&, bool >, public eoPersistent
{
public:
virtual std::string className(void) const { return "edoContinue"; }
void readFrom(std::istream&)
{
/* It should be implemented by subclasses ! */
}
void printOn(std::ostream&) const
{
/* It should be implemented by subclasses ! */
}
};
template < typename D >
class edoDummyContinue : public edoContinue< D >
{
bool operator()(const D&){ return true; }
virtual std::string className() const { return "edoDummyContinue"; }
};
#endif // !_edoContinue_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoDistrib_h
#define _edoDistrib_h
#include <eoFunctor.h>
/** @defgroup Core
*
* Core functors that made the basis of EDO.
*/
/** @defgroup Distributions Distributions
*
* A distribution is a data structure that holds sufficient informations to
* describe a probability density function by a set of parameters.
*
* It is passed across EDO operators and can be updated or manipulated by them.
*/
/** Base class for distributions. This is really just an empty shell.
*
* @ingroup Distributions
* @ingroup Core
*/
template < typename EOT >
class edoDistrib : public eoFunctorBase
{
public:
//! Alias for the type
typedef EOT EOType;
virtual ~edoDistrib(){}
};
#endif // !_edoDistrib_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoEDASA_h
#define _edoEDASA_h
#include <eo>
//#include <mo>
#include <utils/eoRNG.h>
#include "edoAlgo.h"
#include "edoEstimator.h"
#include "edoModifierMass.h"
#include "edoSampler.h"
#include "edoContinue.h"
//! edoEDASA< D >
template < typename D >
class edoEDASA : public edoAlgo< D >
{
public:
//! Alias for the type EOT
typedef typename D::EOType EOType;
//! Alias for the atom type
typedef typename EOType::AtomType AtomType;
//! Alias for the fitness
typedef typename EOType::Fitness Fitness;
public:
//! edoEDASA constructor
/*!
All the boxes used by a EDASA need to be given.
\param selector Population Selector
\param estimator Distribution Estimator
\param selectone SelectOne
\param modifier Distribution Modifier
\param sampler Distribution Sampler
\param pop_continue Population Continuator
\param distribution_continue Distribution Continuator
\param evaluation Evaluation function.
\param sa_continue Stopping criterion.
\param cooling_schedule Cooling schedule, describes how the temperature is modified.
\param initial_temperature The initial temperature.
\param replacor Population replacor
*/
edoEDASA (eoSelect< EOType > & selector,
edoEstimator< D > & estimator,
eoSelectOne< EOType > & selectone,
edoModifierMass< D > & modifier,
edoSampler< D > & sampler,
eoContinue< EOType > & pop_continue,
edoContinue< D > & distribution_continue,
eoEvalFunc < EOType > & evaluation,
moContinuator< moDummyNeighbor<EOType> > & sa_continue,
moCoolingSchedule<EOType> & cooling_schedule,
double initial_temperature,
eoReplacement< EOType > & replacor
)
: _selector(selector),
_estimator(estimator),
_selectone(selectone),
_modifier(modifier),
_sampler(sampler),
_pop_continue(pop_continue),
_distribution_continue(distribution_continue),
_evaluation(evaluation),
_sa_continue(sa_continue),
_cooling_schedule(cooling_schedule),
_initial_temperature(initial_temperature),
_replacor(replacor)
{}
//! function that launches the EDASA algorithm.
/*!
As a moTS or a moHC, the EDASA can be used for HYBRIDATION in an evolutionary algorithm.
\param pop A population to improve.
\return TRUE.
*/
void operator ()(eoPop< EOType > & pop)
{
assert(pop.size() > 0);
double temperature = _initial_temperature;
eoPop< EOType > current_pop;
eoPop< EOType > selected_pop;
//-------------------------------------------------------------
// Estimating a first time the distribution parameter thanks
// to population.
//-------------------------------------------------------------
D distrib = _estimator(pop);
double size = distrib.size();
assert(size > 0);
//-------------------------------------------------------------
do
{
//-------------------------------------------------------------
// (3) Selection of the best points in the population
//-------------------------------------------------------------
selected_pop.clear();
_selector(pop, selected_pop);
assert( selected_pop.size() > 0 );
//-------------------------------------------------------------
//-------------------------------------------------------------
// (4) Estimation of the distribution parameters
//-------------------------------------------------------------
distrib = _estimator(selected_pop);
//-------------------------------------------------------------
// TODO: utiliser selected_pop ou pop ???
assert(selected_pop.size() > 0);
//-------------------------------------------------------------
// Init of a variable contening a point with the bestest fitnesses
//-------------------------------------------------------------
EOType current_solution = _selectone(selected_pop);
//-------------------------------------------------------------
//-------------------------------------------------------------
// Fit the current solution with the distribution parameters (bounds)
//-------------------------------------------------------------
// FIXME: si besoin de modifier la dispersion de la distribution
// _modifier_dispersion(distribution, selected_pop);
_modifier(distrib, current_solution);
//-------------------------------------------------------------
//-------------------------------------------------------------
// Evaluating a first time the current solution
//-------------------------------------------------------------
_evaluation( current_solution );
//-------------------------------------------------------------
//-------------------------------------------------------------
// Building of the sampler in current_pop
//-------------------------------------------------------------
_sa_continue.init( current_solution );
current_pop.clear();
do
{
EOType candidate_solution = _sampler(distrib);
_evaluation( candidate_solution );
// TODO: verifier le critere d'acceptation
if ( candidate_solution.fitness() < current_solution.fitness() ||
rng.uniform() < exp( ::fabs(candidate_solution.fitness() - current_solution.fitness()) / temperature ) )
{
current_pop.push_back(candidate_solution);
current_solution = candidate_solution;
}
}
while ( _sa_continue( current_solution ) );
//-------------------------------------------------------------
_replacor(pop, current_pop); // copy current_pop in pop
pop.sort();
if ( ! _cooling_schedule( temperature ) ){ eo::log << eo::debug << "_cooling_schedule" << std::endl; break; }
if ( ! _distribution_continue( distrib ) ){ eo::log << eo::debug << "_distribution_continue" << std::endl; break; }
if ( ! _pop_continue( pop ) ){ eo::log << eo::debug << "_pop_continue" << std::endl; break; }
}
while ( 1 );
}
private:
//! A EOType selector
eoSelect < EOType > & _selector;
//! A EOType estimator. It is going to estimate distribution parameters.
edoEstimator< D > & _estimator;
//! SelectOne
eoSelectOne< EOType > & _selectone;
//! A D modifier
edoModifierMass< D > & _modifier;
//! A D sampler
edoSampler< D > & _sampler;
//! A EOType population continuator
eoContinue < EOType > & _pop_continue;
//! A D continuator
edoContinue < D > & _distribution_continue;
//! A full evaluation function.
eoEvalFunc < EOType > & _evaluation;
//! Stopping criterion before temperature update
moContinuator< moDummyNeighbor<EOType> > & _sa_continue;
//! The cooling schedule
moCoolingSchedule<EOType> & _cooling_schedule;
//! Initial temperature
double _initial_temperature;
//! A EOType replacor
eoReplacement < EOType > & _replacor;
};
#endif // !_edoEDASA_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoEstimator_h
#define _edoEstimator_h
#include <eoPop.h>
#include <eoFunctor.h>
/** @defgroup Estimators Estimators
*
* Estimators takes an eoPop and estimates the parameters of a distributions
* (defined as an hypothesis) from it.
*/
/** Base class for estimators.
*
* Estimators takes an eoPop and estimates the parameters of a distributions
* (defined as an hypothesis) from it.
*
* @ingroup Estimators
* @ingroup Core
*/
template < typename D >
class edoEstimator : public eoUF< eoPop< typename D::EOType >&, D >
{
public:
typedef typename D::EOType EOType;
// virtual D operator() ( eoPop< EOT >& )=0 (provided by eoUF< A1, R >)
};
#endif // !_edoEstimator_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoEstimatorAdaptive_h
#define _edoEstimatorAdaptive_h
#include <eoPop.h>
#include <eoFunctor.h>
#include "edoEstimator.h"
/** An interface that explicits the needs for a permanent distribution
* that will be updated by operators.
*
* @ingroup Estimators
* @ingroup Core
*/
template < typename D >
class edoEstimatorAdaptive : public edoEstimator<D>
{
public:
typedef typename D::EOType EOType;
edoEstimatorAdaptive<D>( D& distrib ) : _distrib(distrib) {}
// virtual D operator() ( eoPop< EOT >& )=0 (provided by eoUF< A1, R >)
D & distribution() const { return _distrib; }
protected:
D & _distrib;
};
#endif // !_edoEstimatorAdaptive_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoEstimatorNormalAdaptive_h
#define _edoEstimatorNormalAdaptive_h
#ifdef WITH_EIGEN
#include <algorithm>
#include<Eigen/Dense>
#include "edoNormalAdaptive.h"
#include "edoEstimatorAdaptive.h"
/** An estimator that works on adaptive normal distributions, basically the heart of the CMA-ES algorithm.
*
* @ingroup Estimators
* @ingroup CMAES
* @ingroup Adaptivenormal
*/
template< typename EOT, typename D = edoNormalAdaptive<EOT> >
class edoEstimatorNormalAdaptive : public edoEstimatorAdaptive< D >
{
public:
typedef typename EOT::AtomType AtomType;
typedef typename D::Vector Vector; // column vectors @see edoNormalAdaptive
typedef typename D::Matrix Matrix;
edoEstimatorNormalAdaptive( D& distrib ) :
edoEstimatorAdaptive<D>( distrib ),
_calls(0),
_eigeneval(0)
{}
private:
Eigen::VectorXd edoCMAESweights( unsigned int pop_size )
{
// compute recombination weights
Eigen::VectorXd weights( pop_size );
double sum_w = 0;
for( unsigned int i = 0; i < pop_size; ++i ) {
double w_i = log( pop_size + 0.5 ) - log( i + 1 );
weights(i) = w_i;
sum_w += w_i;
}
// normalization of weights
weights /= sum_w;
assert( weights.size() == pop_size);
return weights;
}
public:
void resetCalls()
{
_calls = 0;
}
// update the distribution reference this->distribution()
edoNormalAdaptive<EOT> operator()( eoPop<EOT>& pop )
{
/**********************************************************************
* INITIALIZATION
*********************************************************************/
unsigned int N = pop[0].size(); // FIXME expliciter la dimension du pb ?
unsigned int lambda = pop.size();
// number of calls to the operator == number of generations
_calls++;
// number of "evaluations" until now
unsigned int counteval = _calls * lambda;
// Here, if we are in canonical CMA-ES,
// pop is supposed to be the mu ranked better solutions,
// as the rank mu selection is supposed to have occured.
Matrix arx( N, lambda );
// copy the pop (most probably a vector of vectors) in a Eigen3 matrix
for( unsigned int d = 0; d < N; ++d ) {
for( unsigned int i = 0; i < lambda; ++i ) {
arx(d,i) = pop[i][d]; // NOTE: pop = arx.transpose()
} // dimensions
} // individuals
// muXone array for weighted recombination
Eigen::VectorXd weights = edoCMAESweights( lambda );
assert( weights.size() == lambda );
// FIXME exposer les constantes dans l'interface
// variance-effectiveness of sum w_i x_i
double mueff = pow(weights.sum(), 2) / (weights.array().square()).sum();
// time constant for cumulation for C
double cc = (4+mueff/N) / (N+4 + 2*mueff/N);
// t-const for cumulation for sigma control
double cs = (mueff+2) / (N+mueff+5);
// learning rate for rank-one update of C
double c1 = 2 / (pow(N+1.3,2)+mueff);
// and for rank-mu update
double cmu = 2 * (mueff-2+1/mueff) / ( pow(N+2,2)+mueff);
// damping for sigma
double damps = 1 + 2*std::max(0.0, sqrt((mueff-1)/(N+1))-1) + cs;
// shortcut to the referenced distribution
D& d = this->distribution();
// C^-1/2
Matrix invsqrtC =
d.coord_sys() * d.scaling().asDiagonal().inverse()
* d.coord_sys().transpose();
assert( invsqrtC.innerSize() == d.coord_sys().innerSize() );
assert( invsqrtC.outerSize() == d.coord_sys().outerSize() );
// expectation of ||N(0,I)|| == norm(randn(N,1))
double chiN = sqrt(N)*(1-1/(4*N)+1/(21*pow(N,2)));
/**********************************************************************
* WEIGHTED MEAN
*********************************************************************/
// compute weighted mean into xmean
Vector xold = d.mean();
assert( xold.size() == N );
// xmean ( N, 1 ) = arx( N, lambda ) * weights( lambda, 1 )
Vector xmean = arx * weights;
assert( xmean.size() == N );
d.mean( xmean );
/**********************************************************************
* CUMULATION: UPDATE EVOLUTION PATHS
*********************************************************************/
// cumulation for sigma
d.path_sigma(
(1.0-cs)*d.path_sigma() + sqrt(cs*(2.0-cs)*mueff)*invsqrtC*(xmean-xold)/d.sigma()
);
// sign of h
double hsig;
if( d.path_sigma().norm()/sqrt(1.0-pow((1.0-cs),(2.0*counteval/lambda)))/chiN
< 1.4 + 2.0/(N+1.0)
) {
hsig = 1.0;
} else {
hsig = 0.0;
}
// cumulation for the covariance matrix
d.path_covar(
(1.0-cc)*d.path_covar() + hsig*sqrt(cc*(2.0-cc)*mueff)*(xmean-xold) / d.sigma()
);
Matrix xmu( N, lambda);
xmu = xold.rowwise().replicate(lambda);
assert( xmu.innerSize() == N );
assert( xmu.outerSize() == lambda );
Matrix artmp = (1.0/d.sigma()) * (arx - xmu);
// Matrix artmp = (1.0/d.sigma()) * arx - xold.colwise().replicate(lambda);
assert( artmp.innerSize() == N && artmp.outerSize() == lambda );
/**********************************************************************
* COVARIANCE MATRIX ADAPTATION
*********************************************************************/
d.covar(
(1-c1-cmu) * d.covar() // regard old matrix
+ c1 * (d.path_covar()*d.path_covar().transpose() // plus rank one update
+ (1-hsig) * cc*(2-cc) * d.covar()) // minor correction if hsig==0
+ cmu * artmp * weights.asDiagonal() * artmp.transpose() // plus rank mu update
);
// Adapt step size sigma
d.sigma( d.sigma() * exp((cs/damps)*(d.path_sigma().norm()/chiN - 1)) );
/**********************************************************************
* DECOMPOSITION OF THE COVARIANCE MATRIX
*********************************************************************/
// Decomposition of C into B*diag(D.^2)*B' (diagonalization)
if( counteval - _eigeneval > lambda/(c1+cmu)/N/10 ) { // to achieve O(N^2)
_eigeneval = counteval;
// enforce symmetry of the covariance matrix
Matrix C = d.covar();
// FIXME edoEstimatorNormalAdaptive.h:213:44: erreur: expected primary-expression before ‘)’ token
// copy the upper part in the lower one
//C.triangularView<Eigen::Lower>() = C.adjoint();
// Matrix CS = C.triangularView<Eigen::Upper>() + C.triangularView<Eigen::StrictlyUpper>().transpose();
d.covar( C );
Eigen::SelfAdjointEigenSolver<Matrix> eigensolver( d.covar() ); // FIXME use JacobiSVD?
d.coord_sys( eigensolver.eigenvectors() );
Matrix mD = eigensolver.eigenvalues().asDiagonal();
assert( mD.innerSize() == N && mD.outerSize() == N );
// from variance to standard deviations
mD.cwiseSqrt();
d.scaling( mD.diagonal() );
}
return d;
} // operator()
protected:
unsigned int _calls;
unsigned int _eigeneval;
// D & distribution() inherited from edoEstimatorAdaptive
};
#endif // WITH_EIGEN
#endif // !_edoEstimatorNormalAdaptive_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoEstimatorNormalMono_h
#define _edoEstimatorNormalMono_h
#include "edoEstimator.h"
#include "edoNormalMono.h"
/** An estimator for edoNormalMono
*
* @ingroup Estimators
* @ingroup Mononormal
*/
template < typename EOT >
class edoEstimatorNormalMono : public edoEstimator< edoNormalMono< EOT > >
{
public:
typedef typename EOT::AtomType AtomType;
//! Knuth's algorithm, online variance, numericably stable
class Variance
{
public:
Variance() : _n(0), _mean(0), _M2(0) {}
void update(AtomType x)
{
_n++;
AtomType delta = x - _mean;
_mean += delta / _n;
_M2 += delta * ( x - _mean );
}
AtomType mean() const {return _mean;}
//! Population variance
AtomType var_n() const {
assert( _n > 0 );
return _M2 / _n;
}
/** Sample variance (using Bessel's correction)
* is an unbiased estimate of the population variance,
* but it has uniformly higher mean squared error
*/
AtomType var() const {
assert( _n > 1 );
return _M2 / (_n - 1);
}
//! Population standard deviation
AtomType std_n() const {return sqrt( var_n() );}
//! Sample standard deviation, is a biased estimate of the population standard deviation
AtomType std() const {return sqrt( var() );}
private:
AtomType _n;
AtomType _mean;
AtomType _M2;
};
public:
edoNormalMono< EOT > operator()(eoPop<EOT>& pop)
{
unsigned int popsize = pop.size();
assert(popsize > 0);
unsigned int dimsize = pop[0].size();
assert(dimsize > 0);
std::vector< Variance > var( dimsize );
for (unsigned int i = 0; i < popsize; ++i)
{
for (unsigned int d = 0; d < dimsize; ++d)
{
var[d].update( pop[i][d] );
}
}
EOT mean( dimsize );
EOT variance( dimsize );
for (unsigned int d = 0; d < dimsize; ++d)
{
mean[d] = var[d].mean();
variance[d] = var[d].var_n();
}
return edoNormalMono< EOT >( mean, variance );
}
};
#endif // !_edoEstimatorNormalMono_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoEstimatorNormalMulti_h
#define _edoEstimatorNormalMulti_h
#include "edoEstimator.h"
#include "edoNormalMulti.h"
#ifdef WITH_BOOST
#include <boost/numeric/ublas/symmetric.hpp>
#include <boost/numeric/ublas/lu.hpp>
namespace ublas = boost::numeric::ublas;
#else
#ifdef WITH_EIGEN
#include <Eigen/Dense>
#endif // WITH_EIGEN
#endif // WITH_BOOST
/** An estimator for edoNormalMulti
*
* Exists in two implementations, using either
* <a href="http://www.boost.org/doc/libs/1_50_0/libs/numeric/ublas/doc/index.htm">Boost::uBLAS</a> (if compiled WITH_BOOST)
* or <a href="http://eigen.tuxfamily.org">Eigen3</a> (WITH_EIGEN).
*
* @ingroup Estimators
* @ingroup EMNA
* @ingroup Multinormal
*/
template < typename EOT, typename D=edoNormalMulti<EOT> >
class edoEstimatorNormalMulti : public edoEstimator<D>
{
#ifdef WITH_BOOST
public:
class CovMatrix
{
public:
typedef typename EOT::AtomType AtomType;
CovMatrix( const eoPop< EOT >& pop )
{
//-------------------------------------------------------------
// Some checks before starting to estimate covar
//-------------------------------------------------------------
unsigned int p_size = pop.size(); // population size
assert(p_size > 0);
unsigned int s_size = pop[0].size(); // solution size
assert(s_size > 0);
//-------------------------------------------------------------
//-------------------------------------------------------------
// Copy the population to an ublas matrix
//-------------------------------------------------------------
ublas::matrix< AtomType > sample( p_size, s_size );
for (unsigned int i = 0; i < p_size; ++i)
{
for (unsigned int j = 0; j < s_size; ++j)
{
sample(i, j) = pop[i][j];
}
}
//-------------------------------------------------------------
_varcovar.resize(s_size);
//-------------------------------------------------------------
// variance-covariance matrix are symmetric (and semi-definite
// positive), thus a triangular storage is sufficient
//
// variance-covariance matrix computation : transpose(A) * A
//-------------------------------------------------------------
ublas::symmetric_matrix< AtomType, ublas::lower > var = ublas::prod( ublas::trans( sample ), sample );
// Be sure that the symmetric matrix got the good size
assert(var.size1() == s_size);
assert(var.size2() == s_size);
assert(var.size1() == _varcovar.size1());
assert(var.size2() == _varcovar.size2());
//-------------------------------------------------------------
// TODO: to remove the comment below
// for (unsigned int i = 0; i < s_size; ++i)
// {
// // triangular LOWER matrix, thus j is not going further than i
// for (unsigned int j = 0; j <= i; ++j)
// {
// // we want a reducted covariance matrix
// _varcovar(i, j) = var(i, j) / p_size;
// }
// }
_varcovar = var / p_size;
_mean.resize(s_size); // FIXME: check if it is really used because of the assignation below
// unit vector
ublas::scalar_vector< AtomType > u( p_size, 1 );
// sum over columns
_mean = ublas::prod( ublas::trans( sample ), u );
// division by n
_mean /= p_size;
}
const ublas::symmetric_matrix< AtomType, ublas::lower >& get_varcovar() const {return _varcovar;}
const ublas::vector< AtomType >& get_mean() const {return _mean;}
private:
ublas::symmetric_matrix< AtomType, ublas::lower > _varcovar;
ublas::vector< AtomType > _mean;
};
public:
typedef typename EOT::AtomType AtomType;
edoNormalMulti< EOT > operator()(eoPop<EOT>& pop)
{
unsigned int popsize = pop.size();
assert(popsize > 0);
unsigned int dimsize = pop[0].size();
assert(dimsize > 0);
CovMatrix cov( pop );
return edoNormalMulti< EOT >( cov.get_mean(), cov.get_varcovar() );
}
};
#else
#ifdef WITH_EIGEN
public:
class CovMatrix
{
public:
typedef typename EOT::AtomType AtomType;
typedef typename D::Vector Vector;
typedef typename D::Matrix Matrix;
CovMatrix( const eoPop< EOT >& pop )
{
// Some checks before starting to estimate covar
unsigned int p_size = pop.size(); // population size
assert(p_size > 0);
unsigned int s_size = pop[0].size(); // solution size
assert(s_size > 0);
// Copy the population to an ublas matrix
Matrix sample( p_size, s_size );
for (unsigned int i = 0; i < p_size; ++i) {
for (unsigned int j = 0; j < s_size; ++j) {
sample(i, j) = pop[i][j];
}
}
// variance-covariance matrix are symmetric, thus a triangular storage is sufficient
// variance-covariance matrix computation : transpose(A) * A
Matrix var = sample.transpose() * sample;
// Be sure that the symmetric matrix got the good size
assert(var.innerSize() == s_size);
assert(var.outerSize() == s_size);
_varcovar = var / p_size;
// unit vector
Vector u( p_size);
u = Vector::Constant(p_size, 1);
// sum over columns
_mean = sample.transpose() * u;
// division by n
_mean /= p_size;
}
const Matrix& get_varcovar() const {return _varcovar;}
const Vector& get_mean() const {return _mean;}
private:
Matrix _varcovar;
Vector _mean;
};
public:
typedef typename EOT::AtomType AtomType;
edoNormalMulti< EOT > operator()(eoPop<EOT>& pop)
{
unsigned int p_size = pop.size();
assert(p_size > 0);
unsigned int s_size = pop[0].size();
assert(s_size > 0);
CovMatrix cov( pop );
assert( cov.get_mean().innerSize() == s_size );
assert( cov.get_mean().outerSize() == 1 );
assert( cov.get_varcovar().innerSize() == s_size );
assert( cov.get_varcovar().outerSize() == s_size );
return edoNormalMulti< EOT >( cov.get_mean(), cov.get_varcovar() );
}
#endif // WITH_EIGEN
#endif // WITH_BOOST
}; // class edoNormalMulti
#endif // !_edoEstimatorNormalMulti_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoEstimatorUniform_h
#define _edoEstimatorUniform_h
#include "edoEstimator.h"
#include "edoUniform.h"
/** An estimator for edoUniform
*
* @ingroup Estimators
*/
template < typename EOT >
class edoEstimatorUniform : public edoEstimator< edoUniform< EOT > >
{
public:
edoUniform< EOT > operator()(eoPop<EOT>& pop)
{
unsigned int size = pop.size();
assert(size > 0);
EOT min = pop[0];
EOT max = pop[0];
for (unsigned int i = 1; i < size; ++i)
{
unsigned int size = pop[i].size();
assert(size > 0);
// possibilité d'utiliser std::min_element et std::max_element mais exige 2 pass au lieu d'1.
for (unsigned int d = 0; d < size; ++d)
{
if (pop[i][d] < min[d])
min[d] = pop[i][d];
if (pop[i][d] > max[d])
max[d] = pop[i][d];
}
}
return edoUniform< EOT >(min, max);
}
};
#endif // !_edoEstimatorUniform_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoModifier_h
#define _edoModifier_h
/** @defgroup Modifiers
*
* A set of classes that arbitrarly modify a given distribution.
*/
/** A functor to arbitrarly modify a distribution
*
* @ingroup Core
* @ingroup Modifiers
*/
template < typename D >
class edoModifier
{
public:
virtual ~edoModifier(){}
typedef typename D::EOType EOType;
};
#endif // !_edoModifier_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoModifierDispersion_h
#define _edoModifierDispersion_h
#include <eoPop.h>
#include <eoFunctor.h>
#include "edoModifier.h"
/** An semantic pseudo-interface for modifiers that updates dispersion parameters (like variance).
*
* @ingroup Modifiers
*/
template < typename D >
class edoModifierDispersion : public edoModifier< D >, public eoBF< D&, eoPop< typename D::EOType >&, void >
{
public:
// virtual void operator() ( D&, eoPop< D::EOType >& )=0 (provided by eoBF< A1, A2, R >)
};
#endif // !_edoModifierDispersion_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoModifierMass_h
#define _edoModifierMass_h
#include <eoFunctor.h>
#include "edoModifier.h"
/** An semantic pseudo-interface for modifiers that updates mass parameters (like mean).
*
* @ingroup Modifiers
*/
template < typename D >
class edoModifierMass : public edoModifier< D >, public eoBF< D&, typename D::EOType&, void >
{
public:
//typedef typename D::EOType::AtomType AtomType; // does not work !!!
// virtual void operator() ( D&, D::EOType& )=0 (provided by eoBF< A1, A2, R >)
};
#endif // !_edoModifierMass_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dreo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoNormalAdaptive_h
#define _edoNormalAdaptive_h
#include "edoDistrib.h"
#ifdef WITH_EIGEN
#include <Eigen/Dense>
/** @defgroup CMAES CMAES
*
* CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a stochastic,
* derivative-free methods for numerical optimization of non-linear or
* non-convex continuous optimization problems.
*
* @ingroup Algorithms
*/
/** @defgroup Adaptivenormal Adaptive normal
*
* A multi-variate normal distribution that can be updated via several components.
* This is the data structure on which works the CMA-ES algorithm.
*
* @ingroup Distributions
*/
/** A normal distribution that can be updated via several components. This is the data structure on which works the CMA-ES
* algorithm.
*
* This is *just* a data structure, the operators working on it are supposed to maintain its consistency (e.g. of the
* covariance matrix against its eigen vectors).
*
* The distribution is defined by its mean, its covariance matrix (which can be decomposed in its eigen vectors and
* values), a scaling factor (sigma) and the so-called evolution paths for the covariance and sigma.
* evolution paths.
*
* NOTE: this is only available as an Eigen3 implementation (built WITH_EIGEN).
*
* @ingroup Distributions
* @ingroup CMAES
* @ingroup Adaptivenormal
*/
template < typename EOT >
class edoNormalAdaptive : public edoDistrib< EOT >
{
public:
//typedef EOT EOType;
typedef typename EOT::AtomType AtomType;
typedef Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector; // column vectors ( n lines, 1 column)
typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;
edoNormalAdaptive( unsigned int dim = 1 ) :
_dim(dim),
_mean( Vector::Zero(dim) ),
_C( Matrix::Identity(dim,dim) ),
_B( Matrix::Identity(dim,dim) ),
_D( Vector::Constant( dim, 1) ),
_sigma(1.0),
_p_c( Vector::Zero(dim) ),
_p_s( Vector::Zero(dim) )
{
assert( _dim > 0);
}
edoNormalAdaptive( unsigned int dim,
Vector mean,
Matrix C,
Matrix B,
Vector D,
double sigma,
Vector p_c,
Vector p_s
) :
_mean( mean ),
_C( C ),
_B( B ),
_D( D ),
_sigma(sigma),
_p_c( p_c ),
_p_s( p_s )
{
assert( dim > 0);
assert( _mean.innerSize() == dim );
assert( _C.innerSize() == dim && _C.outerSize() == dim );
assert( _B.innerSize() == dim && _B.outerSize() == dim );
assert( _D.innerSize() == dim );
assert( _sigma != 0.0 );
assert( _p_c.innerSize() == dim );
assert( _p_s.innerSize() == dim );
}
unsigned int size()
{
return _mean.innerSize();
}
Vector mean() const {return _mean;}
Matrix covar() const {return _C;}
Matrix coord_sys() const {return _B;}
Vector scaling() const {return _D;}
double sigma() const {return _sigma;}
Vector path_covar() const {return _p_c;}
Vector path_sigma() const {return _p_s;}
//! Set the mean with an Eigen3 vector
void mean( Vector m ) { _mean = m; assert( m.size() == _dim ); }
/** Set the mean with an EOT instead of an Eigen3 mean
*
* Explicit copy of the EOT in a vector.
*/
void mean( EOT m )
{
Vector center( m.size() );
for( unsigned int i=0, end=m.size(); i<end; ++i) {
center[i] = m[i];
}
mean( center );
}
void covar( Matrix c ) { _C = c; assert( c.innerSize() == _dim && c.outerSize() == _dim ); }
void coord_sys( Matrix b ) { _B = b; assert( b.innerSize() == _dim && b.outerSize() == _dim ); }
void scaling( Vector d ) { _D = d; assert( d.size() == _dim ); }
void sigma( double s ) { _sigma = s; assert( s != 0.0 );}
void path_covar( Vector p ) { _p_c = p; assert( p.size() == _dim ); }
void path_sigma( Vector p ) { _p_s = p; assert( p.size() == _dim ); }
private:
unsigned int _dim;
Vector _mean; // mean vector
Matrix _C; // covariance matrix
Matrix _B; // eigen vectors / coordinates system
Vector _D; // eigen values / scaling
double _sigma; // absolute scaling of the distribution
Vector _p_c; // evolution path for C
Vector _p_s; // evolution path for sigma
};
#else
#pragma message "WARNING: there is no Boost::uBLAS implementation of edoNormalAdaptive, build WITH_EIGEN if you need it."
#endif // WITH_EIGEN
#endif // !_edoNormalAdaptive_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoNormalMono_h
#define _edoNormalMono_h
#include "edoDistrib.h"
/** @defgroup Mononormal Normal
* A normal (Gaussian) distribution that only model variances of variables.
*
* @ingroup Distributions
*/
/** A normal (Gaussian) distribution that only model variances of variables.
*
* This is basically a mean vector and a variances vector. Do not model co-variances.
*
* @ingroup Distributions
* @ingroup Mononormal
*/
template < typename EOT >
class edoNormalMono : public edoDistrib< EOT >
{
public:
edoNormalMono()
: _mean(EOT(1,0)), _variance(EOT(1,1))
{}
edoNormalMono( const EOT& mean, const EOT& variance )
: _mean(mean), _variance(variance)
{
assert(_mean.size() > 0);
assert(_mean.size() == _variance.size());
}
unsigned int size()
{
assert(_mean.size() == _variance.size());
return _mean.size();
}
EOT mean(){return _mean;}
EOT variance(){return _variance;}
private:
EOT _mean;
EOT _variance;
};
#endif // !_edoNormalMono_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoNormalMonoCenter_h
#define _edoNormalMonoCenter_h
#include "edoModifierMass.h"
#include "edoNormalMono.h"
/** Change a distribution's mean for a given EOT
*
* @ingroup Modifiers
* @ingroup Mononormal
*/
template < typename EOT >
class edoNormalMonoCenter : public edoModifierMass< edoNormalMono< EOT > >
{
public:
typedef typename EOT::AtomType AtomType;
void operator() ( edoNormalMono< EOT >& distrib, EOT& mass )
{
distrib.mean() = mass;
}
};
#endif // !_edoNormalMonoCenter_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dreo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoNormalMulti_h
#define _edoNormalMulti_h
#include "edoDistrib.h"
#ifdef WITH_BOOST
#include <boost/numeric/ublas/symmetric.hpp>
#include <boost/numeric/ublas/lu.hpp>
namespace ublas = boost::numeric::ublas;
#else
#ifdef WITH_EIGEN
#include <Eigen/Dense>
#endif // WITH_EIGEN
#endif // WITH_BOOST
/** @defgroup EMNA
*
* Estimation of Multivariate Normal Algorithm (EMNA) is a stochastic,
* derivative-free methods for numerical optimization of non-linear or
* non-convex continuous optimization problems.
*
* @ingroup Algorithms
*/
/** @defgroup Multinormal Multivariate normal
*
* Distribution that model co-variances between variables.
*
* @ingroup Distributions
*/
/** A multi-normal distribution, that models co-variances.
*
* Defines a mean vector and a co-variances matrix.
*
* Exists in two implementations, using either
* <a href="http://www.boost.org/doc/libs/1_50_0/libs/numeric/ublas/doc/index.htm">Boost::uBLAS</a> (if compiled WITH_BOOST)
* or <a href="http://eigen.tuxfamily.org">Eigen3</a> (WITH_EIGEN).
*
* @ingroup Distributions
* @ingroup EMNA
* @ingroup Multinormal
*/
template < typename EOT >
class edoNormalMulti : public edoDistrib< EOT >
{
#ifdef WITH_BOOST
public:
typedef typename EOT::AtomType AtomType;
edoNormalMulti( unsigned int dim = 1 ) :
_mean( const ublas::vector<AtomType>(0,dim) ),
_varcovar( const ublas::identity_matrix<AtomType>(dim) )
{
assert(_mean.size() > 0);
assert(_mean.size() == _varcovar.size1());
assert(_mean.size() == _varcovar.size2());
}
edoNormalMulti
(
const ublas::vector< AtomType >& mean,
const ublas::symmetric_matrix< AtomType, ublas::lower >& varcovar
)
: _mean(mean), _varcovar(varcovar)
{
assert(_mean.size() > 0);
assert(_mean.size() == _varcovar.size1());
assert(_mean.size() == _varcovar.size2());
}
unsigned int size()
{
assert(_mean.size() == _varcovar.size1());
assert(_mean.size() == _varcovar.size2());
return _mean.size();
}
ublas::vector< AtomType > mean() const {return _mean;}
ublas::symmetric_matrix< AtomType, ublas::lower > varcovar() const {return _varcovar;}
private:
ublas::vector< AtomType > _mean;
ublas::symmetric_matrix< AtomType, ublas::lower > _varcovar;
#else
#ifdef WITH_EIGEN
public:
typedef typename EOT::AtomType AtomType;
typedef Eigen::Matrix< AtomType, Eigen::Dynamic, 1> Vector;
typedef Eigen::Matrix< AtomType, Eigen::Dynamic, Eigen::Dynamic> Matrix;
edoNormalMulti( unsigned int dim = 1 ) :
_mean( Vector::Zero(dim) ),
_varcovar( Matrix::Identity(dim,dim) )
{
assert(_mean.size() > 0);
assert(_mean.innerSize() == _varcovar.innerSize());
assert(_mean.innerSize() == _varcovar.outerSize());
}
edoNormalMulti(
const Vector & mean,
const Matrix & varcovar
)
: _mean(mean), _varcovar(varcovar)
{
assert(_mean.innerSize() > 0);
assert(_mean.innerSize() == _varcovar.innerSize());
assert(_mean.innerSize() == _varcovar.outerSize());
}
unsigned int size()
{
assert(_mean.innerSize() == _varcovar.innerSize());
assert(_mean.innerSize() == _varcovar.outerSize());
return _mean.innerSize();
}
Vector mean() const {return _mean;}
Matrix varcovar() const {return _varcovar;}
private:
Vector _mean;
Matrix _varcovar;
#endif // WITH_EIGEN
#endif // WITH_BOOST
}; // class edoNormalMulti
#endif // !_edoNormalMulti_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoNormalMultiCenter_h
#define _edoNormalMultiCenter_h
#include "edoModifierMass.h"
#include "edoNormalMulti.h"
#ifdef WITH_BOOST
/** Changes a given distribution's mean by a given EOT.
*
* @ingroup Modifiers
* @ingroup EMNA
* @inngroup Multinormal
*/
template < typename EOT >
class edoNormalMultiCenter : public edoModifierMass< edoNormalMulti< EOT > >
{
public:
typedef typename EOT::AtomType AtomType;
void operator() ( edoNormalMulti< EOT >& distrib, EOT& mass )
{
ublas::vector< AtomType > mean( distrib.size() );
std::copy( mass.begin(), mass.end(), mean.begin() );
distrib.mean() = mean;
}
};
#else
#ifdef WITH_EIGEN
/** Changes a given distribution's mean by a given EOT.
*
* @ingroup Modifiers
*/
template < typename EOT, typename D = edoNormalMulti< EOT > >
class edoNormalMultiCenter : public edoModifierMass<D>
{
public:
typedef typename EOT::AtomType AtomType;
typedef typename D::Vector Vector;
void operator() ( edoNormalMulti< EOT >& distrib, EOT& mass )
{
assert( distrib.size() == mass.innerSize() );
Vector mean( distrib.size() );
for( unsigned int i=0; i < distrib.size(); i++ ) {
mean(i) = mass[i];
}
distrib.mean() = mean;
}
};
#endif // WITH_EIGEN
#endif // WITH_BOOST
#endif // !_edoNormalMultiCenter_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2011 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoRepairer_h
#define _edoRepairer_h
#include <eoFunctor.h>
/** @defgroup Repairers
*
* A set of classes that modifies an unfeasible candidate
* solution so as to respect a given set of constraints and thus make a feasible
* solution.
*/
/** The interface of a set of classes that modifies an unfeasible candidate
* solution so as to respect a given set of constraints and thus make a feasible
* solution.
*
* @ingroup Repairers
* @ingroup Core
*/
template < typename EOT >
class edoRepairer : public eoUF< EOT&, void >
{
public:
// virtual void operator()( EOT& ) = 0 (provided by eoUF< A1, R >)
virtual void operator()( EOT& ) {}
};
#endif // !_edoRepairer_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2011 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
*/
#ifndef _edoRepairerApply_h
#define _edoRepairerApply_h
#include <algorithm>
#include "edoRepairer.h"
/** Interface for applying an arbitrary unary function as a repairer on each item of the solution
*
* @ingroup Repairers
*/
template < typename EOT, typename F = typename EOT::AtomType(typename EOT::AtomType) >
class edoRepairerApply : public edoRepairer<EOT>
{
public:
edoRepairerApply( F function ) : _function(function) {}
protected:
F * _function;
};
/** Apply an arbitrary unary function as a repairer on each item of the solution
*
* By default, the signature of the expected function is "EOT::AtomType(EOT::AtomType)"
*
* @ingroup Repairers
*/
template < typename EOT, typename F = typename EOT::AtomType(typename EOT::AtomType)>
class edoRepairerApplyUnary : public edoRepairerApply<EOT,F>
{
public:
edoRepairerApplyUnary( F function ) : edoRepairerApply<EOT,F>(function) {}
virtual void operator()( EOT& sol )
{
std::transform( sol.begin(), sol.end(), sol.begin(), *(this->_function) );
sol.invalidate();
}
};
/** Apply an arbitrary binary function as a repairer on each item of the solution,
* the second argument of the function being fixed and given at instanciation.
*
* @see edoRepairerApplyUnary
*
* @ingroup Repairers
*/
template < typename EOT, typename F = typename EOT::AtomType(typename EOT::AtomType, typename EOT::AtomType)>
class edoRepairerApplyBinary : public edoRepairerApply<EOT,F>
{
public:
typedef typename EOT::AtomType ArgType;
edoRepairerApplyBinary(
F function,
ArgType arg
) : edoRepairerApply<EOT,F>(function), _arg(arg) {}
virtual void operator()( EOT& sol )
{
// call the binary function on each item
// TODO find a way to use std::transform here? Or would it be too bloated?
for(typename EOT::iterator it = sol.begin(); it != sol.end(); ++it ) {
*it = (*(this->_function))( *it, _arg );
}
sol.invalidate();
}
protected:
ArgType _arg;
};
#endif // !_edoRepairerApply_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2011 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoRepairerDispatcher_h
#define _edoRepairerDispatcher_h
#include <vector>
#include <utility>
#include "edoRepairer.h"
/** Repair a candidate solution by sequentially applying several repairers on
* subparts of the solution (subparts being defined by the corresponding set
* of indexes).
*
* Only work on EOT that implements the "push_back( EOT::AtomType )" and
* "operator[](uint)" and "at(uint)" methods (i.e. random access containers).
*
* Expects _addresses_ of the repairer operators.
*
* Use the second template type if you want a different container to store
* indexes. You can use any iterable. For example, you may want to use a set if
* you need to be sure that indexes are use only once:
* edoRepairerDispatcher<EOT, std::set<unsigned int> > rpd;
* std::set<unsigned int> idx(1,1);
* idx.insert(2);
* rpd.add( idx, &repairer );
*
* A diagram trying to visually explain how it works:
\ditaa
|
/-\ | /------------\
| +---|---+ Dispatcher |
| | v | |
| |+-----+| --------------------------------+
| || x_0 || +-+-+-+ | +------------\ | /-\
| |+-----+| |2|3|5+*----*-* Repairer A +---|---+ |
| || x_1 || +-+-+-+ | | | | v | |
| |+-----+| | | | |+-----+| |
| || x_2 || | | | || x_2 || |
| |+-----+| | | | |+-----+| |
| || x_3 || | | | || x_3 || |
| |+-----+| | | | |+-----+| |
| || x_4 || | | | || x_5 || |
| |+-----+| | | | |+-----+| |
| || x_5 || | | | | | | |
| |+-----+| | | | +---|---+ |
| || x_6 || | | \------------/ | \-/
| |+-----+| <-------------------------------+
| || x_7 || | |
| |+-----+| +-+-+ | |
| || x_8 || |2|3+*------+
| |+-----+| +-+-+ |
| || x_9 || |
| |+-----+| +-+-+ | +------------\ /-\
| | | | |1|5+*--------* Repairer B +-------+ |
| | | | +-+-+ | | | | |
| | | | | | | | |
| | | | | | +-------+ |
| +---|---+ | \------------/ \-/
\-/ | \------------/
v
\endditaa
* @example t-dispatcher-round.cpp
*
* @ingroup Repairers
*/
template < typename EOT, typename ICT = std::vector<unsigned int> >
class edoRepairerDispatcher
: public edoRepairer<EOT>,
std::vector<
std::pair< ICT, edoRepairer< EOT >* >
>
{
public:
//! Empty constructor
edoRepairerDispatcher() :
std::vector<
std::pair< std::vector< unsigned int >, edoRepairer< EOT >* >
>()
{}
//! Constructor with a single index set and repairer operator
edoRepairerDispatcher( ICT idx, edoRepairer<EOT>* op ) :
std::vector<
std::pair< std::vector< unsigned int >, edoRepairer< EOT >* >
>()
{
this->add( idx, op );
}
//! Add more indexes set and their corresponding repairer operator address to the list
void add( ICT idx, edoRepairer<EOT>* op )
{
#ifndef NDEBUG
if( idx.size() == 0 ) {
eo::log << eo::warnings << "A repairer is added to the dispatcher while having an empty index list, nothing will be repaired" << std::endl;
}
#endif
assert( op != NULL );
this->push_back( std::make_pair(idx, op) );
}
//! Repair a solution by calling several repair operator on subset of indexes
virtual void operator()( EOT& sol )
{
// std::cout << "in dispatcher, sol = " << sol << std::endl;
// iterate over { indexe, repairer }
// ipair is an iterator that points on a pair of <indexes,repairer>
for( typename edoRepairerDispatcher<EOT>::iterator ipair = this->begin(); ipair != this->end(); ++ipair ) {
assert( ipair->first.size() <= sol.size() ); // assert there is less indexes than items in the whole solution
// a partial copy of the sol
EOT partsol;
// std::cout << "\tusing indexes = ";
//
// iterate over indexes
// j is an iterator that points on an uint
for( std::vector< unsigned int >::iterator j = ipair->first.begin(); j != ipair->first.end(); ++j ) {
// std::cout << *j << " ";
// std::cout.flush();
partsol.push_back( sol.at(*j) );
} // for j
// std::cout << std::endl;
// std::cout << "\tpartial sol = " << partsol << std::endl;
if( partsol.size() == 0 ) {
continue;
}
assert( partsol.size() > 0 );
// apply the repairer on the partial copy
// the repairer is a functor, thus second is callable
(*(ipair->second))( partsol );
{ // copy back the repaired partial solution to sol
// browse partsol with uint k, and the idx set with an iterator (std::vector is an associative tab)
unsigned int k=0;
for( std::vector< unsigned int >::iterator j = ipair->first.begin(); j != ipair->first.end(); ++j ) {
sol[ *j ] = partsol[ k ];
k++;
} // for j
} // context for k
} // for ipair
sol.invalidate();
}
};
#endif // !_edoRepairerDispatcher_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2011 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
*/
#ifndef _edoRepairerModulo_h
#define _edoRepairerModulo_h
#include <cmath>
#include "edoRepairerApply.h"
/** Repair an EOT container by applying the standard modulo function on it.
*
* @ingroup Repairers
*/
template < typename EOT >
class edoRepairerModulo: public edoRepairerApplyBinary<EOT>
{
public:
edoRepairerModulo<EOT>( double denominator ) : edoRepairerApplyBinary<EOT>( std::fmod, denominator ) {}
};
#endif // !_edoRepairerModulo_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2011 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoRepairerRound_h
#define _edoRepairerRound_h
#include <cmath>
#include "edoRepairerApply.h"
/** A repairer that calls "floor" on each items of a solution
*
* Just a proxy to "edoRepairerApplyUnary<EOT, EOT::AtomType(EOT::AtomType)> rep( std::floor);"
*
* @ingroup Repairers
*/
template < typename EOT >
class edoRepairerFloor : public edoRepairerApplyUnary<EOT>
{
public:
edoRepairerFloor() : edoRepairerApplyUnary<EOT>( std::floor ) {}
};
/** A repairer that calls "ceil" on each items of a solution
*
* @see edoRepairerFloor
*
* @ingroup Repairers
*/
template < typename EOT >
class edoRepairerCeil : public edoRepairerApplyUnary<EOT>
{
public:
edoRepairerCeil() : edoRepairerApplyUnary<EOT>( std::ceil ) {}
};
// FIXME find a way to put this function as a member of edoRepairerRoundDecimals
template< typename ArgType >
ArgType edoRound( ArgType val, ArgType prec = 1.0 )
{
return (val > 0.0) ?
floor(val * prec + 0.5) / prec :
ceil(val * prec - 0.5) / prec ;
}
/** A repairer that round values at a given a precision.
*
* e.g. if prec=0.1, 8.06 will be rounded to 8.1
*
* @see edoRepairerFloor
* @see edoRepairerCeil
*
* @ingroup Repairers
*/
template < typename EOT >
class edoRepairerRoundDecimals : public edoRepairerApplyBinary<EOT>
{
public:
typedef typename EOT::AtomType ArgType;
//! Generally speaking, we expect decimals being <= 1, but it can work for higher values
edoRepairerRoundDecimals( ArgType decimals ) : edoRepairerApplyBinary<EOT>( edoRound<ArgType>, 1 / decimals )
{
assert( decimals <= 1.0 );
assert( 1/decimals >= 1.0 );
}
};
/** A repairer that do a rounding around val+0.5
*
* @see edoRepairerRoundDecimals
*
* @ingroup Repairers
*/
template < typename EOT >
class edoRepairerRound : public edoRepairerRoundDecimals<EOT>
{
public:
edoRepairerRound() : edoRepairerRoundDecimals<EOT>( 1.0 ) {}
};
#endif // !_edoRepairerRound_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoSampler_h
#define _edoSampler_h
#include <eoFunctor.h>
#include "edoRepairer.h"
#include "edoBounderNo.h"
/** @defgroup Samplers
*
* Functors that draw and repair individuals according to a given distribution.
*/
/** Base class for samplers
*
* The functor here is already implemented: it first sample an EOT from the
* given distribution, and then apply the given repairers (if set).
*
* Thus, the function that need to be overloaded is "sample", unlike most of EO
* functors.
*
* @ingroup Samplers
* @ingroup Core
*/
template < typename D >
class edoSampler : public eoUF< D&, typename D::EOType >
{
public:
typedef typename D::EOType EOType;
edoSampler(edoRepairer< EOType > & repairer)
: _dummy_repairer(), _repairer(repairer)
{}
edoSampler()
: _dummy_repairer(), _repairer( _dummy_repairer )
{}
// virtual EOType operator()( D& ) = 0 (provided by eoUF< A1, R >)
EOType operator()( D& distrib )
{
assert( distrib.size() > 0 );
// Point we want to sample to get higher a set of points
// (coordinates in n dimension)
// x = {x1, x2, ..., xn}
// the sample method is implemented in the derivated class
EOType solution(sample(distrib));
// Now we are bounding the distribution thanks to min and max
// parameters.
_repairer(solution);
return solution;
}
protected:
virtual EOType sample( D& ) = 0;
private:
edoBounderNo<EOType> _dummy_repairer;
//! repairer functor
edoRepairer< EOType > & _repairer;
};
#endif // !_edoSampler_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#ifndef _edoSamplerNormalAdaptive_h
#define _edoSamplerNormalAdaptive_h
#include <cmath>
#include <limits>
#include <edoSampler.h>
/** Sample points in a multi-normal law defined by a mean vector, a covariance matrix, a sigma scale factor and
* evolution paths. This is a step of the CMA-ES algorithm.
*
* @ingroup Samplers
* @ingroup CMAES
* @ingroup Adaptivenormal
*/
#ifdef WITH_EIGEN
template< class EOT, typename D = edoNormalAdaptive< EOT > >
class edoSamplerNormalAdaptive : public edoSampler< D >
{
public:
typedef typename EOT::AtomType AtomType;
typedef typename D::Vector Vector;
typedef typename D::Matrix Matrix;
edoSamplerNormalAdaptive( edoRepairer<EOT> & repairer )
: edoSampler< D >( repairer)
{}
EOT sample( D& distrib )
{
unsigned int N = distrib.size();
assert( N > 0);
// T = vector of size elements drawn in N(0,1)
Vector T( N );
for ( unsigned int i = 0; i < N; ++i ) {
T( i ) = rng.normal();
}
assert(T.innerSize() == N );
assert(T.outerSize() == 1);
// mean(N,1) + sigma * B(N,N) * ( D(N,1) .* T(N,1) )
Vector sol = distrib.mean()
+ distrib.sigma()
* distrib.coord_sys() * (distrib.scaling().cwiseProduct(T) ); // C * T = B * (D .* T)
assert( sol.size() == N );
/*Vector sol = distrib.mean() + distrib.sigma()
* distrib.coord_sys().dot( distrib.scaling().dot( T ) );*/
// copy in the EOT structure (more probably a vector)
EOT solution( N );
for( unsigned int i = 0; i < N; i++ ) {
solution[i]= sol(i);
}
return solution;
}
};
#endif // WITH_EIGEN
#endif // !_edoSamplerNormalAdaptive_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoSamplerNormalMono_h
#define _edoSamplerNormalMono_h
#include <cmath>
#include <utils/eoRNG.h>
#include "edoSampler.h"
#include "edoNormalMono.h"
#include "edoBounder.h"
/** A sampler for edoNormalMono
*
* @ingroup Samplers
* @ingroup Mononormal
*/
template < typename EOT, typename D = edoNormalMono< EOT > >
class edoSamplerNormalMono : public edoSampler< D >
{
public:
typedef typename EOT::AtomType AtomType;
edoSamplerNormalMono( edoRepairer<EOT> & repairer ) : edoSampler< D >( repairer) {}
EOT sample( edoNormalMono<EOT>& distrib )
{
unsigned int size = distrib.size();
assert(size > 0);
// The point we want to draw
// (coordinates in n dimension)
// x = {x1, x2, ..., xn}
EOT solution;
// Sampling all dimensions
for (unsigned int i = 0; i < size; ++i) {
AtomType mean = distrib.mean()[i];
AtomType variance = distrib.variance()[i];
// should use the standard deviation, which have the same scale than the mean
AtomType random = rng.normal(mean, sqrt(variance) );
solution.push_back(random);
}
return solution;
}
};
#endif // !_edoSamplerNormalMono_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoSamplerNormalMulti_h
#define _edoSamplerNormalMulti_h
#include <cmath>
#include <limits>
#include <edoSampler.h>
#ifdef WITH_BOOST
#include <utils/edoCholesky.h>
#include <boost/numeric/ublas/lu.hpp>
#include <boost/numeric/ublas/symmetric.hpp>
namespace ublas = boost::numeric::ublas;
#else
#ifdef WITH_EIGEN
#include <Eigen/Dense>
#endif // WITH_EIGEN
#endif // WITH_BOOST
/** Sample points in a multi-normal law defined by a mean vector and a covariance matrix.
*
* Given M the mean vector and V the covariance matrix, of order n:
* - draw a vector T in N(0,I) (i.e. each value is drawn in a normal law with mean=0 an stddev=1)
* - compute the Cholesky decomposition L of V (i.e. such as V=LL*)
* - return X = M + LT
*
* Exists in two implementations, using either
* <a href="http://www.boost.org/doc/libs/1_50_0/libs/numeric/ublas/doc/index.htm">Boost::uBLAS</a> (if compiled WITH_BOOST)
* or <a href="http://eigen.tuxfamily.org">Eigen3</a> (WITH_EIGEN).
*
* @ingroup Samplers
* @ingroup EMNA
* @ingroup Multinormal
*/
template< typename EOT, typename D = edoNormalMulti< EOT > >
class edoSamplerNormalMulti : public edoSampler< D >
{
#ifdef WITH_BOOST
public:
typedef typename EOT::AtomType AtomType;
edoSamplerNormalMulti( edoRepairer<EOT> & repairer )
: edoSampler< D >( repairer)
{}
EOT sample( D& distrib )
{
unsigned int size = distrib.size();
assert(size > 0);
// L = cholesky decomposition of varcovar
const typename cholesky::CholeskyBase<AtomType>::FactorMat& L = _cholesky( distrib.varcovar() );
// T = vector of size elements drawn in N(0,1)
ublas::vector< AtomType > T( size );
for ( unsigned int i = 0; i < size; ++i ) {
T( i ) = rng.normal();
}
// LT = L * T
ublas::vector< AtomType > LT = ublas::prod( L, T );
// solution = means + LT
ublas::vector< AtomType > mean = distrib.mean();
ublas::vector< AtomType > ublas_solution = mean + LT;
EOT solution( size );
std::copy( ublas_solution.begin(), ublas_solution.end(), solution.begin() );
return solution;
}
protected:
cholesky::CholeskyLLT<AtomType> _cholesky;
#else
#ifdef WITH_EIGEN
public:
typedef typename EOT::AtomType AtomType;
typedef typename D::Vector Vector;
typedef typename D::Matrix Matrix;
edoSamplerNormalMulti( edoRepairer<EOT> & repairer )
: edoSampler< D >( repairer)
{}
EOT sample( D& distrib )
{
unsigned int size = distrib.size();
assert(size > 0);
// LsD = cholesky decomposition of varcovar
// Computes L and mD such as V = L mD L^T
Eigen::LDLT<Matrix> cholesky( distrib.varcovar() );
Matrix L = cholesky.matrixL();
assert(L.innerSize() == size);
assert(L.outerSize() == size);
Matrix mD = cholesky.vectorD().asDiagonal();
assert(mD.innerSize() == size);
assert(mD.outerSize() == size);
// now compute the final symetric matrix: LsD = L mD^1/2
// remember that V = ( L mD^1/2) ( L mD^1/2)^T
// fortunately, the square root of a diagonal matrix is the square
// root of all its elements
Matrix sqrtD = mD.cwiseSqrt();
assert(sqrtD.innerSize() == size);
assert(sqrtD.outerSize() == size);
Matrix LsD = L * sqrtD;
assert(LsD.innerSize() == size);
assert(LsD.outerSize() == size);
// T = vector of size elements drawn in N(0,1)
Vector T( size );
for ( unsigned int i = 0; i < size; ++i ) {
T( i ) = rng.normal();
}
assert(T.innerSize() == size);
assert(T.outerSize() == 1);
// LDT = (L mD^1/2) * T
Vector LDT = LsD * T;
assert(LDT.innerSize() == size);
assert(LDT.outerSize() == 1);
// solution = means + LDT
Vector mean = distrib.mean();
assert(mean.innerSize() == size);
assert(mean.outerSize() == 1);
Vector typed_solution = mean + LDT;
assert(typed_solution.innerSize() == size);
assert(typed_solution.outerSize() == 1);
// copy in the EOT structure (more probably a vector)
EOT solution( size );
for( unsigned int i = 0; i < mean.innerSize(); i++ ) {
solution[i]= typed_solution(i);
}
assert( solution.size() == size );
return solution;
}
#endif // WITH_EIGEN
#endif // WITH_BOOST
}; // class edoNormalMulti
#endif // !_edoSamplerNormalMulti_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoSamplerUniform_h
#define _edoSamplerUniform_h
#include <utils/eoRNG.h>
#include "edoSampler.h"
#include "edoUniform.h"
/**
* This class uses the Uniform distribution parameters (bounds) to return
* a random position used for population sampling.
*
* Returns a random number in [min,max[ for each variable defined by the given
* distribution.
*
* Note: if the distribution given at call defines a min==max for one of the
* variable, the result will be the same number.
*
* @ingroup Samplers
*/
template < typename EOT, class D = edoUniform<EOT> >
class edoSamplerUniform : public edoSampler< D >
{
public:
typedef D Distrib;
edoSamplerUniform( edoRepairer<EOT> & repairer ) : edoSampler< D >( repairer) {}
EOT sample( edoUniform< EOT >& distrib )
{
unsigned int size = distrib.size();
assert(size > 0);
// Point we want to sample to get higher a set of points
// (coordinates in n dimension)
// x = {x1, x2, ..., xn}
EOT solution;
// Sampling all dimensions
for (unsigned int i = 0; i < size; ++i)
{
double min = distrib.min()[i];
double max = distrib.max()[i];
double random = rng.uniform(min, max);
assert( ( min == random && random == max ) || ( min <= random && random < max) ); // random in [ min, max [
solution.push_back(random);
}
return solution;
}
};
#endif // !_edoSamplerUniform_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoUniform_h
#define _edoUniform_h
#include "edoDistrib.h"
#include "edoVectorBounds.h"
/** A uniform distribution.
*
* Defined by its bounds.
*
* @ingroup Distributions
*/
template < typename EOT >
class edoUniform : public edoDistrib< EOT >, public edoVectorBounds< EOT >
{
public:
edoUniform(EOT min, EOT max)
: edoVectorBounds< EOT >(min, max)
{}
};
#endif // !_edoUniform_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoUniformCenter_h
#define _edoUniformCenter_h
#include "edoModifierMass.h"
#include "edoUniform.h"
/** Modify an edoUniform distribution by centering its bounds around a given EOT.
*
* @ingroup Modifiers
*/
template < typename EOT >
class edoUniformCenter : public edoModifierMass< edoUniform< EOT > >
{
public:
typedef typename EOT::AtomType AtomType;
void operator() ( edoUniform< EOT >& distrib, EOT& mass )
{
for (unsigned int i = 0, n = mass.size(); i < n; ++i)
{
AtomType& min = distrib.min()[i];
AtomType& max = distrib.max()[i];
AtomType range = (max - min) / 2;
min = mass[i] - range;
max = mass[i] + range;
}
}
};
#endif // !_edoUniformCenter_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoVectorBounds_h
#define _edoVectorBounds_h
/** A class that holds min and max bounds vectors.
*/
template < typename EOT >
class edoVectorBounds
{
public:
edoVectorBounds(EOT min, EOT max)
: _min(min), _max(max)
{
assert(_min.size() > 0);
assert(_min.size() == _max.size());
}
EOT min(){return _min;}
EOT max(){return _max;}
unsigned int size()
{
assert(_min.size() == _max.size());
return _min.size();
}
private:
EOT _min;
EOT _max;
};
#endif // !_edoVectorBounds_h

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######################################################################################
### 1) Set all needed source files for the project
######################################################################################
FILE(GLOB SOURCES *.cpp)
SET(LIBRARY_OUTPUT_PATH ${CMAKE_BINARY_DIR}/lib)
ADD_LIBRARY(edoutils ${SOURCES})
INSTALL(TARGETS edoutils ARCHIVE DESTINATION lib COMPONENT libraries)
FILE(GLOB HDRS *.h utils)
INSTALL(FILES ${HDRS} DESTINATION include/edo/utils COMPONENT headers)
######################################################################################

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoCheckPoint_h
#define _edoCheckPoint_h
#include <utils/eoUpdater.h>
#include <utils/eoMonitor.h>
#include "edoContinue.h"
#include "edoStat.h"
//! eoCheckPoint< EOT > classe fitted to Distribution Object library
template < typename D >
class edoCheckPoint : public edoContinue< D >
{
public:
typedef typename D::EOType EOType;
edoCheckPoint(edoContinue< D >& _cont)
{
_continuators.push_back( &_cont );
}
bool operator()(const D& distrib)
{
for ( unsigned int i = 0, size = _stats.size(); i < size; ++i )
{
(*_stats[i])( distrib );
}
for ( unsigned int i = 0, size = _updaters.size(); i < size; ++i )
{
(*_updaters[i])();
}
for ( unsigned int i = 0, size = _monitors.size(); i < size; ++i )
{
(*_monitors[i])();
}
bool bContinue = true;
for ( unsigned int i = 0, size = _continuators.size(); i < size; ++i )
{
if ( !(*_continuators[i])( distrib ) )
{
bContinue = false;
}
}
if ( !bContinue )
{
for ( unsigned int i = 0, size = _stats.size(); i < size; ++i )
{
_stats[i]->lastCall( distrib );
}
for ( unsigned int i = 0, size = _updaters.size(); i < size; ++i )
{
_updaters[i]->lastCall();
}
for ( unsigned int i = 0, size = _monitors.size(); i < size; ++i )
{
_monitors[i]->lastCall();
}
}
return bContinue;
}
void add(edoContinue< D >& cont) { _continuators.push_back( &cont ); }
void add(edoStatBase< D >& stat) { _stats.push_back( &stat ); }
void add(eoMonitor& mon) { _monitors.push_back( &mon ); }
void add(eoUpdater& upd) { _updaters.push_back( &upd ); }
virtual std::string className(void) const { return "edoCheckPoint"; }
std::string allClassNames() const
{
std::string s("\n" + className() + "\n");
s += "Stats\n";
for ( unsigned int i = 0, size = _stats.size(); i < size; ++i )
{
s += _stats[i]->className() + "\n";
}
s += "\n";
s += "Updaters\n";
for ( unsigned int i = 0; i < _updaters.size(); ++i )
{
s += _updaters[i]->className() + "\n";
}
s += "\n";
s += "Monitors\n";
for ( unsigned int i = 0; i < _monitors.size(); ++i )
{
s += _monitors[i]->className() + "\n";
}
s += "\n";
s += "Continuators\n";
for ( unsigned int i = 0, size = _continuators.size(); i < size; ++i )
{
s += _continuators[i]->className() + "\n";
}
s += "\n";
return s;
}
private:
std::vector< edoContinue< D >* > _continuators;
std::vector< edoStatBase< D >* > _stats;
std::vector< eoMonitor* > _monitors;
std::vector< eoUpdater* > _updaters;
};
#endif // !_edoCheckPoint_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
namespace cholesky {
#ifdef WITH_BOOST
/** Cholesky decomposition, given a matrix V, return a matrix L
* such as V = L L^T (L^T being the transposed of L).
*
* Need a symmetric and positive definite matrix as an input, which
* should be the case of a non-ill-conditionned covariance matrix.
* Thus, expect a (lower) triangular matrix.
*/
template< typename T >
class CholeskyBase
{
public:
//! The covariance-matrix is symetric
typedef ublas::symmetric_matrix< T, ublas::lower > CovarMat;
//! The factorization matrix is triangular
// FIXME check if triangular types behaviour is like having 0
typedef ublas::matrix< T > FactorMat;
/** Instanciate without computing anything, you are responsible of
* calling the algorithm and getting the result with operator()
* */
CholeskyBase( size_t s1 = 1, size_t s2 = 1 ) :
_L(ublas::zero_matrix<T>(s1,s2))
{}
/** Computation is made at instanciation and then cached in a member variable,
* use decomposition() to get the result.
*/
CholeskyBase(const CovarMat& V) :
_L(ublas::zero_matrix<T>(V.size1(),V.size2()))
{
(*this)( V );
}
/** Compute the factorization and cache the result */
virtual void factorize( const CovarMat& V ) = 0;
/** Compute the factorization and return the result */
virtual const FactorMat& operator()( const CovarMat& V )
{
this->factorize( V );
return decomposition();
}
//! The decomposition of the covariance matrix
const FactorMat & decomposition() const
{
return _L;
}
protected:
/** Assert that the covariance matrix have the required properties and returns its dimension.
*
* Note: if compiled with NDEBUG, will not assert anything and just return the dimension.
*/
unsigned assert_properties( const CovarMat& V )
{
unsigned int Vl = V.size1(); // number of lines
// the result goes in _L
_L = ublas::zero_matrix<T>(Vl,Vl);
#ifndef NDEBUG
assert(Vl > 0);
unsigned int Vc = V.size2(); // number of columns
assert(Vc > 0);
assert( Vl == Vc );
// partial assert that V is semi-positive definite
// assert that all diagonal elements are positives
for( unsigned int i=0; i < Vl; ++i ) {
assert( V(i,i) > 0 );
}
/* FIXME what is the more efficient way to check semi-positive definite? Candidates are:
* perform the cholesky factorization
* check if all eigenvalues are positives
* check if all of the leading principal minors are positive
*/
#endif
return Vl;
}
//! The decomposition is a (lower) symetric matrix, just like the covariance matrix
FactorMat _L;
};
/** This standard algorithm makes use of square root and is thus subject
* to round-off errors if the covariance matrix is very ill-conditioned.
*
* Compute L such that V = L L^T
*
* When compiled in debug mode and called on ill-conditionned matrix,
* will raise an assert before calling the square root on a negative number.
*/
template< typename T >
class CholeskyLLT : public CholeskyBase<T>
{
public:
virtual void factorize( const typename CholeskyBase<T>::CovarMat& V )
{
unsigned int N = assert_properties( V );
unsigned int i=0, j=0, k;
this->_L(0, 0) = sqrt( V(0, 0) );
// end of the column
for ( j = 1; j < N; ++j ) {
this->_L(j, 0) = V(0, j) / this->_L(0, 0);
}
// end of the matrix
for ( i = 1; i < N; ++i ) { // each column
// diagonal
double sum = 0.0;
for ( k = 0; k < i; ++k) {
sum += this->_L(i, k) * this->_L(i, k);
}
this->_L(i,i) = L_i_i( V, i, sum );
for ( j = i + 1; j < N; ++j ) { // rows
// one element
sum = 0.0;
for ( k = 0; k < i; ++k ) {
sum += this->_L(j, k) * this->_L(i, k);
}
this->_L(j, i) = (V(j, i) - sum) / this->_L(i, i);
} // for j in ]i,N[
} // for i in [1,N[
}
/** The step of the standard LLT algorithm where round off errors may appear */
inline virtual T L_i_i( const typename CholeskyBase<T>::CovarMat& V, const unsigned int& i, const double& sum ) const
{
// round-off errors may appear here
assert( V(i,i) - sum >= 0 );
return sqrt( V(i,i) - sum );
}
};
/** This standard algorithm makes use of square root but do not fail
* if the covariance matrix is very ill-conditioned.
* Here, we propagate the error by using the absolute value before
* computing the square root.
*
* Be aware that this increase round-off errors, this is just a ugly
* hack to avoid crash.
*/
template< typename T >
class CholeskyLLTabs : public CholeskyLLT<T>
{
public:
inline virtual T L_i_i( const typename CholeskyBase<T>::CovarMat& V, const unsigned int& i, const double& sum ) const
{
/***** ugly hack *****/
return sqrt( fabs( V(i,i) - sum) );
}
};
/** This standard algorithm makes use of square root but do not fail
* if the covariance matrix is very ill-conditioned.
* Here, if the diagonal difference ir negative, we set it to zero.
*
* Be aware that this increase round-off errors, this is just a ugly
* hack to avoid crash.
*/
template< typename T >
class CholeskyLLTzero : public CholeskyLLT<T>
{
public:
inline virtual T L_i_i( const typename CholeskyBase<T>::CovarMat& V, const unsigned int& i, const double& sum ) const
{
T Lii;
if( V(i,i) - sum >= 0 ) {
Lii = sqrt( V(i,i) - sum);
} else {
/***** ugly hack *****/
Lii = 0;
}
return Lii;
}
};
/** This alternative algorithm do not use square root in an inner loop,
* but only for some diagonal elements of the matrix D.
*
* Computes L and D such as V = L D L^T.
* The factorized matrix is (L D^1/2), because V = (L D^1/2) (L D^1/2)^T
*/
template< typename T >
class CholeskyLDLT : public CholeskyBase<T>
{
public:
virtual void factorize( const typename CholeskyBase<T>::CovarMat& V )
{
// use "int" everywhere, because of the "j-1" operation
int N = assert_properties( V );
// example of an invertible matrix whose decomposition is undefined
assert( V(0,0) != 0 );
typename CholeskyBase<T>::FactorMat L = ublas::zero_matrix<T>(N,N);
typename CholeskyBase<T>::FactorMat D = ublas::zero_matrix<T>(N,N);
D(0,0) = V(0,0);
for( int j=0; j<N; ++j ) { // each columns
L(j, j) = 1;
D(j,j) = V(j,j);
for( int k=0; k<=j-1; ++k) { // sum
D(j,j) -= L(j,k) * L(j,k) * D(k,k);
}
for( int i=j+1; i<N; ++i ) { // remaining rows
L(i,j) = V(i,j);
for( int k=0; k<=j-1; ++k) { // sum
L(i,j) -= L(i,k)*L(j,k) * D(k,k);
}
L(i,j) /= D(j,j);
} // for i in rows
} // for j in columns
this->_L = root( L, D );
}
inline typename CholeskyBase<T>::FactorMat root( typename CholeskyBase<T>::FactorMat& L, typename CholeskyBase<T>::FactorMat& D )
{
// now compute the final symetric matrix: this->_L = L D^1/2
// remember that V = ( L D^1/2) ( L D^1/2)^T
// fortunately, the square root of a diagonal matrix is the square
// root of all its elements
typename CholeskyBase<T>::FactorMat sqrt_D = D;
for( int i=0; i < D.size1(); ++i) {
sqrt_D(i,i) = sqrt(D(i,i));
}
// the factorization is thus this->_L*D^1/2
return ublas::prod( L, sqrt_D );
}
};
#else
#ifdef WITH_EIGEN
#endif // WITH_EIGEN
#endif // WITH_BOOST
} // namespace cholesky

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (c) Marc Schoenauer, Maarten Keijzer and GeNeura Team, 2001
Copyright (C) 2010 Thales group
*/
/*
Authors:
todos@geneura.ugr.es
Marc Schoenauer <Marc.Schoenauer@polytechnique.fr>
Martin Keijzer <mkeijzer@dhi.dk>
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <stdexcept>
#include <utils/edoFileSnapshot.h>
#include <utils/compatibility.h>
#include <utils/eoParam.h>
edoFileSnapshot::edoFileSnapshot(std::string dirname,
unsigned int frequency /*= 1*/,
std::string filename /*= "gen"*/,
std::string delim /*= " "*/,
unsigned int counter /*= 0*/,
bool rmFiles /*= true*/,
bool saveFilenames /*= true*/)
: _dirname(dirname), _frequency(frequency),
_filename(filename), _delim(delim),
_counter(counter), _saveFilenames(saveFilenames),
_descOfFiles( NULL ), _boolChanged(true)
{
std::string s = "test -d " + _dirname;
int res = system(s.c_str());
// test for (unlikely) errors
if ( (res == -1) || (res == 127) )
{
throw std::runtime_error("Problem executing test of dir in eoFileSnapshot");
}
// now make sure there is a dir without any genXXX file in it
if (res) // no dir present
{
s = std::string("mkdir ") + _dirname;
}
else if (!res && rmFiles)
{
s = std::string("/bin/rm -f ") + _dirname+ "/" + _filename + "*";
}
else
{
s = " ";
}
int dummy;
dummy = system(s.c_str());
// all done
_descOfFiles = new std::ofstream( std::string(dirname + "/list_of_files.txt").c_str() );
}
edoFileSnapshot::~edoFileSnapshot()
{
delete _descOfFiles;
}
void edoFileSnapshot::setCurrentFileName()
{
std::ostringstream oscount;
oscount << _counter;
_currentFileName = _dirname + "/" + _filename + oscount.str();
}
eoMonitor& edoFileSnapshot::operator()(void)
{
if (_counter % _frequency)
{
_boolChanged = false; // subclass with gnuplot will do nothing
_counter++;
return (*this);
}
_counter++;
_boolChanged = true;
setCurrentFileName();
std::ofstream os(_currentFileName.c_str());
if (!os)
{
std::string str = "edoFileSnapshot: Could not open " + _currentFileName;
throw std::runtime_error(str);
}
if ( _saveFilenames )
{
*_descOfFiles << _currentFileName.c_str() << std::endl;
}
return operator()(os);
}
eoMonitor& edoFileSnapshot::operator()(std::ostream& os)
{
iterator it = vec.begin();
os << (*it)->getValue();
for ( ++it; it != vec.end(); ++it )
{
os << _delim.c_str() << (*it)->getValue();
}
os << '\n';
return *this;
}

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (c) Marc Schoenauer, Maarten Keijzer and GeNeura Team, 2001
Copyright (C) 2010 Thales group
*/
/*
Authors:
todos@geneura.ugr.es
Marc Schoenauer <Marc.Schoenauer@polytechnique.fr>
Martin Keijzer <mkeijzer@dhi.dk>
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoFileSnapshot_h
#define _edoFileSnapshot_h
#include <string>
#include <fstream>
#include <stdexcept>
#include "utils/eoMonitor.h"
//! edoFileSnapshot
class edoFileSnapshot : public eoMonitor
{
public:
edoFileSnapshot(std::string dirname,
unsigned int frequency = 1,
std::string filename = "gen",
std::string delim = " ",
unsigned int counter = 0,
bool rmFiles = true,
bool saveFilenames = true);
virtual ~edoFileSnapshot();
virtual bool hasChanged() {return _boolChanged;}
virtual std::string getDirName() { return _dirname; }
virtual unsigned int getCounter() { return _counter; }
virtual const std::string baseFileName() { return _filename;}
std::string getFileName() {return _currentFileName;}
void setCurrentFileName();
virtual eoMonitor& operator()(void);
virtual eoMonitor& operator()(std::ostream& os);
private :
std::string _dirname;
unsigned int _frequency;
std::string _filename;
std::string _delim;
std::string _currentFileName;
unsigned int _counter;
bool _saveFilenames;
std::ofstream* _descOfFiles;
bool _boolChanged;
};
#endif // !_edoFileSnapshot

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoHyperVolume_h
#define _edoHyperVolume_h
//! edoHyperVolume
template < typename EOT >
class edoHyperVolume
{
public:
typedef typename EOT::AtomType AtomType;
edoHyperVolume() : _hv(1) {}
void update(AtomType v)
{
_hv *= ::sqrt( v );
assert( _hv <= std::numeric_limits< AtomType >::max() );
}
AtomType get_hypervolume() const { return _hv; }
protected:
AtomType _hv;
};
#endif // !_edoHyperVolume_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (c) Marc Schoenauer, Maarten Keijzer and GeNeura Team, 2001
Copyright (C) 2010 Thales group
*/
/*
Authors:
todos@geneura.ugr.es
Marc Schoenauer <Marc.Schoenauer@polytechnique.fr>
Martin Keijzer <mkeijzer@dhi.dk>
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoPopStat_h
#define _edoPopStat_h
#include <utils/eoStat.h>
/** Thanks to MS/VC++, eoParam mechanism is unable to handle std::vectors of stats.
This snippet is a workaround:
This class will "print" a whole population into a std::string - that you can later
send to any stream
This is the plain version - see eoPopString for the Sorted version
Note: this Stat should probably be used only within eoStdOutMonitor, and not
inside an eoFileMonitor, as the eoState construct will work much better there.
*/
template <class EOT>
class edoPopStat : public eoStat<EOT, std::string>
{
public:
using eoStat<EOT, std::string>::value;
/** default Ctor, void std::string by default, as it appears
on the description line once at beginning of evolution. and
is meaningless there. _howMany defaults to 0, that is, the whole
population*/
edoPopStat(std::string _desc ="")
: eoStat<EOT, std::string>("", _desc) {}
/** Fills the value() of the eoParam with the dump of the population. */
void operator()(const eoPop<EOT>& _pop)
{
std::ostringstream os;
os << _pop;
value() = os.str();
}
};
#endif // !_edoPopStat_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoStat_h
#define _edoStat_h
#include <eoFunctor.h>
//! edoStatBase< D >
template < typename D >
class edoStatBase : public eoUF< const D&, void >
{
public:
// virtual void operator()( const D& ) = 0 (provided by eoUF< A1, R >)
virtual void lastCall( const D& ) {}
virtual std::string className() const { return "edoStatBase"; }
};
template < typename D > class edoCheckPoint;
template < typename D, typename T >
class edoStat : public eoValueParam< T >, public edoStatBase< D >
{
public:
edoStat(T value, std::string description)
: eoValueParam< T >(value, description)
{}
virtual std::string className(void) const { return "edoStat"; }
edoStat< D, T >& addTo(edoCheckPoint< D >& cp) { cp.add(*this); return *this; }
// TODO: edoStat< D, T >& addTo(eoMonitor& mon) { mon.add(*this); return *this; }
};
//! A parent class for any kind of distribution to dump parameter to std::string type
template < typename D >
class edoDistribStat : public edoStat< D, std::string >
{
public:
using edoStat< D, std::string >::value;
edoDistribStat(std::string desc)
: edoStat< D, std::string >("", desc)
{}
};
#endif // !_edoStat_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoStatNormalMono_h
#define _edoStatNormalMono_h
#include "edoStat.h"
#include "edoNormalMono.h"
//! edoStatNormalMono< EOT >
template < typename EOT >
class edoStatNormalMono : public edoDistribStat< edoNormalMono< EOT > >
{
public:
using edoDistribStat< edoNormalMono< EOT > >::value;
edoStatNormalMono( std::string desc = "" )
: edoDistribStat< edoNormalMono< EOT > >( desc )
{}
void operator()( const edoNormalMono< EOT >& distrib )
{
value() = "\n# ====== mono normal distribution dump =====\n";
std::ostringstream os;
os << distrib.mean() << " " << distrib.variance() << std::endl;
value() += os.str();
}
};
#endif // !_edoStatNormalMono_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoStatNormalMulti_h
#define _edoStatNormalMulti_h
#include<sstream>
#include "edoStat.h"
#include "../edoNormalMulti.h"
#ifdef WITH_BOOST
#include <boost/numeric/ublas/io.hpp>
#else
#ifdef WITH_EIGEN
// include nothing
#endif // WITH_EIGEN
#endif // WITH_BOOST
//! edoStatNormalMulti< EOT >
template < typename EOT >
class edoStatNormalMulti : public edoDistribStat< edoNormalMulti< EOT > >
{
public:
// typedef typename EOT::AtomType AtomType;
using edoDistribStat< edoNormalMulti< EOT > >::value;
edoStatNormalMulti( std::string desc = "" )
: edoDistribStat< edoNormalMulti< EOT > >( desc )
{}
void operator()( const edoNormalMulti< EOT >& distrib )
{
value() = "\n# ====== multi normal distribution dump =====\n";
std::ostringstream os;
os << distrib.mean() << std::endl << std::endl << distrib.varcovar() << std::endl;
// ublas::vector< AtomType > mean = distrib.mean();
// std::copy(mean.begin(), mean.end(), std::ostream_iterator< std::string >( os, " " ));
// ublas::symmetric_matrix< AtomType, ublas::lower > varcovar = distrib.varcovar();
// std::copy(varcovar.begin(), varcovar.end(), std::ostream_iterator< std::string >( os, " " ));
// os << std::endl;
value() += os.str();
}
};
#endif // !_edoStatNormalMulti_h

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#ifndef _edoStatUniform_h
#define _edoStatUniform_h
#include "edoStat.h"
#include "edoUniform.h"
//! edoStatUniform< EOT >
template < typename EOT >
class edoStatUniform : public edoDistribStat< edoUniform< EOT > >
{
public:
using edoDistribStat< edoUniform< EOT > >::value;
edoStatUniform( std::string desc = "" )
: edoDistribStat< edoUniform< EOT > >( desc )
{}
void operator()( const edoUniform< EOT >& distrib )
{
value() = "\n# ====== uniform distribution dump =====\n";
std::ostringstream os;
os << distrib.min() << " " << distrib.max() << std::endl;
value() += os.str();
}
};
#endif // !_edoStatUniform_h

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###############################################################################
##
## CMakeLists file for unit test
##
###############################################################################
######################################################################################
### 1) Include the sources
######################################################################################
######################################################################################
######################################################################################
### 2) Specify where CMake can find the libraries
######################################################################################
######################################################################################
######################################################################################
### 3) Define your targets and link the librairies
######################################################################################
FIND_PACKAGE(Boost 1.33.0)
INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
INCLUDE_DIRECTORIES(${Boost_INCLUDE_DIRS})
LINK_DIRECTORIES(${Boost_LIBRARY_DIRS})
INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/application/common)
SET(SOURCES
#t-cholesky
t-variance
t-edoEstimatorNormalMulti
t-mean-distance
t-bounderno
t-uniform
t-continue
t-dispatcher-round
t-repairer-modulo
)
FOREACH(current ${SOURCES})
ADD_EXECUTABLE(${current} ${current}.cpp)
ADD_TEST(${current} ${current})
TARGET_LINK_LIBRARIES(${current} edo edoutils ${EO_LIBRARIES} ${MO_LIBRARIES} ${Boost_LIBRARIES})
INSTALL(TARGETS ${current} RUNTIME DESTINATION share/edo/test COMPONENT test)
ENDFOREACH()
######################################################################################

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#!/usr/bin/env python
from pylab import *
FILE_LOCATIONS = 'means_distances_results/files_description.txt'
data = []
locations = [ line.split()[0] for line in open( FILE_LOCATIONS ) ]
for cur_file in locations:
data.append( [ float(line.split()[7]) for line in open( cur_file ).readlines() ] )
print locations
#print data
boxplot( data )
show()

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <eo>
#include <edo>
#include "Rosenbrock.h"
typedef eoReal< eoMinimizingFitness > EOT;
int main(void)
{
edoBounderNo< EOT > bounder;
return 0;
}

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
*/
//#include <vector>
#include <cstdlib>
#include <iostream>
#include <sstream>
#include <limits>
#include <iomanip>
#include <ctime>
#include <eo>
#include <es.h>
#include <edo>
typedef eoReal< eoMinimizingFitness > EOT;
typedef edoNormalMulti<EOT> EOD;
void setformat( std::ostream& out )
{
out << std::right;
out << std::setfill(' ');
out << std::setw( 5 + std::numeric_limits<double>::digits10);
out << std::setprecision(std::numeric_limits<double>::digits10);
out << std::setiosflags(std::ios_base::showpoint);
}
template<typename MT>
std::string format(const MT& mat )
{
std::ostringstream out;
setformat(out);
for( unsigned int i=0; i<mat.size1(); ++i) {
for( unsigned int j=0; j<mat.size2(); ++j) {
out << mat(i,j) << "\t";
} // columns
out << std::endl;
} // rows
return out.str();
}
template< typename T >
T round( T val, T prec = 1.0 )
{
return (val > 0.0) ?
floor(val * prec + 0.5) / prec :
ceil(val * prec - 0.5) / prec ;
}
template<typename MT>
bool equal( const MT& M1, const MT& M2, double prec /* = 1/std::numeric_limits<double>::digits10 ???*/ )
{
if( M1.size1() != M2.size1() || M1.size2() != M2.size2() ) {
return false;
}
for( unsigned int i=0; i<M1.size1(); ++i ) {
for( unsigned int j=0; j<M1.size2(); ++j ) {
if( round(M1(i,j),prec) != round(M2(i,j),prec) ) {
std::cout << "round(M(" << i << "," << j << "," << prec << ") == "
<< round(M1(i,j),prec) << " != " << round(M2(i,j),prec) << std::endl;
return false;
}
}
}
return true;
}
template<typename MT >
MT error( const MT& M1, const MT& M2 )
{
assert( M1.size1() == M2.size1() && M1.size1() == M2.size2() );
MT Err = ublas::zero_matrix<double>(M1.size1(),M1.size2());
for( unsigned int i=0; i<M1.size1(); ++i ) {
for( unsigned int j=0; j<M1.size2(); ++j ) {
Err(i,j) = M1(i,j) - M2(i,j);
}
}
return Err;
}
template<typename MT >
double trigsum( const MT& M )
{
double sum;
for( unsigned int i=0; i<M.size1(); ++i ) {
for( unsigned int j=i; j<M.size2(); ++j ) { // triangular browsing
sum += fabs( M(i,j) ); // absolute deviation
}
}
return sum;
}
template<typename T>
double sum( const T& c )
{
return std::accumulate(c.begin(), c.end(), 0);
}
int main(int argc, char** argv)
{
srand(time(0));
unsigned int N = 4; // size of matrix
unsigned int R = 1000; // nb of repetitions
if( argc >= 2 ) {
N = std::atoi(argv[1]);
}
if( argc >= 3 ) {
R = std::atoi(argv[2]);
}
std::cout << "Usage: t-cholesky [matrix size] [repetitions]" << std::endl;
std::cout << "matrix size = " << N << std::endl;
std::cout << "repetitions = " << R << std::endl;
typedef edoSamplerNormalMulti<EOT,EOD>::Cholesky::CovarMat CovarMat;
typedef edoSamplerNormalMulti<EOT,EOD>::Cholesky::FactorMat FactorMat;
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::standard );
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTa( edoSamplerNormalMulti<EOT,EOD>::Cholesky::absolute );
edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTz( edoSamplerNormalMulti<EOT,EOD>::Cholesky::zeroing );
edoSamplerNormalMulti<EOT,EOD>::Cholesky LDLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::robust );
std::vector<double> s0,s1,s2,s3;
for( unsigned int n=0; n<R; ++n ) {
// a variance-covariance matrix of size N*N
CovarMat V(N,N);
// random covariance matrix
for( unsigned int i=0; i<N; ++i) {
V(i,i) = std::pow(rand(),2); // variance should be >= 0
for( unsigned int j=i+1; j<N; ++j) {
V(i,j) = rand();
}
}
FactorMat L0 = LLT(V);
CovarMat V0 = ublas::prod( L0, ublas::trans(L0) );
s0.push_back( trigsum(error(V,V0)) );
FactorMat L1 = LLTa(V);
CovarMat V1 = ublas::prod( L1, ublas::trans(L1) );
s1.push_back( trigsum(error(V,V1)) );
FactorMat L2 = LLTz(V);
CovarMat V2 = ublas::prod( L2, ublas::trans(L2) );
s2.push_back( trigsum(error(V,V2)) );
FactorMat L3 = LDLT(V);
CovarMat V3 = ublas::prod( L3, ublas::trans(L3) );
s3.push_back( trigsum(error(V,V3)) );
}
std::cout << "Average error:" << std::endl;
std::cout << "\tLLT: " << sum(s0)/R << std::endl;
std::cout << "\tLLTa: " << sum(s1)/R << std::endl;
std::cout << "\tLLTz: " << sum(s2)/R << std::endl;
std::cout << "\tLDLT: " << sum(s3)/R << std::endl;
// double precision = 1e-15;
// if( argc >= 4 ) {
// precision = std::atof(argv[3]);
// }
// std::cout << "precision = " << precision << std::endl;
// std::cout << "usage: t-cholesky [N] [precision]" << std::endl;
// std::cout << "N = " << N << std::endl;
// std::cout << "precision = " << precision << std::endl;
// std::string linesep = "--------------------------------------------------------------------------------------------";
// std::cout << linesep << std::endl;
//
// setformat(std::cout);
//
// std::cout << "Covariance matrix" << std::endl << format(V) << std::endl;
// std::cout << linesep << std::endl;
//
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::standard );
// FactorMat L0 = LLT(V);
// CovarMat V0 = ublas::prod( L0, ublas::trans(L0) );
// CovarMat E0 = error(V,V0);
// std::cout << "LLT" << std::endl << format(E0) << std::endl;
// std::cout << trigsum(E0) << std::endl;
// std::cout << "LLT" << std::endl << format(L0) << std::endl;
// std::cout << "LLT covar" << std::endl << format(V0) << std::endl;
// assert( equal(V0,V,precision) );
// std::cout << linesep << std::endl;
//
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LLTa( edoSamplerNormalMulti<EOT,EOD>::Cholesky::absolute );
// FactorMat L1 = LLTa(V);
// CovarMat V1 = ublas::prod( L1, ublas::trans(L1) );
// CovarMat E1 = error(V,V1);
// std::cout << "LLT abs" << std::endl << format(E1) << std::endl;
// std::cout << trigsum(E1) << std::endl;
// std::cout << "LLT abs" << std::endl << format(L1) << std::endl;
// std::cout << "LLT covar" << std::endl << format(V1) << std::endl;
// assert( equal(V1,V,precision) );
// std::cout << linesep << std::endl;
//
// edoSamplerNormalMulti<EOT,EOD>::Cholesky LDLT( edoSamplerNormalMulti<EOT,EOD>::Cholesky::robust );
// FactorMat L2 = LDLT(V);
// CovarMat V2 = ublas::prod( L2, ublas::trans(L2) );
// CovarMat E2 = error(V,V2);
// std::cout << "LDLT" << std::endl << format(E2) << std::endl;
// std::cout << trigsum(E2) << std::endl;
// std::cout << "LDLT" << std::endl << format(L2) << std::endl;
// std::cout << "LDLT covar" << std::endl << format(V2) << std::endl;
// assert( equal(V2,V,precision) );
// std::cout << linesep << std::endl;
}

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <eo>
#include <edo>
#include "Rosenbrock.h"
typedef eoReal< eoMinimizingFitness > EOT;
typedef edoUniform< EOT > Distrib;
int main(void)
{
eoState state;
edoContinue< Distrib >* continuator = new edoDummyContinue< Distrib >();
state.storeFunctor(continuator);
return 0;
}

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Pierre Savéant <pierre.saveant@thalesgroup.com>
*/
#include <eo>
#include <edo>
#include <es.h>
typedef eoReal< eoMinimizingFitness > EOT;
int main(void)
{
EOT sol;
sol.push_back(1.1);
sol.push_back(1.1);
sol.push_back(3.9);
sol.push_back(3.9);
sol.push_back(5.4);
sol.push_back(5.6);
sol.push_back(7.011);
sol.push_back(8.09);
sol.push_back(8.21);
std::cout << "expect: INVALID 9 1 2 3 4 5 6 7 8.1 8.2" << std::endl;
edoRepairer<EOT>* rep1 = new edoRepairerFloor<EOT>();
edoRepairer<EOT>* rep2 = new edoRepairerCeil<EOT>();
edoRepairer<EOT>* rep3 = new edoRepairerRound<EOT>();
edoRepairer<EOT>* rep4 = new edoRepairerRoundDecimals<EOT>( 10 );
std::vector<unsigned int> indexes1;
indexes1.push_back(0);
indexes1.push_back(2);
std::vector<unsigned int> indexes2;
indexes2.push_back(1);
indexes2.push_back(3);
std::vector<unsigned int> indexes3;
indexes3.push_back(4);
indexes3.push_back(5);
std::vector<unsigned int> indexes4;
indexes4.push_back(6);
indexes4.push_back(7);
indexes4.push_back(8);
edoRepairerDispatcher<EOT> repare( indexes1, rep1 );
repare.add( indexes2, rep2 );
repare.add( indexes3, rep3 );
repare.add( indexes4, rep4 );
repare(sol);
std::cout << sol << std::endl;
return 0;
}

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <sstream>
#include <iomanip>
#include <eo>
//#include <mo>
#include <edo>
#include "Rosenbrock.h"
#include "Sphere.h"
typedef eoReal< eoMinimizingFitness > EOT;
typedef edoNormalMulti< EOT > Distrib;
typedef EOT::AtomType AtomType;
#ifdef WITH_BOOST
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/symmetric.hpp>
typedef ublas::vector< AtomType > Vector;
typedef ublas::symmetric_matrix< AtomType, ublas::lower > Matrix;
#else
#ifdef WITH_EIGEN
#include <Eigen/Dense>
typedef edoNormalMulti<EOT>::Vector Vector;
typedef edoNormalMulti<EOT>::Matrix Matrix;
#endif
#endif
int main(int ac, char** av)
{
// (0) parser + eo routines
eoParser parser(ac, av);
std::string section("Algorithm parameters");
unsigned int p_size = parser.createParam((unsigned int)100, "popSize", "Population Size", 'P', section).value(); // P
unsigned int s_size = parser.createParam((unsigned int)2, "dimension-size", "Dimension size", 'd', section).value(); // d
AtomType mean_value = parser.createParam((AtomType)0, "mean", "Mean value", 'm', section).value(); // m
AtomType covar1_value = parser.createParam((AtomType)1.0, "covar1", "Covar value 1", '1', section).value();
AtomType covar2_value = parser.createParam((AtomType)0.5, "covar2", "Covar value 2", '2', section).value();
AtomType covar3_value = parser.createParam((AtomType)1.0, "covar3", "Covar value 3", '3', section).value();
std::ostringstream ss;
ss << p_size << "_" << std::fixed << std::setprecision(1)
<< mean_value << "_" << covar1_value << "_" << covar2_value << "_"
<< covar3_value << "_gen";
std::string gen_filename = ss.str();
if( parser.userNeedsHelp() ) {
parser.printHelp(std::cout);
exit(1);
}
make_verbose(parser);
make_help(parser);
assert(p_size > 0);
assert(s_size > 0);
eoState state;
// (1) Population init and sampler
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( s_size, *gen );
state.storeFunctor(init);
// create an empty pop and let the state handle the memory
// fill population thanks to eoInit instance
eoPop< EOT >& pop = state.takeOwnership( eoPop< EOT >( p_size, *init ) );
// (2) distribution initial parameters
Vector mean( s_size );
for (unsigned int i = 0; i < s_size; ++i) {
mean( i ) = mean_value;
}
Matrix varcovar( s_size, s_size );
varcovar( 0, 0 ) = covar1_value;
varcovar( 0, 1 ) = covar2_value;
varcovar( 1, 1 ) = covar3_value;
Distrib distrib( mean, varcovar );
// (3a) distribution output preparation
edoDummyContinue< Distrib >* distrib_dummy_continue = new edoDummyContinue< Distrib >();
state.storeFunctor(distrib_dummy_continue);
edoCheckPoint< Distrib >* distrib_continue = new edoCheckPoint< Distrib >( *distrib_dummy_continue );
state.storeFunctor(distrib_continue);
edoDistribStat< Distrib >* distrib_stat = new edoStatNormalMulti< EOT >();
state.storeFunctor(distrib_stat);
distrib_continue->add( *distrib_stat );
edoFileSnapshot* distrib_file_snapshot = new edoFileSnapshot( "TestResDistrib", 1, gen_filename );
state.storeFunctor(distrib_file_snapshot);
distrib_file_snapshot->add(*distrib_stat);
distrib_continue->add(*distrib_file_snapshot);
// (3b) distribution output
(*distrib_continue)( distrib );
// Prepare bounder class to set bounds of sampling.
// This is used by edoSampler.
edoBounder< EOT >* bounder = new edoBounderRng< EOT >(
EOT(pop[0].size(), -5), EOT(pop[0].size(), 5), *gen
);
state.storeFunctor(bounder);
// Prepare sampler class with a specific distribution
edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
state.storeFunctor(sampler);
// (4) sampling phase
pop.clear();
for( unsigned int i = 0; i < p_size; ++i ) {
EOT candidate_solution = (*sampler)( distrib );
pop.push_back( candidate_solution );
}
// (5) population output
eoContinue< EOT >* pop_cont = new eoGenContinue< EOT >( 2 ); // never reached fitness
state.storeFunctor(pop_cont);
eoCheckPoint< EOT >* pop_continue = new eoCheckPoint< EOT >( *pop_cont );
state.storeFunctor(pop_continue);
edoPopStat< EOT >* pop_stat = new edoPopStat<EOT>;
state.storeFunctor(pop_stat);
pop_continue->add(*pop_stat);
edoFileSnapshot* pop_file_snapshot = new edoFileSnapshot( "TestResPop", 1, gen_filename );
state.storeFunctor(pop_file_snapshot);
pop_file_snapshot->add(*pop_stat);
pop_continue->add(*pop_file_snapshot);
(*pop_continue)( pop );
// (6) estimation phase
edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
state.storeFunctor(estimator);
distrib = (*estimator)( pop );
// (7) distribution output
(*distrib_continue)( distrib );
// (8) euclidianne distance estimation
Vector new_mean = distrib.mean();
Matrix new_varcovar = distrib.varcovar();
AtomType distance = 0;
for( unsigned int d = 0; d < s_size; ++d ) {
distance += pow( mean[ d ] - new_mean[ d ], 2 );
}
distance = sqrt( distance );
eo::log << eo::logging
<< "mean: " << mean << std::endl
<< "new mean: " << new_mean << std::endl
<< "distance: " << distance << std::endl
;
return 0;
}

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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
Caner Candan <caner.candan@thalesgroup.com>
*/
#include <sys/stat.h>
#include <sys/types.h>
#include <sstream>
#include <iomanip>
#include <fstream>
#include <eo>
//#include <mo>
#include <edo>
#include "Rosenbrock.h"
#include "Sphere.h"
typedef eoReal< eoMinimizingFitness > EOT;
typedef edoNormalMulti< EOT > Distrib;
typedef EOT::AtomType AtomType;
#ifdef WITH_BOOST
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/symmetric.hpp>
typedef ublas::vector< AtomType > Vector;
typedef ublas::symmetric_matrix< AtomType, ublas::lower > Matrix;
#else
#ifdef WITH_EIGEN
#include <Eigen/Dense>
typedef edoNormalMulti<EOT>::Vector Vector;
typedef edoNormalMulti<EOT>::Matrix Matrix;
#endif
#endif
int main(int ac, char** av)
{
// (0) parser + eo routines
eoParser parser(ac, av);
std::string section("Algorithm parameters");
unsigned int r_max = parser.createParam((unsigned int)100, "run-number", "Number of run", 'r', section).value(); // r
unsigned int p_min = parser.createParam((unsigned int)10, "population-min", "Population min", 'p', section).value(); // p
unsigned int p_max = parser.createParam((unsigned int)1000, "population-max", "Population max", 'P', section).value(); // P
unsigned int p_step = parser.createParam((unsigned int)50, "population-step", "Population step", 't', section).value(); // t
unsigned int s_size = parser.createParam((unsigned int)2, "dimension-size", "Dimension size", 'd', section).value(); // d
AtomType mean_value = parser.createParam((AtomType)0, "mean", "Mean value", 'm', section).value(); // m
AtomType covar1_value = parser.createParam((AtomType)1.0, "covar1", "Covar value 1", '1', section).value(); // 1
AtomType covar2_value = parser.createParam((AtomType)0.5, "covar2", "Covar value 2", '2', section).value(); // 2
AtomType covar3_value = parser.createParam((AtomType)1.0, "covar3", "Covar value 3", '3', section).value(); // 3
std::string results_directory = parser.createParam((std::string)"means_distances_results", "results-directory", "Results directory", 'R', section).value(); // R
std::string files_description = parser.createParam((std::string)"files_description.txt", "files-description", "Files description", 'F', section).value(); // F
if (parser.userNeedsHelp())
{
parser.printHelp(std::cout);
exit(1);
}
make_verbose(parser);
make_help(parser);
assert(r_max >= 1);
assert(s_size >= 2);
eo::log << eo::quiet;
::mkdir( results_directory.c_str(), 0755 );
for ( unsigned int p_size = p_min; p_size <= p_max; p_size += p_step )
{
assert(p_size >= p_min);
std::ostringstream desc_file;
desc_file << results_directory << "/" << files_description;
std::ostringstream cur_file;
cur_file << results_directory << "/pop_" << p_size << ".txt";
eo::log << eo::file( desc_file.str() ) << cur_file.str().c_str() << std::endl;
eo::log << eo::file( cur_file.str() );
eo::log << eo::logging << "run_number p_size s_size mean(0) mean(1) new-mean(0) new-mean(1) distance" << std::endl;
eo::log << eo::quiet;
for ( unsigned int r = 1; r <= r_max; ++r)
{
eoState state;
// (1) Population init and sampler
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( s_size, *gen );
state.storeFunctor(init);
// create an empty pop and let the state handle the memory
// fill population thanks to eoInit instance
eoPop< EOT >& pop = state.takeOwnership( eoPop< EOT >( p_size, *init ) );
// (2) distribution initial parameters
#ifdef WITH_BOOST
Vector mean( s_size, mean_value );
#else
#ifdef WITH_EIGEN
Vector mean( s_size );
mean = Vector::Constant( s_size, mean_value);
#endif
#endif
Matrix varcovar( s_size, s_size );
varcovar( 0, 0 ) = covar1_value;
varcovar( 0, 1 ) = covar2_value;
varcovar( 1, 1 ) = covar3_value;
Distrib distrib( mean, varcovar );
// Prepare bounder class to set bounds of sampling.
// This is used by edoSampler.
edoBounder< EOT >* bounder = new edoBounderRng< EOT >(EOT(pop[0].size(), -5),
EOT(pop[0].size(), 5),
*gen);
state.storeFunctor(bounder);
// Prepare sampler class with a specific distribution
edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
state.storeFunctor(sampler);
// (4) sampling phase
pop.clear();
for (unsigned int i = 0; i < p_size; ++i)
{
EOT candidate_solution = (*sampler)( distrib );
pop.push_back( candidate_solution );
}
// (6) estimation phase
edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
state.storeFunctor(estimator);
distrib = (*estimator)( pop );
// (8) euclidianne distance estimation
Vector new_mean = distrib.mean();
Matrix new_varcovar = distrib.varcovar();
AtomType distance = 0;
for ( unsigned int d = 0; d < s_size; ++d )
{
distance += pow( mean[ d ] - new_mean[ d ], 2 );
}
distance = sqrt( distance );
eo::log << r << " " << p_size << " " << s_size << " "
<< mean(0) << " " << mean(1) << " "
<< new_mean(0) << " " << new_mean(1) << " "
<< distance << std::endl
;
}
}
return 0;
}

55
edo/test/t-repairer-modulo.cpp Normal file
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/*
The Evolving Distribution Objects framework (EDO) is a template-based,
ANSI-C++ evolutionary computation library which helps you to write your
own estimation of distribution algorithms.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Copyright (C) 2010 Thales group
*/
/*
Authors:
Johann Dréo <johann.dreo@thalesgroup.com>
*/
#define _USE_MATH_DEFINES
#include <math.h>
#include <eo>
#include <edo>
#include <es.h>
typedef eoReal< eoMinimizingFitness > EOT;
int main(void)
{
EOT sol;
sol.push_back( M_PI * 1 );
sol.push_back( M_PI * 2 );
sol.push_back( M_PI * 3 );
sol.push_back( M_PI * 4 );
sol.push_back( M_PI * 4 + M_PI / 2 );
sol.push_back( M_PI * 5 + M_PI / 2 );
// we expect {pi,0,pi,0,pi/2,pi+pi/2}
std::cout << "expect: INVALID 4 3.14159 0 3.14159 0 1.5708 4.71239" << std::endl;
edoRepairer<EOT>* repare = new edoRepairerModulo<EOT>( 2 * M_PI ); // modulo 2pi
(*repare)(sol);
std::cout << sol << std::endl;
return 0;
}

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