update metric
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@264 331e1502-861f-0410-8da2-ba01fb791d7f
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liefooga
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5 changed files with 570 additions and 358 deletions
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@ -2,7 +2,7 @@
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//-----------------------------------------------------------------------------
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// moeoBinaryMetricSavingUpdater.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2006
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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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@ -19,76 +19,70 @@
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#include <utils/eoUpdater.h>
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#include <metric/moeoMetric.h>
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/**
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* This class allows to save the progression of a binary metric comparing the fitness values of the current population (or archive)
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* with the fitness values of the population (or archive) of the generation (n-1) into a file
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/**
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* This class allows to save the progression of a binary metric comparing the objective vectors of the current population (or archive)
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* with the objective vectors of the population (or archive) of the generation (n-1) into a file
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*/
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template < class EOT > class moeoBinaryMetricSavingUpdater:public eoUpdater
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template < class MOEOT >
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class moeoBinaryMetricSavingUpdater : public eoUpdater
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{
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public:
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/**
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* The fitness type of a solution
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*/
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typedef typename EOT::Fitness EOFitness;
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/**
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* The objective vector type of a solution
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*/
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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 binary metric comparing two Pareto sets
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* @param _pop the main population
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* @param _filename the target filename
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*/
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moeoBinaryMetricSavingUpdater (moeoVectorVsVectorBM < EOT, double >&_metric,
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const eoPop < EOT > &_pop,
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std::string _filename):metric (_metric),
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pop (_pop), filename (_filename), counter (1)
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{
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}
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/**
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* Ctor
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* @param _metric the binary metric comparing two Pareto sets
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* @param _pop the main population
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* @param _filename the target filename
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*/
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moeoBinaryMetricSavingUpdater (moeoVectorVsVectorBinaryMetric < ObjectiveVector, double > & _metric, const eoPop < MOEOT > & _pop, std::string _filename) :
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metric(_metric), pop(_pop), filename(_filename), counter(1)
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{}
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/**
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* Saves the metric's value for the current generation
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*/
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void operator () ()
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{
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if (pop.size ())
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{
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if (firstGen)
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{
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firstGen = false;
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}
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else
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{
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// creation of the two Pareto sets
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std::vector < EOFitness > from;
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std::vector < EOFitness > to;
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for (unsigned i = 0; i < pop.size (); i++)
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from.push_back (pop[i].fitness ());
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for (unsigned i = 0; i < oldPop.size (); i++)
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to.push_back (oldPop[i].fitness ());
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// writing the result into the file
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std::ofstream f (filename.c_str (), std::ios::app);
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f << counter++ << ' ' << metric (from, to) << std::endl;
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f.close ();
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}
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oldPop = pop;
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}
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}
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/**
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* Saves the metric's value for the current generation
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*/
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void operator()() {
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if (pop.size()) {
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if (firstGen) {
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firstGen = false;
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}
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else {
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// creation of the two Pareto sets
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std::vector < ObjectiveVector > from;
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std::vector < ObjectiveVector > to;
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for (unsigned i=0; i<pop.size(); i++)
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from.push_back(pop[i].objectiveVector());
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for (unsigned i=0 ; i<oldPop.size(); i++)
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to.push_back(oldPop[i].objectiveVector());
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// writing the result into the file
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std::ofstream f (filename.c_str(), std::ios::app);
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f << counter++ << ' ' << metric(from,to) << std::endl;
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f.close();
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}
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oldPop = pop;
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}
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}
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private:
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/** binary metric comparing two Pareto sets */
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moeoVectorVsVectorBM < EOT, double >&metric;
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/** main population */
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const eoPop < EOT > &pop;
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/** (n-1) population */
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eoPop < EOT > oldPop;
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/** target filename */
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std::string filename;
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/** is it the first generation ? */
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bool firstGen;
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/** counter */
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unsigned counter;
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/** binary metric comparing two Pareto sets */
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moeoVectorVsVectorBinaryMetric < ObjectiveVector, double > & metric;
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/** main population */
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const eoPop < MOEOT > & pop;
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/** (n-1) population */
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eoPop< MOEOT > oldPop;
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/** target filename */
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std::string filename;
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/** is it the first generation ? */
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bool firstGen;
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/** counter */
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unsigned counter;
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};
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#endif /*MOEOBINARYMETRICSAVINGUPDATER_H_ */
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#endif /*MOEOBINARYMETRICSAVINGUPDATER_H_*/
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@ -2,7 +2,7 @@
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//-----------------------------------------------------------------------------
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// moeoContributionMetric.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2006
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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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@ -17,100 +17,82 @@
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/**
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* The contribution metric evaluates the proportion of non-dominated solutions given by a Pareto set relatively to another Pareto set
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*
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* (Meunier, Talbi, Reininger: 'A multiobjective genetic algorithm for radio network optimization', in Proc. of the 2000 Congress on Evolutionary Computation, IEEE Press, pp. 317-324)
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*/
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template < class EOT > class moeoContributionMetric:public moeoVectorVsVectorBM < EOT,
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double >
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template < class ObjectiveVector >
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class moeoContributionMetric : public moeoVectorVsVectorBinaryMetric < ObjectiveVector, double >
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{
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public:
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/**
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* The fitness type of a solution
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*/
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typedef typename EOT::Fitness EOFitness;
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/**
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* Returns the contribution of the Pareto set '_set1' relatively to the Pareto set '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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double operator () (const std::vector < EOFitness > &_set1,
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const std::vector < EOFitness > &_set2)
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{
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unsigned c = card_C (_set1, _set2);
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unsigned w1 = card_W (_set1, _set2);
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unsigned n1 = card_N (_set1, _set2);
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unsigned w2 = card_W (_set2, _set1);
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unsigned n2 = card_N (_set2, _set1);
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return (double) (c / 2.0 + w1 + n1) / (c + w1 + n1 + w2 + n2);
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}
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/**
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* Returns the contribution of the Pareto set '_set1' relatively to the Pareto set '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned c = card_C(_set1, _set2);
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unsigned w1 = card_W(_set1, _set2);
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unsigned n1 = card_N(_set1, _set2);
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unsigned w2 = card_W(_set2, _set1);
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unsigned n2 = card_N(_set2, _set1);
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return (double) (c / 2.0 + w1 + n1) / (c + w1 + n1 + w2 + n2);
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}
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private:
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/**
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* Returns the number of solutions both in '_set1' and '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_C (const std::vector < EOFitness > &_set1,
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const std::vector < EOFitness > &_set2)
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{
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unsigned c = 0;
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for (unsigned i = 0; i < _set1.size (); i++)
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for (unsigned j = 0; j < _set2.size (); j++)
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if (_set1[i] == _set2[j])
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{
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c++;
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break;
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}
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return c;
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}
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/**
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* Returns the number of solutions both in '_set1' and '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_C (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned c=0;
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for (unsigned i=0; i<_set1.size(); i++)
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for (unsigned j=0; j<_set2.size(); j++)
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if (_set1[i] == _set2[j]) {
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c++;
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break;
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}
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return c;
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}
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/**
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* Returns the number of solutions in '_set1' dominating at least one solution of '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_W (const std::vector < EOFitness > &_set1,
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const std::vector < EOFitness > &_set2)
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{
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unsigned w = 0;
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for (unsigned i = 0; i < _set1.size (); i++)
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for (unsigned j = 0; j < _set2.size (); j++)
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if (_set1[i].dominates (_set2[j]))
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{
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w++;
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break;
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}
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return w;
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}
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/**
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* Returns the number of solutions in '_set1' dominating at least one solution of '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_W (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned w=0;
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for (unsigned i=0; i<_set1.size(); i++)
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for (unsigned j=0; j<_set2.size(); j++)
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if (_set1[i].dominates(_set2[j])) {
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w++;
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break;
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}
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return w;
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}
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/**
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* Returns the number of solutions in '_set1' having no relation of dominance with those from '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_N (const std::vector < EOFitness > &_set1,
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const std::vector < EOFitness > &_set2)
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{
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unsigned n = 0;
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for (unsigned i = 0; i < _set1.size (); i++)
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{
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bool domin_rel = false;
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for (unsigned j = 0; j < _set2.size (); j++)
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if (_set1[i].dominates (_set2[j]) || _set2[j].dominates (_set1[i]))
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{
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domin_rel = true;
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break;
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}
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if (!domin_rel)
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n++;
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}
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return n;
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}
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/**
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* Returns the number of solutions in '_set1' having no relation of dominance with those from '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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unsigned card_N (const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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unsigned n=0;
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for (unsigned i=0; i<_set1.size(); i++) {
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bool domin_rel = false;
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for (unsigned j=0; j<_set2.size(); j++)
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if (_set1[i].dominates(_set2[j]) || _set2[j].dominates(_set1 [i])) {
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domin_rel = true;
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break;
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}
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if (! domin_rel)
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n++;
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}
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return n;
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}
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};
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#endif /*MOEOCONTRIBUTIONMETRIC_H_ */
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#endif /*MOEOCONTRIBUTIONMETRIC_H_*/
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//-----------------------------------------------------------------------------
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// moeoEntropyMetric.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2006
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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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@ -16,164 +16,162 @@
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#include <metric/moeoMetric.h>
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/**
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* The entropy gives an idea of the diversity of a Pareto set relatively to another Pareto set
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*
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* The entropy gives an idea of the diversity of a Pareto set relatively to another
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* (Basseur, Seynhaeve, Talbi: 'Design of Multi-objective Evolutionary Algorithms: Application to the Flow-shop Scheduling Problem', in Proc. of the 2002 Congress on Evolutionary Computation, IEEE Press, pp. 1155-1156)
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*/
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template < class EOT > class moeoEntropyMetric:public moeoVectorVsVectorBM < EOT,
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double >
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template < class ObjectiveVector >
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class moeoEntropyMetric : public moeoVectorVsVectorBinaryMetric < ObjectiveVector, double >
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{
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public:
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/**
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* The fitness type of a solution
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*/
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typedef typename EOT::Fitness EOFitness;
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/**
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* Returns the entropy of the Pareto set '_set1' relatively to the Pareto set '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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// normalization
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std::vector< ObjectiveVector > set1 = _set1;
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std::vector< ObjectiveVector > set2= _set2;
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removeDominated (set1);
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removeDominated (set2);
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prenormalize (set1);
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normalize (set1);
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normalize (set2);
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/**
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* Returns the entropy of the Pareto set '_set1' relatively to the Pareto set '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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double operator () (const std::vector < EOFitness > &_set1,
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const std::vector < EOFitness > &_set2)
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{
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// normalization
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std::vector < EOFitness > set1 = _set1;
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std::vector < EOFitness > set2 = _set2;
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removeDominated (set1);
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removeDominated (set2);
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prenormalize (set1);
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normalize (set1);
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normalize (set2);
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// making of PO*
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std::vector< ObjectiveVector > star; // rotf :-)
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computeUnion (set1, set2, star);
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removeDominated (star);
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// making of PO*
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std::vector < EOFitness > star; // rotf :-)
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computeUnion (set1, set2, star);
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removeDominated (star);
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// making of PO1 U PO*
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std::vector< ObjectiveVector > union_set1_star; // rotf again ...
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computeUnion (set1, star, union_set1_star);
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// making of PO1 U PO*
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std::vector < EOFitness > union_set1_star; // rotf again ...
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computeUnion (set1, star, union_set1_star);
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unsigned C = union_set1_star.size();
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float omega=0;
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float entropy=0;
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unsigned C = union_set1_star.size ();
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float omega = 0;
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float entropy = 0;
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for (unsigned i = 0; i < C; i++)
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{
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unsigned N_i =
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howManyInNicheOf (union_set1_star, union_set1_star[i],
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star.size ());
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unsigned n_i =
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howManyInNicheOf (set1, union_set1_star[i], star.size ());
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if (n_i > 0)
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{
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omega += 1.0 / N_i;
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entropy +=
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(float) n_i / (N_i * C) * log (((float) n_i / C) / log (2.0));
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}
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}
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entropy /= -log (omega);
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entropy *= log (2.0);
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return entropy;
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}
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for (unsigned i=0 ; i<C ; i++) {
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unsigned N_i = howManyInNicheOf (union_set1_star, union_set1_star[i], star.size());
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unsigned n_i = howManyInNicheOf (set1, union_set1_star[i], star.size());
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if (n_i > 0) {
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omega += 1.0 / N_i;
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entropy += (float) n_i / (N_i * C) * log (((float) n_i / C) / log (2.0));
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}
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}
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entropy /= - log (omega);
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entropy *= log (2.0);
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return entropy;
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}
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private:
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std::vector < double >vect_min_val;
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std::vector < double >vect_max_val;
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/** vector of min values */
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std::vector<double> vect_min_val;
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/** vector of max values */
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std::vector<double> vect_max_val;
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void removeDominated (std::vector < EOFitness > &_f)
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{
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for (unsigned i = 0; i < _f.size (); i++)
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{
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bool dom = false;
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for (unsigned j = 0; j < _f.size (); j++)
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if (i != j && _f[j].dominates (_f[i]))
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{
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dom = true;
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break;
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}
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if (dom)
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{
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_f[i] = _f.back ();
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_f.pop_back ();
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i--;
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}
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}
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}
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void prenormalize (const std::vector < EOFitness > &_f)
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{
|
||||
vect_min_val.clear ();
|
||||
vect_max_val.clear ();
|
||||
/**
|
||||
* Removes the dominated individuals contained in _f
|
||||
* @param _f a Pareto set
|
||||
*/
|
||||
void removeDominated(std::vector < ObjectiveVector > & _f) {
|
||||
for (unsigned i=0 ; i<_f.size(); i++) {
|
||||
bool dom = false;
|
||||
for (unsigned j=0; j<_f.size(); j++)
|
||||
if (i != j && _f[j].dominates(_f[i])) {
|
||||
dom = true;
|
||||
break;
|
||||
}
|
||||
if (dom) {
|
||||
_f[i] = _f.back();
|
||||
_f.pop_back();
|
||||
i--;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (unsigned char i = 0; i < EOFitness::fitness_traits::nObjectives ();
|
||||
i++)
|
||||
{
|
||||
float min_val = _f.front ()[i], max_val = min_val;
|
||||
for (unsigned j = 1; j < _f.size (); j++)
|
||||
{
|
||||
if (_f[j][i] < min_val)
|
||||
min_val = _f[j][i];
|
||||
if (_f[j][i] > max_val)
|
||||
max_val = _f[j][i];
|
||||
}
|
||||
vect_min_val.push_back (min_val);
|
||||
vect_max_val.push_back (max_val);
|
||||
}
|
||||
}
|
||||
|
||||
void normalize (std::vector < EOFitness > &_f)
|
||||
{
|
||||
for (unsigned i = 0; i < EOFitness::fitness_traits::nObjectives (); i++)
|
||||
for (unsigned j = 0; j < _f.size (); j++)
|
||||
_f[j][i] =
|
||||
(_f[j][i] - vect_min_val[i]) / (vect_max_val[i] - vect_min_val[i]);
|
||||
}
|
||||
/**
|
||||
* Prenormalization
|
||||
* @param _f a Pareto set
|
||||
*/
|
||||
void prenormalize (const std::vector< ObjectiveVector > & _f) {
|
||||
vect_min_val.clear();
|
||||
vect_max_val.clear();
|
||||
|
||||
void computeUnion (const std::vector < EOFitness > &_f1,
|
||||
const std::vector < EOFitness > &_f2,
|
||||
std::vector < EOFitness > &_f)
|
||||
{
|
||||
_f = _f1;
|
||||
for (unsigned i = 0; i < _f2.size (); i++)
|
||||
{
|
||||
bool b = false;
|
||||
for (unsigned j = 0; j < _f1.size (); j++)
|
||||
if (_f1[j] == _f2[i])
|
||||
{
|
||||
b = true;
|
||||
break;
|
||||
}
|
||||
if (!b)
|
||||
_f.push_back (_f2[i]);
|
||||
}
|
||||
}
|
||||
for (unsigned char i=0 ; i<ObjectiveVector::nObjectives(); i++) {
|
||||
float min_val = _f.front()[i], max_val = min_val;
|
||||
for (unsigned j=1 ; j<_f.size(); j++) {
|
||||
if (_f[j][i] < min_val)
|
||||
min_val = _f[j][i];
|
||||
if (_f[j][i]>max_val)
|
||||
max_val = _f[j][i];
|
||||
}
|
||||
vect_min_val.push_back(min_val);
|
||||
vect_max_val.push_back (max_val);
|
||||
}
|
||||
}
|
||||
|
||||
unsigned howManyInNicheOf (const std::vector < EOFitness > &_f,
|
||||
const EOFitness & _s, unsigned _size)
|
||||
{
|
||||
unsigned n = 0;
|
||||
for (unsigned i = 0; i < _f.size (); i++)
|
||||
{
|
||||
if (euclidianDistance (_f[i], _s) < (_s.size () / (double) _size))
|
||||
n++;
|
||||
}
|
||||
return n;
|
||||
}
|
||||
|
||||
double euclidianDistance (const EOFitness & _set1, const EOFitness & _to,
|
||||
unsigned _deg = 2)
|
||||
{
|
||||
double dist = 0;
|
||||
for (unsigned i = 0; i < _set1.size (); i++)
|
||||
dist += pow (fabs (_set1[i] - _to[i]), (int) _deg);
|
||||
return pow (dist, 1.0 / _deg);
|
||||
}
|
||||
/**
|
||||
* Normalization
|
||||
* @param _f a Pareto set
|
||||
*/
|
||||
void normalize (std::vector< ObjectiveVector > & _f) {
|
||||
for (unsigned i=0 ; i<ObjectiveVector::nObjectives(); i++)
|
||||
for (unsigned j=0; j<_f.size(); j++)
|
||||
_f[j][i] = (_f[j][i] - vect_min_val[i]) / (vect_max_val[i] - vect_min_val[i]);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Computation of the union of _f1 and _f2 in _f
|
||||
* @param _f1 the first Pareto set
|
||||
* @param _f2 the second Pareto set
|
||||
* @param _f the final Pareto set
|
||||
*/
|
||||
void computeUnion(const std::vector< ObjectiveVector > & _f1, const std::vector< ObjectiveVector > & _f2, std::vector< ObjectiveVector > & _f) {
|
||||
_f = _f1 ;
|
||||
for (unsigned i=0; i<_f2.size(); i++) {
|
||||
bool b = false;
|
||||
for (unsigned j=0; j<_f1.size(); j ++)
|
||||
if (_f1[j] == _f2[i]) {
|
||||
b = true;
|
||||
break;
|
||||
}
|
||||
if (! b)
|
||||
_f.push_back(_f2[i]);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* How many in niche
|
||||
*/
|
||||
unsigned howManyInNicheOf (const std::vector< ObjectiveVector > & _f, const ObjectiveVector & _s, unsigned _size) {
|
||||
unsigned n=0;
|
||||
for (unsigned i=0 ; i<_f.size(); i++) {
|
||||
if (euclidianDistance(_f[i], _s) < (_s.size() / (double) _size))
|
||||
n++;
|
||||
}
|
||||
return n;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Euclidian distance
|
||||
*/
|
||||
double euclidianDistance (const ObjectiveVector & _set1, const ObjectiveVector & _to, unsigned _deg = 2) {
|
||||
double dist=0;
|
||||
for (unsigned i=0; i<_set1.size(); i++)
|
||||
dist += pow(fabs(_set1[i] - _to[i]), (int)_deg);
|
||||
return pow(dist, 1.0 / _deg);
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
#endif /*MOEOENTROPYMETRIC_H_ */
|
||||
#endif /*MOEOENTROPYMETRIC_H_*/
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
//-----------------------------------------------------------------------------
|
||||
// moeoMetric.h
|
||||
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2006
|
||||
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
|
||||
/*
|
||||
This library...
|
||||
|
||||
|
|
@ -16,91 +16,58 @@
|
|||
#include <eoFunctor.h>
|
||||
|
||||
/**
|
||||
* Base class for performance metrics (also called quality indicators)
|
||||
* Base class for performance metrics (also known as quality indicators).
|
||||
*/
|
||||
class moeoMetric:public eoFunctorBase
|
||||
{
|
||||
};
|
||||
class moeoMetric : public eoFunctorBase
|
||||
{};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for unary metrics
|
||||
* Base class for unary metrics.
|
||||
*/
|
||||
template < class A, class R > class moeoUM:public eoUF < A, R >,
|
||||
public moeoMetric
|
||||
{
|
||||
};
|
||||
template < class A, class R >
|
||||
class moeoUnaryMetric : public eoUF < A, R >, public moeoMetric
|
||||
{};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics
|
||||
* Base class for binary metrics.
|
||||
*/
|
||||
template < class A1, class A2, class R > class moeoBM:public eoBF < A1, A2, R >,
|
||||
public moeoMetric
|
||||
{
|
||||
};
|
||||
template < class A1, class A2, class R >
|
||||
class moeoBinaryMetric : public eoBF < A1, A2, R >, public moeoMetric
|
||||
{};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for unary metrics dedicated to the performance evaluation of a single solution's Pareto fitness
|
||||
* Base class for unary metrics dedicated to the performance evaluation of a single solution's objective vector.
|
||||
*/
|
||||
template < class EOT, class R, class EOFitness = typename EOT::Fitness > class moeoSolutionUM:public moeoUM <
|
||||
const
|
||||
EOFitness &,
|
||||
R >
|
||||
{
|
||||
};
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoSolutionUnaryMetric : public moeoUnaryMetric < const ObjectiveVector &, R >
|
||||
{};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of Pareto fitnesses)
|
||||
* Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of objective vectors)
|
||||
*/
|
||||
template < class EOT, class R, class EOFitness = typename EOT::Fitness > class moeoVectorUM:public moeoUM <
|
||||
const
|
||||
std::vector <
|
||||
EOFitness > &,
|
||||
R >
|
||||
{
|
||||
};
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoVectorUnaryMetric : public moeoUnaryMetric < const std::vector < ObjectiveVector > &, R >
|
||||
{};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics dedicated to the performance comparison between two solutions's Pareto fitnesses
|
||||
* Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors.
|
||||
*/
|
||||
template < class EOT, class R, class EOFitness = typename EOT::Fitness > class moeoSolutionVsSolutionBM:public moeoBM <
|
||||
const
|
||||
EOFitness &, const
|
||||
EOFitness &,
|
||||
R >
|
||||
{
|
||||
};
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoSolutionVsSolutionBinaryMetric : public moeoBinaryMetric < const ObjectiveVector &, const ObjectiveVector &, R >
|
||||
{};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics dedicated to the performance comparison between a Pareto set (a vector of Pareto fitnesses) and a single solution's Pareto fitness
|
||||
* Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of objective vectors)
|
||||
*/
|
||||
template < class EOT, class R, class EOFitness = typename EOT::Fitness > class moeoVectorVsSolutionBM:public moeoBM <
|
||||
const
|
||||
std::vector <
|
||||
EOFitness > &, const
|
||||
EOFitness &,
|
||||
R >
|
||||
{
|
||||
};
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoVectorVsVectorBinaryMetric : public moeoBinaryMetric < const std::vector < ObjectiveVector > &, const std::vector < ObjectiveVector > &, R >
|
||||
{};
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of Pareto fitnesses)
|
||||
*/
|
||||
template < class EOT, class R, class EOFitness = typename EOT::Fitness > class moeoVectorVsVectorBM:public moeoBM <
|
||||
const
|
||||
std::vector <
|
||||
EOFitness > &, const
|
||||
std::vector <
|
||||
EOFitness > &,
|
||||
R >
|
||||
{
|
||||
};
|
||||
|
||||
|
||||
#endif /*MOEOMETRIC_H_ */
|
||||
#endif /*MOEOMETRIC_H_*/
|
||||
|
|
|
|||
|
|
@ -0,0 +1,271 @@
|
|||
// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// moeoNormalizedSolutionVsSolutionBinaryMetric.h
|
||||
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
|
||||
/*
|
||||
This library...
|
||||
|
||||
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
|
||||
*/
|
||||
//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_
|
||||
#define MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_
|
||||
|
||||
#include <stdexcept>
|
||||
#include <metric/moeoMetric.h>
|
||||
|
||||
|
||||
/**
|
||||
* Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors using normalized values.
|
||||
* Then, indicator values lie in the interval [-1,1].
|
||||
* Note that you have to set the bounds for every objective before using the operator().
|
||||
*/
|
||||
template < class ObjectiveVector, class R >
|
||||
class moeoNormalizedSolutionVsSolutionBinaryMetric : public moeoSolutionVsSolutionBinaryMetric < ObjectiveVector, R >
|
||||
{
|
||||
public:
|
||||
|
||||
/**
|
||||
* Default ctr for any moeoNormalizedSolutionVsSolutionBinaryMetric object
|
||||
*/
|
||||
moeoNormalizedSolutionVsSolutionBinaryMetric()
|
||||
{
|
||||
bounds.resize(ObjectiveVector::Traits::nObjectives());
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Sets the lower bound (_min) and the upper bound (_max) for the objective _obj
|
||||
* _min lower bound
|
||||
* _max upper bound
|
||||
* _obj the objective index
|
||||
*/
|
||||
void setup(double _min, double _max, unsigned _obj)
|
||||
{
|
||||
if (_min == _max)
|
||||
{
|
||||
_min -= tiny();
|
||||
_max += tiny();
|
||||
}
|
||||
bounds[_obj] = eoRealInterval(_min, _max);
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the lower bound and the upper bound for the objective _obj using a eoRealInterval object
|
||||
* _realInterval the eoRealInterval object
|
||||
* _obj the objective index
|
||||
*/
|
||||
virtual void setup(eoRealInterval _realInterval, unsigned _obj)
|
||||
{
|
||||
bounds[_obj] = _realInterval;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Returns a very small value that can be used to avoid extreme cases (where the min bound == the max bound)
|
||||
*/
|
||||
static double tiny()
|
||||
{
|
||||
return 1e-6;
|
||||
}
|
||||
|
||||
|
||||
protected:
|
||||
|
||||
/** the bounds for every objective (bounds[i] = bounds for the objective i) */
|
||||
std::vector < eoRealInterval > bounds;
|
||||
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* Additive epsilon binary metric allowing to compare two objective vectors as proposed in
|
||||
* Zitzler E., Thiele L., Laumanns M., Fonseca C. M., Grunert da Fonseca V.:
|
||||
* Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), pp.117–132 (2003).
|
||||
*/
|
||||
template < class ObjectiveVector >
|
||||
class moeoAdditiveEpsilonBinaryMetric : public moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double >
|
||||
{
|
||||
public:
|
||||
|
||||
/**
|
||||
* Returns the minimal distance by which the objective vector _o1 must be translated in all objectives
|
||||
* so that it weakly dominates the objective vector _o2
|
||||
* @warning don't forget to set the bounds for every objective before the call of this function
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
*/
|
||||
double operator()(const ObjectiveVector & _o1, const ObjectiveVector & _o2)
|
||||
{
|
||||
// computation of the epsilon value for the first objective
|
||||
double result = epsilon(_o1, _o2, 0);
|
||||
// computation of the epsilon value for the other objectives
|
||||
double tmp;
|
||||
for (unsigned i=1; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
tmp = epsilon(_o1, _o2, i);
|
||||
result = std::max(result, tmp);
|
||||
}
|
||||
// returns the maximum epsilon value
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
private:
|
||||
|
||||
/** the bounds for every objective */
|
||||
using moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > :: bounds;
|
||||
|
||||
|
||||
/**
|
||||
* Returns the epsilon value by which the objective vector _o1 must be translated in the objective _obj
|
||||
* so that it dominates the objective vector _o2
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
* @param _obj the index of the objective
|
||||
*/
|
||||
double epsilon(const ObjectiveVector & _o1, const ObjectiveVector & _o2, const unsigned _obj)
|
||||
{
|
||||
double result;
|
||||
// if the objective _obj have to be minimized
|
||||
if (ObjectiveVector::Traits::minimizing(_obj))
|
||||
{
|
||||
// _o1[_obj] - _o2[_obj]
|
||||
result = ( (_o1[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() ) - ( (_o2[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() );
|
||||
}
|
||||
// if the objective _obj have to be maximized
|
||||
else
|
||||
{
|
||||
// _o2[_obj] - _o1[_obj]
|
||||
result = ( (_o2[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() ) - ( (_o1[_obj] - bounds[_obj].minimum()) / bounds[_obj].range() );
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* Hypervolume binary metric allowing to compare two objective vectors as proposed in
|
||||
* Zitzler E., Künzli S.: Indicator-Based Selection in Multiobjective Search. In Parallel Problem Solving from Nature (PPSN VIII).
|
||||
* Lecture Notes in Computer Science 3242, Springer, Birmingham, UK pp.832–842 (2004).
|
||||
* This indicator is based on the hypervolume concept introduced in
|
||||
* Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study.
|
||||
* Parallel Problem Solving from Nature (PPSN-V), pp.292-301 (1998).
|
||||
*/
|
||||
template < class ObjectiveVector >
|
||||
class moeoHypervolumeBinaryMetric : public moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double >
|
||||
{
|
||||
public:
|
||||
|
||||
/**
|
||||
* Ctor
|
||||
* @param _rho value used to compute the reference point from the worst values for each objective (default : 1.1)
|
||||
*/
|
||||
moeoHypervolumeBinaryMetric(double _rho = 1.1) : rho(_rho)
|
||||
{
|
||||
// not-a-maximization problem check
|
||||
for (unsigned i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
if (ObjectiveVector::Traits::maximizing(i))
|
||||
{
|
||||
throw std::runtime_error("Hypervolume binary metric not yet implemented for a maximization problem in moeoHypervolumeBinaryMetric");
|
||||
}
|
||||
}
|
||||
// consistency check
|
||||
if (rho < 1)
|
||||
{
|
||||
cout << "Warning, value used to compute the reference point rho for the hypervolume calculation must not be smaller than 1" << endl;
|
||||
cout << "Adjusted to 1" << endl;
|
||||
rho = 1;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Returns the volume of the space that is dominated by _o2 but not by _o1 with respect to a reference point computed using rho.
|
||||
* @warning don't forget to set the bounds for every objective before the call of this function
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
*/
|
||||
double operator()(const ObjectiveVector & _o1, const ObjectiveVector & _o2)
|
||||
{
|
||||
double result;
|
||||
// if _o1 dominates _o2
|
||||
if ( paretoComparator(_o1,_o2) )
|
||||
{
|
||||
result = - hypervolume(_o1, _o2, ObjectiveVector::Traits::nObjectives()-1);
|
||||
}
|
||||
else
|
||||
{
|
||||
result = hypervolume(_o2, _o1, ObjectiveVector::Traits::nObjectives()-1);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
private:
|
||||
|
||||
/** value used to compute the reference point from the worst values for each objective */
|
||||
double rho;
|
||||
/** the bounds for every objective */
|
||||
using moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > :: bounds;
|
||||
/** Functor to compare two objective vectors according to Pareto dominance relation */
|
||||
moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
|
||||
|
||||
/**
|
||||
* Returns the volume of the space that is dominated by _o2 but not by _o1 with respect to a reference point computed using rho for the objective _obj.
|
||||
* @param _o1 the first objective vector
|
||||
* @param _o2 the second objective vector
|
||||
* @param _obj the objective index
|
||||
* @param _flag used for iteration, if _flag=true _o2 is not talen into account (default : false)
|
||||
*/
|
||||
double hypervolume(const ObjectiveVector & _o1, const ObjectiveVector & _o2, const unsigned _obj, const bool _flag = false)
|
||||
{
|
||||
double result;
|
||||
double range = rho * bounds[_obj].range();
|
||||
double max = bounds[_obj].minimum() + range;
|
||||
// value of _1 for the objective _obj
|
||||
double v1 = _o1[_obj];
|
||||
// value of _2 for the objective _obj (if _flag=true, v2=max)
|
||||
double v2;
|
||||
if (_flag)
|
||||
{
|
||||
v2 = max;
|
||||
}
|
||||
else
|
||||
{
|
||||
v2 = _o2[_obj];
|
||||
}
|
||||
// computation of the volume
|
||||
if (_obj == 0)
|
||||
{
|
||||
if (v1 < v2)
|
||||
{
|
||||
result = (v2 - v1) / range;
|
||||
}
|
||||
else
|
||||
{
|
||||
result = 0;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if (v1 < v2)
|
||||
{
|
||||
result = ( hypervolume(_o1, _o2, _obj-1, true) * (v2 - v1) / range ) + ( hypervolume(_o1, _o2, _obj-1) * (max - v2) / range );
|
||||
}
|
||||
else
|
||||
{
|
||||
result = hypervolume(_o1, _o2, _obj-1) * (max - v2) / range;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
|
||||
#endif /*MOEONORMALIZEDSOLUTIONVSSOLUTIONBINARYMETRIC_H_*/
|
||||
Loading…
Add table
Add a link
Reference in a new issue