bug corrected on some Ctors
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1215 331e1502-861f-0410-8da2-ba01fb791d7f
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1 changed files with 96 additions and 65 deletions
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@ -38,8 +38,8 @@
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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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@ -55,140 +55,171 @@
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#include <selection/moeoDetTournamentSelect.h>
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/**
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* IBEA (Indicator-Based Evolutionary Algorithm) as described in:
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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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{
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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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* Simple ctor with a eoGenOp.
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* @param _maxGen number of generations before stopping
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* @param _eval evaluation function
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* @param _op 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 (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoGenOp < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
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defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select(2),
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fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, dummyDiversityAssignment), genBreed(select, _op), breed(genBreed)
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{}
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/**
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* Simple ctor with a eoTransform.
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* @param _maxGen number of generations before stopping
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* @param _eval evaluation function
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* @param _op 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 (unsigned int _maxGen, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
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defaultGenContinuator(_maxGen), continuator(defaultGenContinuator), popEval(_eval), select(2),
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fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, dummyDiversityAssignment), genBreed(select, _op), breed(genBreed)
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{}
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/**
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* Ctor with a crossover, a mutation and their corresponding rates.
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* @param _maxGen 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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* 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), popEval(_eval), select (2),
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fitnessAssignment(_metric, _kappa), replace (fitnessAssignment, dummyDiversityAssignment), defaultSGAGenOp(_crossover, _pCross, _mutation, _pMut),
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genBreed (select, defaultSGAGenOp), breed (genBreed)
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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 continuator (instead of _maxGen) and a eoGenOp.
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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 operator
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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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continuator(_continuator), popEval(_eval), select(2),
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fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, dummyDiversityAssignment), genBreed(select, _op), breed(genBreed)
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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 continuator (instead of _maxGen) and a eoTransform.
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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 _eval evaluation function
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* @param _op variation operator
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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, eoEvalFunc < MOEOT > & _eval, eoTransform < MOEOT > & _op, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa=0.05) :
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continuator(_continuator), popEval(_eval), select(2),
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fitnessAssignment(_metric, _kappa), replace(fitnessAssignment, dummyDiversityAssignment), genBreed(select, _op), breed(genBreed)
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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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* Apply a few generation of evolution to the population _pop until the stopping criteria is verified.
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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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dummyDiversityAssignment(_pop);
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do
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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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// 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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while (continuator (_pop));
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}
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protected:
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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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eoPopLoopEval < MOEOT > popEval;
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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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/** 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 > dummyDiversityAssignment;
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/** elitist replacement */
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moeoEnvironmentalReplacement < MOEOT > replace;
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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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};
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#endif /*MOEOIBEA_H_*/
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