253 lines
8.6 KiB
C++
253 lines
8.6 KiB
C++
/*
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* <moeoExpBinaryIndicatorBasedFitnessAssignment.h>
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* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2014
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* (C) OPAC Team, LIFL, 2002-2014
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*
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* Arnaud Liefooghe
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*
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* Oct 17, 2014 - Arnaud Liefooghe
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* Modifications on the handling of the internal data structure (values)
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* in order to avoid (too much/bad) memory consumptions, in particular
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* when a very large population size is used
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*
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* This software is governed by the CeCILL license under French law and
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* abiding by the rules of distribution of free software. You can use,
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* modify and/ or redistribute the software under the terms of the CeCILL
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* license as circulated by CEA, CNRS and INRIA at the following URL
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* "http://www.cecill.info".
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*
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* As a counterpart to the access to the source code and rights to copy,
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* modify and redistribute granted by the license, users are provided only
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* with a limited warranty and the software's author, the holder of the
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* economic rights, and the successive licensors have only limited liability.
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*
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* In this respect, the user's attention is drawn to the risks associated
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* with loading, using, modifying and/or developing or reproducing the
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* software by the user in light of its specific status of free software,
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* that may mean that it is complicated to manipulate, and that also
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* therefore means that it is reserved for developers and experienced
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* professionals having in-depth computer knowledge. Users are therefore
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* encouraged to load and test the software's suitability as regards their
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* requirements in conditions enabling the security of their systems and/or
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* data to be ensured and, more generally, to use and operate it in the
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* same conditions as regards security.
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* The fact that you are presently reading this means that you have had
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* knowledge of the CeCILL license and that you accept its terms.
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*
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* ParadisEO WebSite : http://paradiseo.gforge.inria.fr
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* Contact: paradiseo-help@lists.gforge.inria.fr
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*
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
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#define MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_
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#include <math.h>
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#include <vector>
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#include <eoPop.h>
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#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
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#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
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#include <utils/moeoConvertPopToObjectiveVectors.h>
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/**
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* Fitness assignment sheme based on an indicator proposed in:
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* E. Zitzler, S. Künzli, "Indicator-Based Selection in Multiobjective Search", Proc. 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp. 832-842, Birmingham, UK (2004).
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* This strategy is, for instance, used in IBEA.
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*/
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template < class MOEOT >
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class moeoExpBinaryIndicatorBasedFitnessAssignment : public moeoBinaryIndicatorBasedFitnessAssignment < MOEOT >
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{
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public:
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/** The type of objective vector */
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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typedef typename ObjectiveVector::Type Type;
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typedef typename MOEOT::Fitness Fitness;
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/**
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* Ctor.
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* @param _metric the quality indicator
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* @param _kappa the scaling factor
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*/
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moeoExpBinaryIndicatorBasedFitnessAssignment(moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric, const double _kappa = 0.05) : metric(_metric), kappa(_kappa), values(0) {}
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/**
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* Dtor.
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*/
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~moeoExpBinaryIndicatorBasedFitnessAssignment()
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{
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// clear "values"
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for (unsigned int i=0; i<values.size(); i++) values[i].clear();
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values.clear();
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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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// 1 - setting of the bounds
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setup(_pop);
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// 2 - computing every indicator values
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computeValues(_pop);
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// 3 - setting fitnesses
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setFitnesses(_pop);
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}
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/**
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* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
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* @param _pop the population
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* @param _objVec the objective vector
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*/
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void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
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{
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std::vector < double > v;
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v.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_objVec, _pop[i].objectiveVector());
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}
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness( _pop[i].fitness() + exp(-v[i]/kappa) );
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}
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v.clear();
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}
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/**
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* Updates the fitness values of the whole population _pop by taking the adding of the objective vector _objVec into account
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* and returns the fitness value of _objVec.
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* @param _pop the population
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* @param _objVec the objective vector
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*/
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double updateByAdding(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
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{
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std::vector < double > v;
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// update every fitness values to take the new individual into account
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v.resize(_pop.size());
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_objVec, _pop[i].objectiveVector());
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}
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness( _pop[i].fitness() - exp(-v[i]/kappa) );
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}
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// compute the fitness of the new individual
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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v[i] = metric(_pop[i].objectiveVector(), _objVec);
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}
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double result = 0;
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for (unsigned int i=0; i<v.size(); i++)
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{
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result -= exp(-v[i]/kappa);
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}
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v.clear();
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return result;
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}
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protected:
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/** the quality indicator */
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moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
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/** the scaling factor */
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double kappa;
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/** the computed indicator values */
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std::vector < std::vector<Type> > values;
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/**
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* Sets the bounds for every objective using the min and the max value for every objective vector of _pop
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* @param _pop the population
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*/
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void setup(const eoPop < MOEOT > & _pop)
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{
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typename MOEOT::ObjectiveVector::Type min, max;
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for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
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{
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min = _pop[0].objectiveVector()[i];
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max = _pop[0].objectiveVector()[i];
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for (unsigned int j=1; j<_pop.size(); j++)
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{
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min = std::min(min, _pop[j].objectiveVector()[i]);
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max = std::max(max, _pop[j].objectiveVector()[i]);
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}
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// setting of the bounds for the objective i
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metric.setup(min, max, i);
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}
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}
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/**
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* Compute every indicator value in values (values[i] = I(_v[i], _o))
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* @param _pop the population
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*/
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virtual void computeValues(const eoPop < MOEOT > & _pop)
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{
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// initialize the values structure (if it is used for the first time with such a population size)
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if (values.size() < _pop.size())
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{
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values.resize(_pop.size());
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for (unsigned int i=0; i<values.size(); i++)
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{
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values[i].resize(_pop.size());
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}
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}
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// go
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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// the metric may not be symetric, thus neither is the matrix
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for (unsigned int j=0; j<_pop.size(); j++)
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{
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if (i != j)
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{
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values[i][j] = Type( metric(_pop[i].objectiveVector(), _pop[j].objectiveVector()) );
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}
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}
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}
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}
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/**
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* Sets the fitness value of the whole population
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* @param _pop the population
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*/
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virtual void setFitnesses(eoPop < MOEOT > & _pop)
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{
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].fitness(computeFitness(_pop, i));
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}
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}
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/**
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* Returns the fitness value of the _idx th individual of the population
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* @param _pop the population (only useful for its size here)
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* @param _idx the index
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*/
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virtual Fitness computeFitness(const eoPop < MOEOT > & _pop, const unsigned int _idx)
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{
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Fitness result(0.0);
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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if (i != _idx)
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{
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result -= exp(-values[i][_idx]/kappa);
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}
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}
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return result;
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}
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};
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#endif /*MOEOEXPBINARYINDICATORBASEDFITNESSASSIGNMENT_H_*/
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