Fuzzy Extension of some classical concepts
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152
moeo/src/diversity/moeoFuzzyCrowdingDiversity.h
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152
moeo/src/diversity/moeoFuzzyCrowdingDiversity.h
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/*
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<moeoFuzzyCrowdingDiversity.h>
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Oumayma BAHRI
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Author:
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Oumayma BAHRI <oumaymabahri.com>
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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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#ifndef MOEOFUZZYCROWDINGDIVERSITY_H_
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#define MOEOFUZZYCROWDINGDIVERSITY_H_
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#include <eoPop.h>
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#include <diversity/moeoDiversityAssignment.h>
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#include <comparator/moeoOneObjectiveComparator.h>
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#include <comparator/moeoFuzzyParetoComparator.h>
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#include <distance/moeoExpectedFuzzyDistance.h>
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/**
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* Diversity assignment sheme based on crowding proposed 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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* Tis strategy assigns diversity values FRONT BY FRONT. It is, for instance, used in NSGA-II.
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*/
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template < class MOEOT >
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class moeoFuzzyCrowdingDiversity : public CrowdingDiversityAssignment < 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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* Updates the diversity 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::cout << "WARNING : updateByDeleting not implemented in FrontByFrontCrowdingDistanceDiversityAssignment" << std::endl;
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}
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private:
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using CrowdingDiversityAssignment < MOEOT >::inf;
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using CrowdingDiversityAssignment < MOEOT >::tiny;
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/**
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* Sets the distance values
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* @param _pop the population
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*/
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void setDistances (eoPop <MOEOT> & _pop)
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{
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unsigned int a,b;
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double min, max, distance;
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unsigned int nObjectives = MOEOT::ObjectiveVector::nObjectives();
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// set diversity to 0 for every individual
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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_pop[i].diversity(0.0);
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}
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// sort the whole pop according to fitness values
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moeoFitnessThenDiversityComparator < MOEOT > fitnessComparator;
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std::vector<MOEOT *> sortedptrpop;
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sortedptrpop.resize(_pop.size());
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// due to intensive sort operations for this diversity assignment,
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// it is more efficient to perform sorts using only pointers to the
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// population members in order to avoid copy of individuals
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for(unsigned int i=0; i< _pop.size(); i++) sortedptrpop[i] = & (_pop[i]);
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//sort the pointers to population members
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moeoFuzzyParetoComparator<MOEOT> comp( fitnessComparator);
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std::sort(sortedptrpop.begin(), sortedptrpop.end(), comp);
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// compute the expected crowding distance values for every individual "front" by "front" (front : from a to b)
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a = 0; // the front starts at a
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while (a < _pop.size())
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{
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b = lastIndex(sortedptrpop,a); // the front ends at b
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//b = lastIndex(_pop,a); // the front ends at b
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// if there is less than 2 individuals in the front...
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if ((b-a) < 2)
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{
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for (unsigned int i=a; i<=b; i++)
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{
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sortedptrpop[i]->diversity(inf());
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//_pop[i].diversity(inf());
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}
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}
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// else...
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else
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{
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// for each objective
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for (unsigned int obj=0; obj<nObjectives; obj++)
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{
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// sort in the descending order using the values of the objective 'obj'
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moeoOneObjectiveComparator < MOEOT > objComp(obj);
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moeoFuzzyParetoComparator<MOEOT> comp( objComp );
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std::sort(sortedptrpop.begin(), sortedptrpop.end(), comp);
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// min & max
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min = (sortedptrpop[b])->objectiveVector()[obj].second;
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max = (sortedptrpop[a])->objectiveVector()[obj].second;
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// avoid extreme case
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if (min == max)
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{
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min -= tiny();
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max += tiny();
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}
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// set the diversity value to infiny for min and max
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sortedptrpop[a]->diversity(inf());
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sortedptrpop[b]->diversity(inf());
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// set the diversity values for the other individuals
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for (unsigned int i=a+1; i<b; i++)
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{
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distance = ( sortedptrpop[i-1]->moeoExpectedFuzzyDistance(objectiveVector()[obj]) - sortedptrpop[i+1]->moeoExpectedFuzzyDistance(objectiveVector()[obj] )) / (max-min);
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sortedptrpop[i]->diversity(sortedptrpop[i]->diversity() + distance);
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}
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}
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}
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// go to the next front
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a = b+1;
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}
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}
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/**
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* Returns the index of the last individual having the same fitness value than _pop[_start]
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* @param _pop the vector of pointers to population individuals
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* @param _start the index to start from
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*/
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unsigned int lastIndex (std::vector<MOEOT *> & _pop, unsigned int _start)
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{
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unsigned int i=_start;
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while ( (i<_pop.size()-1) && (_pop[i]->fitness()==_pop[i+1]->fitness()) )
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{
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i++;
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}
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return i;
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}
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};
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#endif /*MOEOFUZZYCROWDINGDIVERSITY_H_*/
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116
moeo/src/diversity/moeoFuzzyNearestNeighborDiversity.h
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moeo/src/diversity/moeoFuzzyNearestNeighborDiversity.h
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/*
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<moeoFuzzyNearestNeighborDiversity.h>
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Oumayma BAHRI
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Author:
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Oumayma BAHRI <oumaymabahri.com>
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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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#ifndef MOEOFUZZYNEARESTNEIGHBORDIVERSITY_H_
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#define MOEOFUZZYNEARESTNEIGHBORDIVERSITY_H_
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#include <list>
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#include <diversity/moeoDiversityAssignment.h>
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#include <archive/moeoFuzzyArchive.h>
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#include <distance/moeoBertDistance.h>
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/**
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* moeoFuzzyNearestNeighborDiversity is a moeoDiversityAssignment using the fuzzy "Bert" distance between individuals to assign diversity.
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*/
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template < class MOEOT >
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class moeoFuzzyNearestNeighborDiversity : public moeoDiversityAssignment < 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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* Ctor where you can choose your own distance and archive
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* @param _dist the distance used
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* @param _archive the archive used
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* @param _index index for find the k-ieme nearest neighbor, _index correspond to k
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*/
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moeoFuzzyNearestNeighborDiversity(moeoBertDistance <MOEOT, double>& _dist, moeoFuzzyArchive <MOEOT>& _archive, unsigned int _index=1) : distance(_dist), archive(_archive), index(_index)
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{}
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/**
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* Affect the diversity to the pop, diversity corresponding to the k-ieme nearest neighbor.
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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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unsigned int i = _pop.size();
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unsigned int j = archive.size();
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double tmp=0;
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std::vector< std::list<double> > matrice(i+j);
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if (i+j>0)
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{
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for (unsigned k=0; k<i+j-1; k++)
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{
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for (unsigned l=k+1; l<i+j; l++)
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{
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if ( (k<i) && (l<i) )
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tmp=distance(_pop[k], _pop[l]);
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else if ( (k<i) && (l>=i) )
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tmp=distance(_pop[k], archive[l-i]);
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else
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tmp=distance(archive[k-i], archive[l-i]);
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matrice[k].push_back(tmp);
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matrice[l].push_back(tmp);
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}
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}
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}
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for (unsigned int k=0; k<i+j; k++)
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matrice[k].sort();
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for (unsigned int k=0; k<i; k++)
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_pop[k].diversity(-1 * 1/(2+getElement(matrice[k])));
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for (unsigned int k=i; k<i+j; k++)
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archive[k-i].diversity(-1 * 1/(2+getElement(matrice[k])));
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}
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/**
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* Updates the diversity 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::cout << "WARNING : updateByDeleting not implemented in moeoNearestNeighborDiversityAssignment" << std::endl;
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}
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private:
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/** Default distance */
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moeoBertDistance < MOEOT > Distance;
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/** Default archive */
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moeoFuzzyArchive < MOEOT > Archive;
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/** the index corresponding to k for search the k-ieme nearest neighbor */
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unsigned int index;
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/**
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* Return the index-th element of the list _myList
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* @param _myList the list which contains distances
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*/
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double getElement(std::list<double> _myList)
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{
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std::list<double>::iterator it= _myList.begin();
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for (unsigned int i=1; i< std::min((unsigned int)_myList.size(),index); i++)
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it++;
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return *it;
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}
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};
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#endif /*MOEOFUZZYNEARESTNEIGHBORDIVERSITY_H_*/
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