a0f7039b27
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@609 331e1502-861f-0410-8da2-ba01fb791d7f
498 lines
29 KiB
C
498 lines
29 KiB
C
//! \mainpage Creating a simple ParadisEO-PEO Evolutionary Algorithm
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//!
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//! \section structure Introduction
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//!
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//! One of the first steps in designing an evolutionary algorithm using the ParadisEO-PEO framework
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//! consists in having a clear overview of the implemented algorithm. A brief pseudo-code description is offered
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//! bellow - the entire source code for the ParadisEO-PEO evolutionary algorithm is defined in the <b>peoEA.h</b>
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//! header file. The main elements to be considered when building an evolutionary algorithm are the transformation
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//! operators, i.e. crossover and mutation, the evaluation function, the continuation criterion and the selection
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//! and replacement strategy.
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//!
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//! <table style="border:none; border-spacing:0px;text-align:left; vertical-align:top; font-size:8pt;" border="0">
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//! <tr><td><b>do</b> { </td> <td> </td></tr>
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//! <tr><td> select( population, offsprings ); </td> <td>// select the offsprings from the current population</td></tr>
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//! <tr><td> transform( offsprings ); </td> <td>// crossover and mutation operators are applied on the selected offsprings</td></tr>
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//! <tr><td> evaluate( offsprings ); </td> <td>// evaluation step of the resulting offsprings</td></tr>
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//! <tr><td> replace( population, offsprings ); </td> <td>// replace the individuals in the current population whith individuals from the offspring population, according to a specified replacement strategy</td></tr>
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//! <tr><td>} <b>while</b> ( eaCheckpointContinue( population ) ); </td> <td>// checkpoint operators are applied on the current population</td></tr>
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//! </table>
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//!
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//! The peoEA class offers an elementary evolutionary algorithm implementation. The peoEA class has the underlying structure
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//! for including parallel evaluation and parallel transformation operators, migration operators etc. Although there is
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//! no restriction on using the algorithms provided by the EO framework, no parallelism is provided - the EO implementation is exclusively sequential.
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//! <br/>
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//!
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//! \section requirements Requirements
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//!
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//! You should have already installed the ParadisEO-PEO package - this requires several additional packages which should be already
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//! included in the provided archive. The installation script has to be launched in order to configure and compile all the required
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//! components. At the end of the installation phase you should end up having a directory tree resembling the following:
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//! <b>
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//! <br/> ...
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//! <br/> paradiseo-mo
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//! <br/> paradiseo-moeo
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//! <br/> paradiseo-peo
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//! <br/> docs
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//! <br/> examples
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//! <br/> lesson1
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//! <br/> lesson2
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//! <br/> ...
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//! <br/> shared
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//! <br/> ...
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//! <br/> src
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//! <br/> ...
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//! <br/> ...
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//! </b>
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//! <br/>
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//!
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//! The source-code for this tutorial may be found in the <b>paradiseo-peo/examples/lesson1</b> directory, in the <b>main.cpp</b> file.
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//! We strongly encourage creating a backup copy of the file if you consider modifying the source code. For a complete reference on the
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//! TSP-related classes and definitions please refer to the files under the <b>paradiseo-peo/examples/shared</b>. After the installation
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//! phase you should end up having an <b>tspExample</b> executable file in the <b>paradiseo-peo/examples/lesson1</b> directory.
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//! We will discuss testing and launching aspects later in the tutorial.
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//!
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//! You are supposed to be familiar with working in C/C++ (with an extensive use of templates) and you should have at least an introductory
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//! background in working with the EO framework.
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//!
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//! <hr/>
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//! <b>NOTE</b>: All the presented examples have as case study the <i>Traveling Salesman Problem (TSP)</i>. All the presented tutorials rely
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//! on a <a href="../../lsnshared/html/index.html" target="new">common shared source code</a> defining transformation operators,
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//! evaluation functions, etc. for the TSP problem. For a complete understanding of the presented tutorials please take your time for
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//! consulting and for studying the additional underlying defined classes.
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//! <hr/><br/>
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//!
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//! \section problemDef Problem Definition and Representation
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//!
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//! As we are not directly concerned with the <i>Traveling Salesman Problem</i>, and to some extent out of scope, no in depth details are offered
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//! for the TSP. The problem requires finding the shortest path connecting a given set of cities, while visiting each of
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//! the specified cities only once and returning to the startpoint city. The problem is known to be NP-complete, i.e. no polynomial
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//! time algorithm exists for solving the problem in exact manner.
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//!
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//! The construction of a ParadisEO-PEO evolutionary algorithm requires following a few simple steps - please take your time to study the signature
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//! of the peoEA constructor:
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//!
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//! <table border="0" width="100%">
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//! <tr><td style="vertical-align:top;">
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//! peoEA(
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//! <br/> eoContinue< EOT >& __cont,
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//! <br/> peoPopEval< EOT >& __pop_eval,
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//! <br/> eoSelect< EOT >& __select,
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//! <br/> peoTransform< EOT >& __trans,
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//! <br/> eoReplacement< EOT >& __replace
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//! <br/> );
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//! </td>
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//! <td style="vertical-align:top; text-align:center;">
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//! \image html peoEA.png
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//! </td></tr>
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//! </table>
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//!
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//! A few remarks have to be made: while most of the parameters are passed as EO-specific types, the evaluation and the transformation objects have to be
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//! derived from the ParadisEO-PEO peoPopEval and peoTransform classes. Derived classes like the peoParaPopEval and peoParaSGATransform classes allow
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//! for parallel evaluation of the population and parallel transformation operators, respectively. Wrappers are provided thus allowing to make use
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//! of the EO classes.
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//!
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//! In the followings, the main required elements for building an evolutionary algorithm are enumerated. For complete details regarding the
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//! implementation aspects of each of the components, please refer to the <a href="../../lsnshared/html/index.html" target="new">common shared source code</a>.
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//! Each of the bellow referred header files may be found in the <b>pardiseo-peo/examples/shared</b> directory.
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//!
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//! <ol>
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//! <li><i><b>representation</b></i> - the first decision to be taken concerns the representation of the individuals. You may create your
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//! own representation or you may use/derive one of the predefined classes of the EO framework. <br/>
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//!
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//! For our case study, the TSP, each city is defined as a Node in the <b>node.h</b> header file - in fact an unsigned value defined
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//! as <b>typedef unsigned Node</b>. Moreover, each individual (of the evolutionary algorithm) is represented as a Route object, a vector of Node objects, in
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//! the <b>route.h</b> header file - <b>typedef eoVector< int, Node > Route</b>. The definition of the Route object implies two
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//! elements: (1) a route is a vector of nodes, and (2) the fitness is an integer value (please refer to the eoVector
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//! definition in the EO framework).
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//!
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//! In addition you should also take a look in the <b>route_init.h</b> header file which includes the RouteInit class, defined for
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//! initializing in random manner Route objects.
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//! </li>
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//! <li><i><b>evaluation function</b></i> - having a representation model, an evaluation object has to be defined, implementing a specific
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//! fitness function. The designed class has to be derived (directly or indirectly) from the peoPopEval class - you have the choice of
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//! using peoSeqPopEval or peoParaPopEval for sequential and parallel evaluation, respectively. These classes act as wrappers requiring
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//! the specification of an EO evaluation object derived from the eoEvalFunc class - please refer to their respective documentation. <br/>
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//!
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//! The fitness function for our TSP case study is implemented in the <b>route_eval.h</b> header file. The class is derived from the eoEvalFunc
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//! EO class, being defined as <b>class RouteEval : public eoEvalFunc< Route ></b>.
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//! </li>
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//! <li><i><b>transformation operators</b></i> - in order to assure the evolution of the initial population, transformation operators have to be defined.
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//! Depending on your problem, you may specify quadruple operators (two input individuals, two output resulting individuals), i.e. crossover operators,
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//! binary operators (one input individual and one output resulting individual), i.e. mutation operators, or combination of both types. As for the
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//! evaluation function, the signature of the peoEA constructor requires specifying a peoTransform derived object as transformation operator.
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//!
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//! The transform operators, crossover and mutation, for the herein presented example are defined in the <b>order_xover.h</b> and the <b>city_swap.h</b>
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//! header files, respectively.
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//! </li>
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//! <li><i><b>continuation criterion</b></i> - the evolutionary algorithm evolves in an iterative manner; a continuation criterion has to be specified.
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//! One of the most common and simplest options considers a maximum number of generations. It is your choice whether to use
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//! a predefined EO class for specifying the continuation criterion or using a custom defined class. In the later case you have to
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//! make sure that your class derives the eoContinue class.<br/>
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//! </li>
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//! <li><i><b>selection strategy</b></i> - at each iteration a set of individuals are selected for applying the transform operators, in order
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//! to obtain the offspring population. As the specified parameter has to be derived from the eoSelect it is your option of whether using
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//! the EO provided selection strategies or implementing your own, as long as it inherits the eoSelect class.
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//!
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//! For our example we chose to use the eoRankingSelect strategy, provided in the EO framework.
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//! </li>
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//! <li><i><b>replacement strategy</b></i> - once the offspring population is obtained, the offsprings have to be integrated back into the initial
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//! population, according to a given strategy. For custom defined strategies you have to inherit the eoReplacement EO class. We chose to
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//! use an eoPlusReplacement as strategy (please review the EO documentation for details on the different strategies available).
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//! </li>
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//! </ol>
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//! <br/>
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//!
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//! \section example A simple example for constructing a peoEA object
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//!
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//! The source code for this example may be found in the <b>main.cpp</b> file, under the <b>paradiseo-peo/examples/lesson1</b> directory. Please make sure you
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//! At this point you have two options: (a) you can just follow the example without touching the <b>main.cpp</b> or, (b) you can start from scratch,
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//! following the presented steps, in which case you are required make a backup copy of the <b>main.cpp</b> file and replace the original file with an
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//! empty one.
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//!
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//! <ol>
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//! <li> <b>include the necessary header files</b> - as we will be using Route objects, we have to include the files
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//! which define the Route type, the initializing functor and the evaluation functions. Furthermore, in order to make use of
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//! transform operators, we require having the headers which define the crossover and the mutation operators.
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//! All these files may be found in the shared directory that we mentioned in the beginning. At this point you
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//! should have something like the following:<br/>
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//!
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//! <pre>
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//! ##include "route.h"
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//! ##include "route_init.h"
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//! ##include "route_eval.h"
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//!
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//! ##include "order_xover.h"
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//! ##include "city_swap.h"
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//! </pre>
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//! In addition we require having the <i>paradiseo</i> header file, in order to use the ParadisEO-PEO features, and a header specific
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//! for our problem, dealing with processing command-line parameters - the <b>param.h</b> header file. The complete picture at this point
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//! with all the required header files is as follows:<br/>
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//!
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//! <pre>
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//! ##include "route.h"
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//! ##include "route_init.h"
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//! ##include "route_eval.h"
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//!
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//! ##include "order_xover.h"
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//! ##include "city_swap.h"
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//!
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//! ##include "param.h"
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//!
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//! ##include <paradiseo>
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//! </pre>
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//! <b>NOTE</b>: the <b><i>paradiseo</i></b> header file is in fact a "super-header" - it includes all the esential ParadisEO-PEO header files.
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//! It is at at your choice if you want use the <b><i>paradiseo</i></b> header file or to explicitly include different header files,
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//! like the <b>peoEA.h</b> header file, for example.
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//!
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//! </li>
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//! <li> <b>define problem specific parameters</b> - in our case we have to specify how many individuals we want to have in our population, the number
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//! of generations for the evolutionary algorithm to iterate and the probabilities associated with the crossover and mutation operators.<br/>
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//!
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//! <pre>
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//! ##include "route.h"
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//! ##include "route_init.h"
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//! ##include "route_eval.h"
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//!
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//! ##include "order_xover.h"
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//! ##include "city_swap.h"
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//!
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//! ##include "param.h"
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//!
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//! ##include <paradiseo>
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//!
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//!
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//! ##define POP_SIZE 10
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//! ##define NUM_GEN 100
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//! ##define CROSS_RATE 1.0
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//! ##define MUT_RATE 0.01
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//! </pre>
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//! </li>
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//! <li> <b>construct the skeleton of a simple ParadisEO-PEO program</b> - the main function including the code for initializing the ParadisEO-PEO
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//! environment, for loading problem data and for shutting down the ParadisEO-PEO environment. From this point on we will make
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//! abstraction of the previous part referring only to the main function.<br/>
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//!
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//! <pre>
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//! ...
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//!
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//! int main( int __argc, char** __argv ) {
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//!
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//! <i>//</i> initializing the ParadisEO-PEO environment
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//! peo :: init( __argc, __argv );
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//!
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//! <i>//</i> processing the command line specified parameters
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//! loadParameters( __argc, __argv );
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//!
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//!
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//! <i>//</i> EVOLUTIONARY ALGORITHM TO BE DEFINED
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//!
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//!
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//! peo :: run( );
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//! peo :: finalize( );
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//! <i>//</i> shutting down the ParadisEO-PEO environment
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//!
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//! return 0;
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//! }
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//! </pre>
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//! </li>
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//! <li> <b>initialization functors, evaluation function and transform operators</b> - basically we only need to create instances for each of the
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//! enumerated objects, to be passed later as parameters for higher-level components of the evolutionary algorithm.<br/>
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//!
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//! <pre>
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//! RouteInit route_init; <i>//</i> random init object - creates random Route objects
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//! RouteEval full_eval; <i>//</i> evaluator object - offers a fitness value for a specified Route object
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//!
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//! OrderXover crossover; <i>//</i> crossover operator - creates two offsprings out of two specified parents
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//! CitySwap mutation; <i>//</i> mutation operator - randomly mutates one gene for a specified individual
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//! </pre>
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//! </li>
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//! <li> <b>construct the components of the evolutionary algorithm</b> - each of the components that has to be passed as parameter to the
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//! <b>peoEA</b> constructor has to be defined along with the associated parameters. Except for the requirement to provide the
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//! appropriate objects (for example, a peoPopEval derived object must be specified for the evaluation functor), there is no strict
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//! path to follow. It is your option what elements to mix, depending on your problem - we aimed for simplicity in our example.
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//!
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//! <ul>
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//! <li> an initial population has to be specified; the constructor accepts the specification of an initializing object. Further,
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//! an evaluation object is required - the <b>peoEA</b> constructor requires a <b>peoPopEval</b> derived class.
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//! </li>
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//! </ul>
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//! <pre>
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//! eoPop< Route > population( POP_SIZE, route_init ); <i>//</i> initial population for the algorithm having POP_SIZE individuals
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//! peoSeqPopEval< Route > eaPopEval( full_eval ); // evaluator object - to be applied at each iteration on the entire population
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//! </pre>
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//! <ul>
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//! <li> the evolutionary algorithm continues to iterate till a continuation criterion is not met. For our case we consider
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//! a fixed number of generations. The continuation criterion has to be specified as a checkpoint object, thus requiring
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//! the creation of an <i>eoCheckPoint</i> object in addition.
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//! </li>
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//! </ul>
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//! <pre>
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//! eoGenContinue< Route > eaCont( NUM_GEN ); <i>//</i> continuation criterion - the algorithm will iterate for NUM_GEN generations
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//! eoCheckPoint< Route > eaCheckpointContinue( eaCont ); <i>//</i> checkpoint object - verify at each iteration if the continuation criterion is met
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//! </pre>
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//! <ul>
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//! <li> selection strategy - we are required to specify a selection strategy for extracting individuals out of the parent
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//! population; in addition the number of individuals to be selected has to be specified.
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//! </li>
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//! </ul>
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//! <pre>
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//! eoRankingSelect< Route > selectionStrategy; <i>//</i> selection strategy - applied at each iteration for selecting parent individuals
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//! eoSelectNumber< Route > eaSelect( selectionStrategy, POP_SIZE ); <i>//</i> selection object - POP_SIZE individuals are selected at each iteration
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//! </pre>
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//! <ul>
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//! <li> transformation operators - we have to integrate the crossover and the mutation functors into an object which may be passed
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//! as a parameter when creating the <b>peoEA</b> object. The constructor of <b>peoEA</b> requires a <b>peoTransform</b> derived
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//! object. Associated probabilities have to be specified also.
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//! </li>
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//! </ul>
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//! <pre>
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//! <i>//</i> transform operator - includes the crossover and the mutation operators with a specified associated rate
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//! eoSGATransform< Route > transform( crossover, CROSS_RATE, mutation, MUT_RATE );
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//! peoSeqTransform< Route > eaTransform( transform ); <i>//</i> ParadisEO transform operator (please remark the peo prefix) - wraps an e EO transform object
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//! </pre>
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//! <ul>
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//! <li> replacement strategy - required for defining the way for integrating the resulting offsprings into the initial population.
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//! At your option whether you would like to chose one of the predefined replacement strategies that come with the EO framework
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//! or if you want to define your own.
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//! </li>
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//! </ul>
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//! <pre>
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//! eoPlusReplacement< Route > eaReplace; <i>//</i> replacement strategy - for replacing the initial population with offspring individuals
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//! </pre>
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//! </li>
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//! <li> <b>evolutionary algorithm</b> - having defined all the previous components, we are ready for instanciating an evolutionary algorithm.
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//! In the end we have to associate a population with the algorithm, which will serve as the initial population, to be iteratively evolved.
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//!
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//! <pre>
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//! peoEA< Route > eaAlg( eaCheckpointContinue, eaPopEval, eaSelect, eaTransform, eaReplace );
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//!
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//! eaAlg( population ); // specifying the initial population for the algorithm, to be iteratively evolved
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//! </pre>
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//! </li>
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//! </ol>
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//!
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//! If you have not missed any of the enumerated points, your program should be like the following:
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//!
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//! <pre>
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//! ##include "route.h"
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//! ##include "route_init.h"
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//! ##include "route_eval.h"
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//!
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//! ##include "order_xover.h"
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//! ##include "city_swap.h"
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//!
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//! ##include "param.h"
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//!
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//! ##include <paradiseo>
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//!
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//!
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//! ##define POP_SIZE 10
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//! ##define NUM_GEN 100
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//! ##define CROSS_RATE 1.0
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//! ##define MUT_RATE 0.01
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//!
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//!
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//! int main( int __argc, char** __argv ) {
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//!
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//! <i>//</i> initializing the ParadisEO-PEO environment
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//! peo :: init( __argc, __argv );
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//!
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//!
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//! <i>//</i> processing the command line specified parameters
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//! loadParameters( __argc, __argv );
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//!
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//!
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//! <i>//</i> init, eval operators, EA operators -------------------------------------------------------------------------------------------------------------
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//!
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//! RouteInit route_init; <i>//</i> random init object - creates random Route objects
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//! RouteEval full_eval; <i>//</i> evaluator object - offers a fitness value for a specified Route object
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//!
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//! OrderXover crossover; <i>//</i> crossover operator - creates two offsprings out of two specified parents
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//! CitySwap mutation; <i>//</i> mutation operator - randomly mutates one gene for a specified individual
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//! <i>//</i> ------------------------------------------------------------------------------------------------------------------------------------------------
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//!
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//!
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//! <i>//</i> evolutionary algorithm components --------------------------------------------------------------------------------------------------------------
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//!
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//! eoPop< Route > population( POP_SIZE, route_init ); <i>//</i> initial population for the algorithm having POP_SIZE individuals
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//! peoSeqPopEval< Route > eaPopEval( full_eval ); <i>//</i> evaluator object - to be applied at each iteration on the entire population
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//!
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//! eoGenContinue< Route > eaCont( NUM_GEN ); <i>//</i> continuation criterion - the algorithm will iterate for NUM_GEN generations
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//! eoCheckPoint< Route > eaCheckpointContinue( eaCont ); <i>//</i> checkpoint object - verify at each iteration if the continuation criterion is met
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//!
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//! eoRankingSelect< Route > selectionStrategy; <i>//</i> selection strategy - applied at each iteration for selecting parent individuals
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//! eoSelectNumber< Route > eaSelect( selectionStrategy, POP_SIZE ); <i>//</i> selection object - POP_SIZE individuals are selected at each iteration
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//!
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//! <i>//</i> transform operator - includes the crossover and the mutation operators with a specified associated rate
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//! eoSGATransform< Route > transform( crossover, CROSS_RATE, mutation, MUT_RATE );
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//! peoSeqTransform< Route > eaTransform( transform ); <i>//</i> ParadisEO transform operator (please remark the peo prefix) - wraps an e EO transform object
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//!
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//! eoPlusReplacement< Route > eaReplace; <i>//</i> replacement strategy - for replacing the initial population with offspring individuals
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//! <i>//</i> ------------------------------------------------------------------------------------------------------------------------------------------------
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//!
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//!
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//! <i>//</i> ParadisEO-PEO evolutionary algorithm -----------------------------------------------------------------------------------------------------------
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//!
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//! peoEA< Route > eaAlg( eaCheckpointContinue, eaPopEval, eaSelect, eaTransform, eaReplace );
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//!
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//! eaAlg( population ); <i>//</i> specifying the initial population for the algorithm, to be iteratively evolved
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//! <i>//</i> ------------------------------------------------------------------------------------------------------------------------------------------------
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//!
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|
//!
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//! peo :: run( );
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//! peo :: finalize( );
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//! <i>//</i> shutting down the ParadisEO-PEO environment
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//!
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//! return 0;
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|
//! }
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|
//! </pre>
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|
//!
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|
//!
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//! \section testing Compilation and Execution
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//!
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//! First, please make sure that you followed all the previous steps in defining the evolutionary algorithm. Your file should be called <b>main.cpp</b> - please
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|
//! make sure you do not rename the file (we will be using a pre-built makefile, thus you are required not to change the file names). Please make sure you
|
|
//! are in the <b>paradiseo-peo/examples/lesson1</b> directory - you should open a console and you should change your current directory to the one of Lesson1.
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|
//!
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|
//! <b>Compilation</b>: being in the <b>paradiseo-peo/examples/lesson1</b> directory, you have to type <i>make</i>. As a result the <b>main.cpp</b> file
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|
//! will be compiled and you should obtain an executable file called <b>tspExample</b>. If you have errors, please verify any of the followings:
|
|
//!
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|
//! <ul>
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|
//! <li>you are under the right directory - you can verify by typing the <i>pwd</i> command - you should have something like <b>.../paradiseo-peo/examples/lesson1</b></li>
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|
//! <li>you saved your modifications in a file called <b>main.cpp</b>, in the <b>paradiseo-peo/examples/lesson1</b> directory</li>
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|
//! <li>there are no differences between the example presented above and your file</li>
|
|
//! </ul>
|
|
//!
|
|
//! <b>NOTE</b>: in order to successfully compile your program you should already have installed an MPI distribution in your system.
|
|
//!
|
|
//! <b>Execution</b>: the execution of a ParadisEO-PEO program requires having already created an environment for launching MPI programs. For <i>MPICH-2</i>,
|
|
//! for example, this requires starting a ring of daemons. The implementation that we provided as an example is sequential and includes no parallelism - we
|
|
//! will see in the end how to include also parallelism. Executing a parallel program requires specifying a mapping of resources, in order to assing different
|
|
//! algorithms to different machines, define worker machines etc. This mapping is defined by an XML file called <b>schema.xml</b>, which, for our case, has
|
|
//! the following structure:
|
|
//!
|
|
//! <pre>
|
|
//! <?xml version="1.0"?>
|
|
//!
|
|
//! <schema>
|
|
//! <group scheduler="0">
|
|
//! <node name="0" num_workers="0">
|
|
//! </node>
|
|
//!
|
|
//! <node name="1" num_workers="0">
|
|
//! <runner>1</runner>
|
|
//! </node>
|
|
//!
|
|
//! <node name="2" num_workers="1">
|
|
//! </node>
|
|
//! <node name="3" num_workers="1">
|
|
//! </node>
|
|
//! </group>
|
|
//! </schema>
|
|
//! </pre>
|
|
//!
|
|
//! Not going into details, the XML file presented above describes a mapping which includes four nodes, the first one having the role of scheduler,
|
|
//! the second one being the node on which the evolutionary algorithm is actually executed and the third and the fourth ones being slave nodes. Overall
|
|
//! the mapping says that we will be launching four processes, out of which only one will be executing the evolutionary algorithm. The other node entries
|
|
//! in the XML file have no real functionality as we have no parallelism in our program - the entries were created for you convenience, in order to provide
|
|
//! a smooth transition to creating a parallel program.
|
|
//!
|
|
//! Launching the program may be different, depending on your MPI distribution - for MPICH-2, in a console, in the <b>paradiseo-peo/examples/lesson1</b>
|
|
//! directory you have to type the following command:
|
|
//!
|
|
//! <b>mpiexec -n 4 ./tspExample @lesson.param</b>
|
|
//!
|
|
//! <b>NOTE</b>: the "-n 4" indicates the number of processes to be launched. The last argument, "@lesson.param", indicates a file which specifies different
|
|
//! application specific parameters (the mapping file to be used, for example, whether to use logging or not, etc).
|
|
//!
|
|
//! The result of your execution should be similar to the following:
|
|
//! <pre>
|
|
//! Loading '../data/eil101.tsp'.
|
|
//! NAME: eil101.
|
|
//! COMMENT: 101-city problem (Christofides/Eilon).
|
|
//! TYPE: TSP.
|
|
//! DIMENSION: 101.
|
|
//! EDGE_WEIGHT_TYPE: EUC_2D.
|
|
//! Loading '../data/eil101.tsp'.
|
|
//! NAME: eil101.
|
|
//! COMMENT: 101-city problem (Christofides/Eilon).
|
|
//! EOF.
|
|
//! TYPE: TSP.
|
|
//! DIMENSION: 101.
|
|
//! EDGE_WEIGHT_TYPE: EUC_2D.
|
|
//! EOF.
|
|
//! Loading '../data/eil101.tsp'.
|
|
//! NAME: eil101.
|
|
//! COMMENT: 101-city problem (Christofides/Eilon).
|
|
//! TYPE: TSP.
|
|
//! DIMENSION: 101.
|
|
//! EDGE_WEIGHT_TYPE: EUC_2D.
|
|
//! EOF.
|
|
//! Loading '../data/eil101.tsp'.
|
|
//! NAME: eil101.
|
|
//! COMMENT: 101-city problem (Christofides/Eilon).
|
|
//! TYPE: TSP.
|
|
//! DIMENSION: 101.
|
|
//! EDGE_WEIGHT_TYPE: EUC_2D.
|
|
//! EOF.
|
|
//! STOP in eoGenContinue: Reached maximum number of generations [100/100]
|
|
//! </pre>
|
|
//!
|
|
//!
|
|
//! \section paraIntro Introducing parallelism
|
|
//!
|
|
//! Creating parallel programs with ParadisEO-PEO represents an easy task once you have the basic structure for your program. For experimentation,
|
|
//! in the <b>main.cpp</b> file, replace the line
|
|
//! <pre>
|
|
//! peo<b>Seq</b>PopEval< Route > eaPopEval( full_eval );
|
|
//! </pre>
|
|
//! with
|
|
//! <pre>
|
|
//! peo<b>Para</b>PopEval< Route > eaPopEval( full_eval );
|
|
//! </pre>
|
|
//! The second line only tells that we would like to evaluate individuals in parallel - this is very interesting if you have a time consuming fitness
|
|
//! evaluation function. If you take another look on the <b>schema.xml</b> XML file you will see the last two nodes being marked as slaves (the "num_workers"
|
|
//! attribute - these nodes will be used for computing the fitness of the individuals.
|
|
//!
|
|
//! At this point you only have to recompile your program and to launch it again - as we are not using a time consuming fitness fitness function, the
|
|
//! effects might not be visible - you may increase the number of individuals to experiment.
|