add fitness
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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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// moeoAchievementFitnessAssignment.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 MOEOACHIEVEMENTFITNESSASSIGNMENT_H_
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#define MOEOACHIEVEMENTFITNESSASSIGNMENT_H_
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#include <vector>
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#include <eoPop.h>
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#include <fitness/moeoScalarFitnessAssignment.h>
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/**
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* Fitness assignment sheme based on the achievement scalarizing function propozed by Wiersbicki (1980).
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*/
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template < class MOEOT >
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class moeoAchievementFitnessAssignment : public moeoScalarFitnessAssignment < MOEOT >
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{
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public:
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/** the objective vector type of the solutions */
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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/**
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* Default ctor
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* @param _reference reference point vector
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* @param _lambdas weighted coefficients vector
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* @param _spn arbitrary small positive number (0 < _spn << 1)
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*/
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moeoAchievementFitnessAssignment(ObjectiveVector & _reference, std::vector < double > & _lambdas, double _spn=0.0001) : reference(_reference), lambdas(_lambdas), spn(_spn)
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{
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// consistency check
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if ((spn < 0.0) || (spn > 1.0))
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{
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std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
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spn = 0.0001;
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}
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}
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/**
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* Ctor with default values for lambdas (1/nObjectives)
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* @param _reference reference point vector
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* @param _spn arbitrary small positive number (0 < _spn << 1)
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*/
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moeoAchievementFitnessAssignment(ObjectiveVector & _reference, double _spn=0.0001) : reference(_reference), spn(_spn)
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{
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// compute the default values for lambdas
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lambdas = std::vector < double > (ObjectiveVector::nObjectives());
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for (unsigned int i=0 ; i<lambdas.size(); i++)
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{
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lambdas[i] = 1.0 / ObjectiveVector::nObjectives();
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}
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// consistency check
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if ((spn < 0.0) || (spn > 1.0))
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{
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std::cout << "Warning, the arbitrary small positive number should be > 0 and <<1, adjusted to 0.0001\n";
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spn = 0.0001;
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}
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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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virtual void operator()(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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compute(_pop[i]);
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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 deletion of the objective vector _objVec into account (nothing to do).
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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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// nothing to do ;-)
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}
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/**
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* Sets the reference point
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* @param _reference the new reference point
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*/
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void setReference(const ObjectiveVector & _reference)
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{
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reference = _reference;
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}
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private:
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/** the reference point */
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ObjectiveVector reference;
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/** the weighted coefficients vector */
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std::vector < double > lambdas;
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/** an arbitrary small positive number (0 < _spn << 1) */
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double spn;
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/**
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* Returns a big value (regarded as infinite)
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*/
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double inf() const
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{
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return std::numeric_limits<double>::max();
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}
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/**
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* Computes the fitness value for a solution
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* @param _moeo the solution
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*/
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void compute(MOEOT & _moeo)
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{
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unsigned int nobj = MOEOT::ObjectiveVector::nObjectives();
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double temp;
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double min = inf();
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double sum = 0;
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for (unsigned int obj=0; obj<nobj; obj++)
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{
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temp = lambdas[obj] * (reference[obj] - _moeo.objectiveVector()[obj]);
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min = std::min(min, temp);
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sum += temp;
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}
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_moeo.fitness(min + spn*sum);
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}
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};
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#endif /*MOEOACHIEVEMENTFITNESSASSIGNMENT_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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// moeoCriterionBasedFitnessAssignment.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 MOEOCRITERIONBASEDFITNESSASSIGNMENT_H_
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#define MOEOCRITERIONBASEDFITNESSASSIGNMENT_H_
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#include <fitness/moeoFitnessAssignment.h>
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/**
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* moeoCriterionBasedFitnessAssignment is a moeoFitnessAssignment for criterion-based strategies.
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*/
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template < class MOEOT >
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class moeoCriterionBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT > {};
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#endif /*MOEOCRITERIONBASEDFITNESSASSIGNMENT_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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// moeoDummyFitnessAssignment.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 MOEODUMMYFITNESSASSIGNMENT_H_
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#define MOEODUMMYFITNESSASSIGNMENT_H_
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#include <fitness/moeoFitnessAssignment.h>
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/**
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* moeoDummyFitnessAssignment is a moeoFitnessAssignment that gives the value '0' as the individual's fitness for a whole population if it is invalid.
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*/
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template < class MOEOT >
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class moeoDummyFitnessAssignment : public moeoFitnessAssignment < MOEOT >
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{
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public:
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/** The type for objective vector */
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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/**
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* Sets the fitness to '0' for every individuals of the population _pop if it is invalid
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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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for (unsigned int idx = 0; idx<_pop.size (); idx++)
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{
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if (_pop[idx].invalidFitness())
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{
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// set the diversity to 0
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_pop[idx].fitness(0.0);
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}
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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 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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// nothing to do... ;-)
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}
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};
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#endif /*MOEODUMMYFITNESSASSIGNMENT_H_*/
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@ -0,0 +1,239 @@
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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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// moeoFastNonDominatedSortingFitnessAssignment.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 MOEOFASTNONDOMINATEDSORTINGFITNESSASSIGNMENT_H_
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#define MOEOFASTNONDOMINATEDSORTINGFITNESSASSIGNMENT_H_
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#include <vector>
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#include <eoPop.h>
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#include <comparator/moeoObjectiveObjectiveVectorComparator.h>
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#include <comparator/moeoObjectiveVectorComparator.h>
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#include <comparator/moeoParetoObjectiveVectorComparator.h>
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#include <fitness/moeoParetoBasedFitnessAssignment.h>
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/**
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* Fitness assignment sheme based on Pareto-dominance count proposed in:
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* N. Srinivas, K. Deb, "Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms", Evolutionary Computation vol. 2, no. 3, pp. 221-248 (1994)
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* and in:
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* K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, vol. 6, no. 2 (2002).
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* This strategy is, for instance, used in NSGA and NSGA-II.
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*/
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template < class MOEOT >
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class moeoFastNonDominatedSortingFitnessAssignment : public moeoParetoBasedFitnessAssignment < MOEOT >
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{
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public:
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/** the objective vector type of the solutions */
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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/**
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* Default ctor
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*/
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moeoFastNonDominatedSortingFitnessAssignment() : comparator(paretoComparator)
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{}
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/**
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* Ctor where you can choose your own way to compare objective vectors
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* @param _comparator the functor used to compare objective vectors
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*/
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moeoFastNonDominatedSortingFitnessAssignment(moeoObjectiveVectorComparator < ObjectiveVector > & _comparator) : comparator(_comparator)
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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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// number of objectives for the problem under consideration
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unsigned int nObjectives = MOEOT::ObjectiveVector::nObjectives();
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if (nObjectives == 1)
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{
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// one objective
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oneObjective(_pop);
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}
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else if (nObjectives == 2)
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{
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// two objectives (the two objectives function is still to implement)
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mObjectives(_pop);
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}
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else if (nObjectives > 2)
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{
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// more than two objectives
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mObjectives(_pop);
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}
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else
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{
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// problem with the number of objectives
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throw std::runtime_error("Problem with the number of objectives in moeoNonDominatedSortingFitnessAssignment");
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}
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// a higher fitness is better, so the values need to be inverted
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double max = _pop[0].fitness();
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for (unsigned int i=1 ; i<_pop.size() ; i++)
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{
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max = std::max(max, _pop[i].fitness());
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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(max - _pop[i].fitness());
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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 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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for (unsigned int i=0; i<_pop.size(); i++)
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{
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// if _pop[i] is dominated by _objVec
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if ( comparator(_pop[i].objectiveVector(), _objVec) )
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{
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_pop[i].fitness(_pop[i].fitness()+1);
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}
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}
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}
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private:
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/** Functor to compare two objective vectors */
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moeoObjectiveVectorComparator < ObjectiveVector > & comparator;
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/** Functor to compare two objective vectors according to Pareto dominance relation */
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moeoParetoObjectiveVectorComparator < ObjectiveVector > paretoComparator;
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/** Functor allowing to compare two solutions according to their first objective value, then their second, and so on. */
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class ObjectiveComparator : public moeoComparator < MOEOT >
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{
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public:
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/**
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* Returns true if _moeo1 < _moeo2 on the first objective, then on the second, and so on
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* @param _moeo1 the first solution
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* @param _moeo2 the second solution
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*/
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const bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
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{
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return cmp(_moeo1.objectiveVector(), _moeo2.objectiveVector());
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}
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private:
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/** the corresponding comparator for objective vectors */
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moeoObjectiveObjectiveVectorComparator < ObjectiveVector > cmp;
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} objComparator;
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/**
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* Sets the fitness values for mono-objective problems
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* @param _pop the population
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*/
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void oneObjective (eoPop < MOEOT > & _pop)
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{
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// sorts the population in the ascending order
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std::sort(_pop.begin(), _pop.end(), objComparator);
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// assign fitness values
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unsigned int rank = 1;
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_pop[_pop.size()-1].fitness(rank);
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for (unsigned int i=_pop.size()-2; i>=0; i--)
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{
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if (_pop[i].objectiveVector() != _pop[i+1].objectiveVector())
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{
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rank++;
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}
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_pop[i].fitness(rank);
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}
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}
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/**
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* Sets the fitness values for bi-objective problems with a complexity of O(n log n), where n stands for the population size
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* @param _pop the population
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*/
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void twoObjectives (eoPop < MOEOT > & _pop)
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{
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//... TO DO !
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}
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/**
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* Sets the fitness values for problems with more than two objectives with a complexity of O(n² log n), where n stands for the population size
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* @param _pop the population
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*/
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void mObjectives (eoPop < MOEOT > & _pop)
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{
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// S[i] = indexes of the individuals dominated by _pop[i]
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std::vector < std::vector<unsigned int> > S(_pop.size());
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// n[i] = number of individuals that dominate the individual _pop[i]
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std::vector < unsigned int > n(_pop.size(), 0);
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// fronts: F[i] = indexes of the individuals contained in the ith front
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std::vector < std::vector<unsigned int> > F(_pop.size()+2);
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// used to store the number of the first front
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F[1].reserve(_pop.size());
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for (unsigned int p=0; p<_pop.size(); p++)
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{
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for (unsigned int q=0; q<_pop.size(); q++)
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{
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// if q is dominated by p
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if ( comparator(_pop[q].objectiveVector(), _pop[p].objectiveVector()) )
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{
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// add q to the set of solutions dominated by p
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S[p].push_back(q);
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}
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// if p is dominated by q
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else if ( comparator(_pop[p].objectiveVector(), _pop[q].objectiveVector()) )
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{
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// increment the domination counter of p
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n[p]++;
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}
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}
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// if no individual dominates p
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if (n[p] == 0)
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{
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// p belongs to the first front
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_pop[p].fitness(1);
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F[1].push_back(p);
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}
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}
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// front counter
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unsigned int counter=1;
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unsigned int p,q;
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while (! F[counter].empty())
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{
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// used to store the number of the next front
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F[counter+1].reserve(_pop.size());
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for (unsigned int i=0; i<F[counter].size(); i++)
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{
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p = F[counter][i];
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for (unsigned int j=0; j<S[p].size(); j++)
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{
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q = S[p][j];
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n[q]--;
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// if no individual dominates q anymore
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if (n[q] == 0)
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{
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// q belongs to the next front
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_pop[q].fitness(counter+1);
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F[counter+1].push_back(q);
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}
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}
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}
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counter++;
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}
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}
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};
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#endif /*MOEOFASTNONDOMINATEDSORTINGFITNESSASSIGNMENT_H_*/
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@ -0,0 +1,51 @@
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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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// moeoFitnessAssignment.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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||||
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||||
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
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||||
*/
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//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MOEOFITNESSASSIGNMENT_H_
|
||||
#define MOEOFITNESSASSIGNMENT_H_
|
||||
|
||||
#include <eoFunctor.h>
|
||||
#include <eoPop.h>
|
||||
|
||||
/**
|
||||
* Functor that sets the fitness values of a whole population.
|
||||
*/
|
||||
template < class MOEOT >
|
||||
class moeoFitnessAssignment : public eoUF < eoPop < MOEOT > &, void >
|
||||
{
|
||||
public:
|
||||
|
||||
/** The type for objective vector */
|
||||
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
|
||||
|
||||
|
||||
/**
|
||||
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
|
||||
* @param _pop the population
|
||||
* @param _objVec the objective vector
|
||||
*/
|
||||
virtual void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec) = 0;
|
||||
|
||||
|
||||
/**
|
||||
* Updates the fitness values of the whole population _pop by taking the deletion of the individual _moeo into account.
|
||||
* @param _pop the population
|
||||
* @param _moeo the individual
|
||||
*/
|
||||
void updateByDeleting(eoPop < MOEOT > & _pop, MOEOT & _moeo)
|
||||
{
|
||||
updateByDeleting(_pop, _moeo.objectiveVector());
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
#endif /*MOEOFITNESSASSIGNMENT_H_*/
|
||||
|
|
@ -0,0 +1,202 @@
|
|||
// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// moeoIndicatorBasedFitnessAssignment.h
|
||||
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
|
||||
/*
|
||||
This library...
|
||||
|
||||
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
|
||||
*/
|
||||
//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
|
||||
#define MOEOINDICATORBASEDFITNESSASSIGNMENT_H_
|
||||
|
||||
#include <math.h>
|
||||
#include <vector>
|
||||
#include <eoPop.h>
|
||||
#include <fitness/moeoFitnessAssignment.h>
|
||||
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
|
||||
#include <utils/moeoConvertPopToObjectiveVectors.h>
|
||||
|
||||
/**
|
||||
* Fitness assignment sheme based an Indicator proposed in:
|
||||
* E. Zitzler, S. Künzli, "Indicator-Based Selection in Multiobjective Search", Proc. 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp. 832-842, Birmingham, UK (2004).
|
||||
* This strategy is, for instance, used in IBEA.
|
||||
*/
|
||||
template < class MOEOT >
|
||||
class moeoIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
|
||||
{
|
||||
public:
|
||||
|
||||
/** The type of objective vector */
|
||||
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
|
||||
|
||||
|
||||
/**
|
||||
* Ctor.
|
||||
* @param _metric the quality indicator
|
||||
* @param _kappa the scaling factor
|
||||
*/
|
||||
moeoIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa)
|
||||
{}
|
||||
|
||||
|
||||
/**
|
||||
* Sets the fitness values for every solution contained in the population _pop
|
||||
* @param _pop the population
|
||||
*/
|
||||
void operator()(eoPop < MOEOT > & _pop)
|
||||
{
|
||||
// 1 - setting of the bounds
|
||||
setup(_pop);
|
||||
// 2 - computing every indicator values
|
||||
computeValues(_pop);
|
||||
// 3 - setting fitnesses
|
||||
setFitnesses(_pop);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
|
||||
* @param _pop the population
|
||||
* @param _objVec the objective vector
|
||||
*/
|
||||
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
|
||||
{
|
||||
std::vector < double > v;
|
||||
v.resize(_pop.size());
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
v[i] = metric(_objVec, _pop[i].objectiveVector());
|
||||
}
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Updates the fitness values of the whole population _pop by taking the adding of the objective vector _objVec into account
|
||||
* and returns the fitness value of _objVec.
|
||||
* @param _pop the population
|
||||
* @param _objVec the objective vector
|
||||
*/
|
||||
double updateByAdding(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
|
||||
{
|
||||
std::vector < double > v;
|
||||
// update every fitness values to take the new individual into account
|
||||
v.resize(_pop.size());
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
v[i] = metric(_objVec, _pop[i].objectiveVector());
|
||||
}
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
|
||||
}
|
||||
// compute the fitness of the new individual
|
||||
v.clear();
|
||||
v.resize(_pop.size());
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
v[i] = metric(_pop[i].objectiveVector(), _objVec);
|
||||
}
|
||||
double result = 0;
|
||||
for (unsigned int i=0; i<v.size(); i++)
|
||||
{
|
||||
result -= exp(-v[i]/kappa);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
protected:
|
||||
|
||||
/** the quality indicator */
|
||||
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
|
||||
/** the scaling factor */
|
||||
double kappa;
|
||||
/** the computed indicator values */
|
||||
std::vector < std::vector<double> > values;
|
||||
|
||||
|
||||
/**
|
||||
* Sets the bounds for every objective using the min and the max value for every objective vector of _pop
|
||||
* @param _pop the population
|
||||
*/
|
||||
void setup(const eoPop < MOEOT > & _pop)
|
||||
{
|
||||
double min, max;
|
||||
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
min = _pop[0].objectiveVector()[i];
|
||||
max = _pop[0].objectiveVector()[i];
|
||||
for (unsigned int j=1; j<_pop.size(); j++)
|
||||
{
|
||||
min = std::min(min, _pop[j].objectiveVector()[i]);
|
||||
max = std::max(max, _pop[j].objectiveVector()[i]);
|
||||
}
|
||||
// setting of the bounds for the objective i
|
||||
metric.setup(min, max, i);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Compute every indicator value in values (values[i] = I(_v[i], _o))
|
||||
* @param _pop the population
|
||||
*/
|
||||
void computeValues(const eoPop < MOEOT > & _pop)
|
||||
{
|
||||
values.clear();
|
||||
values.resize(_pop.size());
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
values[i].resize(_pop.size());
|
||||
for (unsigned int j=0; j<_pop.size(); j++)
|
||||
{
|
||||
if (i != j)
|
||||
{
|
||||
values[i][j] = metric(_pop[i].objectiveVector(), _pop[j].objectiveVector());
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Sets the fitness value of the whple population
|
||||
* @param _pop the population
|
||||
*/
|
||||
void setFitnesses(eoPop < MOEOT > & _pop)
|
||||
{
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness(computeFitness(i));
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Returns the fitness value of the _idx th individual of the population
|
||||
* @param _idx the index
|
||||
*/
|
||||
double computeFitness(const unsigned int _idx)
|
||||
{
|
||||
double result = 0;
|
||||
for (unsigned int i=0; i<values.size(); i++)
|
||||
{
|
||||
if (i != _idx)
|
||||
{
|
||||
result -= exp(-values[i][_idx]/kappa);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
#endif /*MOEOINDICATORBASEDFITNESSASSIGNMENT_H_*/
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// moeoParetoBasedFitnessAssignment.h
|
||||
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
|
||||
/*
|
||||
This library...
|
||||
|
||||
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
|
||||
*/
|
||||
//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MOEOPARETOBASEDFITNESSASSIGNMENT_H_
|
||||
#define MOEOPARETOBASEDFITNESSASSIGNMENT_H_
|
||||
|
||||
#include <fitness/moeoFitnessAssignment.h>
|
||||
|
||||
/**
|
||||
* moeoParetoBasedFitnessAssignment is a moeoFitnessAssignment for Pareto-based strategies.
|
||||
*/
|
||||
template < class MOEOT >
|
||||
class moeoParetoBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT > {};
|
||||
|
||||
#endif /*MOEOPARETOBASEDFITNESSASSIGNMENT_H_*/
|
||||
|
|
@ -0,0 +1,109 @@
|
|||
// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// moeoReferencePointIndicatorBasedFitnessAssignment.h
|
||||
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
|
||||
/*
|
||||
This library...
|
||||
|
||||
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
|
||||
*/
|
||||
//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_
|
||||
#define MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_
|
||||
|
||||
#include <math.h>
|
||||
#include <eoPop.h>
|
||||
#include <fitness/moeoFitnessAssignment.h>
|
||||
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
|
||||
|
||||
/**
|
||||
* Fitness assignment sheme based a Reference Point and a Quality Indicator.
|
||||
*/
|
||||
template < class MOEOT >
|
||||
class moeoReferencePointIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
|
||||
{
|
||||
public:
|
||||
|
||||
/** The type of objective vector */
|
||||
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
|
||||
|
||||
/**
|
||||
* Ctor
|
||||
* @param _refPoint the reference point
|
||||
* @param _metric the quality indicator
|
||||
*/
|
||||
moeoReferencePointIndicatorBasedFitnessAssignment (ObjectiveVector & _refPoint, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric) :
|
||||
refPoint(_refPoint), metric(_metric)
|
||||
{}
|
||||
|
||||
|
||||
/**
|
||||
* Sets the fitness values for every solution contained in the population _pop
|
||||
* @param _pop the population
|
||||
*/
|
||||
void operator()(eoPop < MOEOT > & _pop)
|
||||
{
|
||||
// 1 - setting of the bounds
|
||||
setup(_pop);
|
||||
// 2 - setting fitnesses
|
||||
setFitnesses(_pop);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
|
||||
* @param _pop the population
|
||||
* @param _objVec the objective vector
|
||||
*/
|
||||
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
|
||||
{
|
||||
// nothing to do ;-)
|
||||
}
|
||||
|
||||
|
||||
protected:
|
||||
|
||||
/** the reference point */
|
||||
ObjectiveVector & refPoint;
|
||||
/** the quality indicator */
|
||||
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
|
||||
|
||||
|
||||
/**
|
||||
* Sets the bounds for every objective using the min and the max value for every objective vector of _pop (and the reference point)
|
||||
* @param _pop the population
|
||||
*/
|
||||
void setup(const eoPop < MOEOT > & _pop)
|
||||
{
|
||||
double min, max;
|
||||
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
min = refPoint[i];
|
||||
max = refPoint[i];
|
||||
for (unsigned int j=0; j<_pop.size(); j++)
|
||||
{
|
||||
min = std::min(min, _pop[j].objectiveVector()[i]);
|
||||
max = std::max(max, _pop[j].objectiveVector()[i]);
|
||||
}
|
||||
// setting of the bounds for the objective i
|
||||
metric.setup(min, max, i);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the fitness of every individual contained in the population _pop
|
||||
* @param _pop the population
|
||||
*/
|
||||
void setFitnesses(eoPop < MOEOT > & _pop)
|
||||
{
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness(- metric(_pop[i].objectiveVector(), refPoint) );
|
||||
}
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
#endif /*MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_*/
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
|
||||
|
||||
//-----------------------------------------------------------------------------
|
||||
// moeoScalarFitnessAssignment.h
|
||||
// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
|
||||
/*
|
||||
This library...
|
||||
|
||||
Contact: paradiseo-help@lists.gforge.inria.fr, http://paradiseo.gforge.inria.fr
|
||||
*/
|
||||
//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MOEOSCALARFITNESSASSIGNMENT_H_
|
||||
#define MOEOSCALARFITNESSASSIGNMENT_H_
|
||||
|
||||
#include <fitness/moeoFitnessAssignment.h>
|
||||
|
||||
/**
|
||||
* moeoScalarFitnessAssignment is a moeoFitnessAssignment for scalar strategies.
|
||||
*/
|
||||
template < class MOEOT >
|
||||
class moeoScalarFitnessAssignment : public moeoFitnessAssignment < MOEOT > {};
|
||||
|
||||
#endif /*MOEOSCALARFITNESSASSIGNMENT_H_*/
|
||||
Loading…
Add table
Add a link
Reference in a new issue