add some abstract classes
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@492 331e1502-861f-0410-8da2-ba01fb791d7f
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4 changed files with 252 additions and 180 deletions
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
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//-----------------------------------------------------------------------------
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// moeoBinaryIndicatorBasedFitnessAssignment.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
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/*
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This library...
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Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
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#define MOEOBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
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#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
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/**
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* moeoIndicatorBasedFitnessAssignment for binary indicators.
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*/
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template < class MOEOT >
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class moeoBinaryIndicatorBasedFitnessAssignment : public moeoIndicatorBasedFitnessAssignment < MOEOT > {};
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#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
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//-----------------------------------------------------------------------------
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// moeoIndicatorBasedFitnessAssignment.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
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/*
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This library...
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Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
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#define MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
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#include <math.h>
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#include <vector>
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#include <eoPop.h>
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#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
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#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
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#include <utils/moeoConvertPopToObjectiveVectors.h>
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/**
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* Fitness assignment sheme based on an indicator proposed in:
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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 strategy is, for instance, used in IBEA.
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*/
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template < class MOEOT >
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class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorBasedFitnessAssignment < 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.
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* @param _metric the quality indicator
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* @param _kappa the scaling factor
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*/
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moeoExpBinaryIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa)
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{}
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/**
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* Sets the fitness values for every solution contained in the population _pop
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* @param _pop the population
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*/
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void operator()(eoPop < MOEOT > & _pop)
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{
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// 1 - setting of the bounds
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setup(_pop);
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// 2 - computing every indicator values
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computeValues(_pop);
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// 3 - setting fitnesses
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setFitnesses(_pop);
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}
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/**
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* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
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* @param _pop the population
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* @param _objVec the objective vector
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*/
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void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
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{
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std::vector < double > v;
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v.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_objVec, _pop[i].objectiveVector());
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}
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
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}
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}
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/**
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* Updates the fitness values of the whole population _pop by taking the adding of the objective vector _objVec into account
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* and returns the fitness value of _objVec.
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* @param _pop the population
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* @param _objVec the objective vector
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*/
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double updateByAdding(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
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{
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std::vector < double > v;
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// update every fitness values to take the new individual into account
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v.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_objVec, _pop[i].objectiveVector());
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}
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
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}
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// compute the fitness of the new individual
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v.clear();
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v.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_pop[i].objectiveVector(), _objVec);
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}
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double result = 0;
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for (unsigned int i=0; i<v.size(); i++)
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{
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result -= exp(-v[i]/kappa);
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}
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return result;
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}
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protected:
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/** the quality indicator */
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moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
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/** the scaling factor */
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double kappa;
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/** the computed indicator values */
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std::vector < std::vector<double> > values;
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/**
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* Sets the bounds for every objective using the min and the max value for every objective vector of _pop
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* @param _pop the population
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*/
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void setup(const eoPop < MOEOT > & _pop)
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{
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double min, max;
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
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{
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min = _pop[0].objectiveVector()[i];
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max = _pop[0].objectiveVector()[i];
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for (unsigned int j=1; j<_pop.size(); j++)
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{
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min = std::min(min, _pop[j].objectiveVector()[i]);
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max = std::max(max, _pop[j].objectiveVector()[i]);
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}
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// setting of the bounds for the objective i
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metric.setup(min, max, i);
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}
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}
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/**
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* Compute every indicator value in values (values[i] = I(_v[i], _o))
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* @param _pop the population
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*/
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void computeValues(const eoPop < MOEOT > & _pop)
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{
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values.clear();
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values.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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values[i].resize(_pop.size());
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for (unsigned int j=0; j<_pop.size(); j++)
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{
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if (i != j)
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{
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values[i][j] = metric(_pop[i].objectiveVector(), _pop[j].objectiveVector());
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}
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}
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}
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}
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/**
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* Sets the fitness value of the whple population
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* @param _pop the population
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*/
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void setFitnesses(eoPop < MOEOT > & _pop)
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{
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness(computeFitness(i));
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}
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}
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/**
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* Returns the fitness value of the _idx th individual of the population
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* @param _idx the index
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*/
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double computeFitness(const unsigned int _idx)
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{
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double result = 0;
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for (unsigned int i=0; i<values.size(); i++)
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{
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if (i != _idx)
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{
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result -= exp(-values[i][_idx]/kappa);
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}
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}
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return result;
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}
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};
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#endif /*MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_*/
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@ -13,190 +13,12 @@
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#ifndef MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
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#define MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
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#include <math.h>
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#include <vector>
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#include <eoPop.h>
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#include <fitness/moeoFitnessAssignment.h>
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#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
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#include <utils/moeoConvertPopToObjectiveVectors.h>
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/**
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* Fitness assignment sheme based an Indicator proposed in:
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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 strategy is, for instance, used in IBEA.
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* moeoIndicatorBasedFitnessAssignment is a moeoFitnessAssignment for Indicator-based strategies.
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*/
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template < class MOEOT >
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class moeoIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < 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.
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* @param _metric the quality indicator
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* @param _kappa the scaling factor
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*/
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moeoIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa)
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{}
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/**
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* Sets the fitness values for every solution contained in the population _pop
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* @param _pop the population
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*/
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void operator()(eoPop < MOEOT > & _pop)
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{
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// 1 - setting of the bounds
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setup(_pop);
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// 2 - computing every indicator values
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computeValues(_pop);
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// 3 - setting fitnesses
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setFitnesses(_pop);
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}
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/**
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* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
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* @param _pop the population
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* @param _objVec the objective vector
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*/
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void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
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{
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std::vector < double > v;
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v.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_objVec, _pop[i].objectiveVector());
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}
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
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}
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}
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/**
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* Updates the fitness values of the whole population _pop by taking the adding of the objective vector _objVec into account
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* and returns the fitness value of _objVec.
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* @param _pop the population
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* @param _objVec the objective vector
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*/
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double updateByAdding(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
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{
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std::vector < double > v;
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// update every fitness values to take the new individual into account
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v.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_objVec, _pop[i].objectiveVector());
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}
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
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}
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// compute the fitness of the new individual
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v.clear();
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v.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_pop[i].objectiveVector(), _objVec);
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}
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double result = 0;
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for (unsigned int i=0; i<v.size(); i++)
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{
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result -= exp(-v[i]/kappa);
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}
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return result;
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}
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protected:
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/** the quality indicator */
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moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
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/** the scaling factor */
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double kappa;
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/** the computed indicator values */
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std::vector < std::vector<double> > values;
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/**
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* Sets the bounds for every objective using the min and the max value for every objective vector of _pop
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* @param _pop the population
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*/
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void setup(const eoPop < MOEOT > & _pop)
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{
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double min, max;
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
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{
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min = _pop[0].objectiveVector()[i];
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max = _pop[0].objectiveVector()[i];
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for (unsigned int j=1; j<_pop.size(); j++)
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{
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min = std::min(min, _pop[j].objectiveVector()[i]);
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max = std::max(max, _pop[j].objectiveVector()[i]);
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}
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// setting of the bounds for the objective i
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metric.setup(min, max, i);
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}
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}
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/**
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* Compute every indicator value in values (values[i] = I(_v[i], _o))
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* @param _pop the population
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*/
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void computeValues(const eoPop < MOEOT > & _pop)
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{
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values.clear();
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values.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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values[i].resize(_pop.size());
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for (unsigned int j=0; j<_pop.size(); j++)
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{
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if (i != j)
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{
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values[i][j] = metric(_pop[i].objectiveVector(), _pop[j].objectiveVector());
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}
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}
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}
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}
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/**
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* Sets the fitness value of the whple population
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* @param _pop the population
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*/
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void setFitnesses(eoPop < MOEOT > & _pop)
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{
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness(computeFitness(i));
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}
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}
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/**
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* Returns the fitness value of the _idx th individual of the population
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* @param _idx the index
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*/
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double computeFitness(const unsigned int _idx)
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{
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double result = 0;
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for (unsigned int i=0; i<values.size(); i++)
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{
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if (i != _idx)
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{
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result -= exp(-values[i][_idx]/kappa);
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}
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}
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return result;
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}
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};
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class moeoIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT > {};
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#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/
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@ -0,0 +1,24 @@
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
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//-----------------------------------------------------------------------------
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// moeoUnaryIndicatorBasedFitnessAssignment.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
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/*
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This library...
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Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOUNARYINDICATORBASEDFITNESSASSIGNMENT_H_
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#define MOEOUNARYINDICATORBASEDFITNESSASSIGNMENT_H_
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#include <fitness/moeoIndicatorBasedFitnessAssignment.h>
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/**
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* moeoIndicatorBasedFitnessAssignment for unary indicators.
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*/
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template < class MOEOT >
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class moeoUnaryIndicatorBasedFitnessAssignment : public moeoIndicatorBasedFitnessAssignment < MOEOT > {};
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#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/
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