9f24113e1c
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1215 331e1502-861f-0410-8da2-ba01fb791d7f
225 lines
10 KiB
C++
225 lines
10 KiB
C++
/*
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* <moeoIBEA.h>
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* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
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* (C) OPAC Team, LIFL, 2002-2007
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*
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* Arnaud Liefooghe
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*
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* This software is governed by the CeCILL license under French law and
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* abiding by the rules of distribution of free software. You can use,
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* modify and/ or redistribute the software under the terms of the CeCILL
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* license as circulated by CEA, CNRS and INRIA at the following URL
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* "http://www.cecill.info".
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*
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* As a counterpart to the access to the source code and rights to copy,
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* modify and redistribute granted by the license, users are provided only
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* with a limited warranty and the software's author, the holder of the
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* economic rights, and the successive licensors have only limited liability.
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*
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* In this respect, the user's attention is drawn to the risks associated
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* with loading, using, modifying and/or developing or reproducing the
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* software by the user in light of its specific status of free software,
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* that may mean that it is complicated to manipulate, and that also
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* therefore means that it is reserved for developers and experienced
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* professionals having in-depth computer knowledge. Users are therefore
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* encouraged to load and test the software's suitability as regards their
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* requirements in conditions enabling the security of their systems and/or
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* data to be ensured and, more generally, to use and operate it in the
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* same conditions as regards security.
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* The fact that you are presently reading this means that you have had
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* knowledge of the CeCILL license and that you accept its terms.
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*
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* ParadisEO WebSite : http://paradiseo.gforge.inria.fr
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* Contact: paradiseo-help@lists.gforge.inria.fr
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*
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOIBEA_H_
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#define MOEOIBEA_H_
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#include <eoBreed.h>
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#include <eoCloneOps.h>
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#include <eoContinue.h>
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#include <eoEvalFunc.h>
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#include <eoGenContinue.h>
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#include <eoGeneralBreeder.h>
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#include <eoGenOp.h>
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#include <eoPopEvalFunc.h>
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#include <eoSGAGenOp.h>
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#include <algo/moeoEA.h>
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#include <diversity/moeoDummyDiversityAssignment.h>
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#include <fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h>
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#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
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#include <replacement/moeoEnvironmentalReplacement.h>
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#include <selection/moeoDetTournamentSelect.h>
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/**
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* IBEA (Indicator-Based Evolutionary Algorithm).
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* E. Zitzler, S. Künzli, "Indicator-Based Selection in Multiobjective Search", Proc. 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp. 832-842, Birmingham, UK (2004).
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* This class builds the IBEA algorithm only by using the fine-grained components of the ParadisEO-MOEO framework.
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*/
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template < class MOEOT >
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class moeoIBEA : public moeoEA < MOEOT >
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{
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public:
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/** The type of objective vector */
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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/**
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* Ctor with a crossover, a mutation and their corresponding rates.
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* @param _maxGen maximum number of generations before stopping
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* @param _eval evaluation function
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* @param _crossover crossover
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* @param _pCross crossover probability
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* @param _mutation mutation
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* @param _pMut mutation probability
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* @param _metric metric
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* @param _kappa scaling factor kappa
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*/
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moeoIBEA (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoQuadOp < MOEOT > & _crossover, double _pCross, eoMonOp < MOEOT > & _mutation, double _pMut, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
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defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), eval(_eval), defaultPopEval(_eval), popEval(defaultPopEval), select (2), selectMany(select,0.0), selectTransform(defaultSelect, defaultTransform), defaultSGAGenOp(_crossover, _pCross, _mutation, _pMut), genBreed (select, defaultSGAGenOp), breed (genBreed), fitnessAssignment(_metric, _kappa), replace (fitnessAssignment, diversityAssignment)
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{}
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/**
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* Ctor with a eoContinue and a eoGenOp.
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* @param _continuator stopping criteria
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* @param _eval evaluation function
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* @param _op variation operators
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* @param _metric metric
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* @param _kappa scaling factor kappa
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*/
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moeoIBEA (eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoGenOp < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
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defaultGenContinuator(0), continuator(_continuator), eval(_eval), defaultPopEval(_eval), popEval(defaultPopEval), select(2),
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selectMany(select,0.0), selectTransform(defaultSelect, defaultTransform), defaultSGAGenOp(defaultQuadOp, 1.0, defaultMonOp, 1.0), genBreed(select, _op), breed(genBreed), fitnessAssignment(_metric, _kappa), replace (fitnessAssignment, diversityAssignment)
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{}
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/**
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* Ctor with a eoContinue, a eoPopEval and a eoGenOp.
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* @param _continuator stopping criteria
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* @param _popEval population evaluation function
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* @param _op variation operators
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* @param _metric metric
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* @param _kappa scaling factor kappa
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*/
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moeoIBEA (eoContinue < MOEOT > & _continuator, eoPopEvalFunc < MOEOT > & _popEval, eoGenOp < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
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defaultGenContinuator(0), continuator(_continuator), eval(defaultEval), defaultPopEval(eval), popEval(_popEval), select(2),
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selectMany(select,0.0), selectTransform(defaultSelect, defaultTransform), defaultSGAGenOp(defaultQuadOp, 1.0, defaultMonOp, 1.0), genBreed(select, _op), breed(genBreed), fitnessAssignment(_metric, _kappa), replace (fitnessAssignment, diversityAssignment)
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{}
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/**
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* Ctor with a eoContinue and a eoTransform.
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* @param _continuator stopping criteria
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* @param _eval evaluation function
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* @param _transform variation operator
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* @param _metric metric
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* @param _kappa scaling factor kappa
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*/
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moeoIBEA (eoContinue < MOEOT > & _continuator, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _transform, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
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defaultGenContinuator(0), continuator(_continuator), eval(_eval), defaultPopEval(_eval), popEval(defaultPopEval),
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select(2), selectMany(select, 1.0), selectTransform(selectMany, _transform), defaultSGAGenOp(defaultQuadOp, 0.0, defaultMonOp, 0.0), genBreed(select, defaultSGAGenOp), breed(selectTransform), fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, diversityAssignment)
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{}
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/**
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* Ctor with a eoContinue, a eoPopEval and a eoTransform.
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* @param _continuator stopping criteria
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* @param _popEval population evaluation function
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* @param _transform variation operator
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* @param _metric metric
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* @param _kappa scaling factor kappa
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*/
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moeoIBEA (eoContinue < MOEOT > & _continuator, eoPopEvalFunc < MOEOT > & _popEval, eoTransform < MOEOT > & _transform, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
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defaultGenContinuator(0), continuator(_continuator), eval(defaultEval), defaultPopEval(eval), popEval(_popEval),
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select(2), selectMany(select, 1.0), selectTransform(selectMany, _transform), defaultSGAGenOp(defaultQuadOp, 0.0, defaultMonOp, 0.0), genBreed(select, defaultSGAGenOp), breed(selectTransform), fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, diversityAssignment)
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{}
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/**
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* Apply the algorithm to the population _pop until the stopping criteria is satified.
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* @param _pop the population
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*/
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virtual void operator () (eoPop < MOEOT > &_pop)
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{
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eoPop < MOEOT > offspring, empty_pop;
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popEval (empty_pop, _pop); // a first eval of _pop
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// evaluate fitness and diversity
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fitnessAssignment(_pop);
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diversityAssignment(_pop);
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do
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{
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// generate offspring, worths are recalculated if necessary
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breed (_pop, offspring);
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// eval of offspring
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popEval (_pop, offspring);
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// after replace, the new pop is in _pop. Worths are recalculated if necessary
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replace (_pop, offspring);
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}
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while (continuator (_pop));
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}
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protected:
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/** a continuator based on the number of generations (used as default) */
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eoGenContinue < MOEOT > defaultGenContinuator;
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/** stopping criteria */
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eoContinue < MOEOT > & continuator;
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/** default eval */
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class DummyEval : public eoEvalFunc < MOEOT >
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{
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public:
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void operator()(MOEOT &) {}
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}
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defaultEval;
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/** evaluation function */
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eoEvalFunc < MOEOT > & eval;
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/** default popEval */
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eoPopLoopEval < MOEOT > defaultPopEval;
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/** evaluation function used to evaluate the whole population */
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eoPopEvalFunc < MOEOT > & popEval;
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/** default select */
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class DummySelect : public eoSelect < MOEOT >
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{
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public :
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void operator()(const eoPop<MOEOT>&, eoPop<MOEOT>&) {}
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}
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defaultSelect;
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/** binary tournament selection */
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moeoDetTournamentSelect < MOEOT > select;
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/** default select many */
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eoSelectMany < MOEOT > selectMany;
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/** select transform */
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eoSelectTransform < MOEOT > selectTransform;
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/** a default crossover */
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eoQuadCloneOp < MOEOT > defaultQuadOp;
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/** a default mutation */
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eoMonCloneOp < MOEOT > defaultMonOp;
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/** an object for genetic operators (used as default) */
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eoSGAGenOp < MOEOT > defaultSGAGenOp;
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/** default transform */
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class DummyTransform : public eoTransform < MOEOT >
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{
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public :
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void operator()(eoPop<MOEOT>&) {}
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}
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defaultTransform;
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/** general breeder */
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eoGeneralBreeder < MOEOT > genBreed;
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/** breeder */
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eoBreed < MOEOT > & breed;
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/** fitness assignment used in IBEA */
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moeoExpBinaryIndicatorBasedFitnessAssignment < MOEOT > fitnessAssignment;
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/** dummy diversity assignment */
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moeoDummyDiversityAssignment < MOEOT > diversityAssignment;
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/** environmental replacement */
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moeoEnvironmentalReplacement < MOEOT > replace;
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};
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#endif /*MOEOIBEA_H_*/
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