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