documentation modifications
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@178 331e1502-861f-0410-8da2-ba01fb791d7f
This commit is contained in:
liefooga
parent
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commit
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16 changed files with 76 additions and 73 deletions
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@ -14,19 +14,20 @@
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#define MOEOARCHIVE_H_
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#include <eoPop.h>
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#include <moeoObjectiveVectorComparator.h>
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/**
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* An archive is a secondary population that stores non-dominated solutions
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* An archive is a secondary population that stores non-dominated solutions.
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*/
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template < class MOEOT >
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class moeoArchive : public eoPop < MOEOT >
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{
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public:
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using std::vector< MOEOT > :: size;
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using std::vector< MOEOT > :: operator[];
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using std::vector< MOEOT > :: back;
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using std::vector< MOEOT > :: pop_back;
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using std::vector < MOEOT > :: size;
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using std::vector < MOEOT > :: operator[];
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using std::vector < MOEOT > :: back;
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using std::vector < MOEOT > :: pop_back;
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/**
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@ -57,9 +58,9 @@ public:
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*/
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bool dominates (const ObjectiveVector & _objectiveVector) const
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{
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for (unsigned i = 0; i < size(); i++)
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for (unsigned i = 0; i<size(); i++)
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{
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if ( comparator(operator[](i).fitness(), _objectiveVector)==1 )
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if ( comparator(operator[](i).fitness(), _objectiveVector) == 1 )
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{
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return true;
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}
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@ -74,9 +75,9 @@ public:
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*/
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bool contains (const ObjectiveVector & _objectiveVector) const
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{
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for (unsigned i = 0; i < size; i++)
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for (unsigned i = 0; i<size(); i++)
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{
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if (operator[](i).fitness () == _objectiveVector)
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if (operator[](i).fitness() == _objectiveVector)
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{
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return true;
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}
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@ -92,16 +93,14 @@ public:
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void update (const MOEOT & _moeo)
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{
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// first step: removing the dominated solutions from the archive
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for (unsigned j = 0; j < size();)
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for (unsigned j=0; j<size();)
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{
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// if _moeo.fitness() dominates operator[](j).fitness()
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//if ( comparator(_moeo.fitness(), operator[](j).fitness())==1 )
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// if _moeo dominates the jth solution contanied in the archive
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if ( comparator(_moeo.objectiveVector(), operator[](j).objectiveVector())==1 )
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{
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operator[](j) = back();
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pop_back();
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}
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//else if (_moeo.fitness() == operator[](j).fitness())
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else if (_moeo.objectiveVector() == operator[](j).objectiveVector())
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{
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operator[](j) = back();
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@ -116,9 +115,8 @@ public:
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bool dom = false;
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for (unsigned j=0; j<size(); j++)
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{
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// if operator[](j).fitness() dominates _moeo.fitness()
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//if ( comparator(operator[](j).fitness(), _moeo.fitness())==1 )
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if ( comparator(operator[](j).objectiveVector(), _moeo.objectiveVector())==1 )
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// if the jth solution contanied in the archive dominates _moeo
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if ( comparator(operator[](j).objectiveVector(), _moeo.objectiveVector()) == 1 )
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{
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dom = true;
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break;
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@ -18,7 +18,7 @@
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#include <moeoArchive.h>
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/**
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* This class allows to update the archive at each generation with newly found non-dominated solutions
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* This class allows to update the archive at each generation with newly found non-dominated solutions.
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*/
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template < class EOT >
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class moeoArchiveUpdater : public eoUpdater
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@ -19,27 +19,30 @@
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/**
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* This class allows to embed a set of local searches that are sequentially applied,
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* and so working and updating the same archive of non-dominated solutions
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* and so working and updating the same archive of non-dominated solutions.
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*/
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template < class MOEOT >
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class moeoCombinedLS : public moeoLS < MOEOT > {
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class moeoCombinedLS : public moeoLS < MOEOT >
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{
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public:
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/**
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* Ctor
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* @param _eval the full evaluator of a solution
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* @param _first_ls the first multi-objective local search to add
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* @param _first_mols the first multi-objective local search to add
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*/
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moeoCombinedLS(moeoEvalFunc < MOEOT > & _eval, moeoLS < MOEOT > & _first_ls) : eval (_eval) {
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combinedLS.push_back (& _first_ls);
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moeoCombinedLS(moeoEvalFunc < MOEOT > & _eval, moeoLS < MOEOT > & _first_mols) : eval (_eval)
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{
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combinedLS.push_back (& _first_mols);
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}
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/**
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* Adds a new local search to combine
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* @param _ls the multi-objective local search to add
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* @param _mols the multi-objective local search to add
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*/
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void add(moeoLS < MOEOT > & _ls) {
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combinedMOLS.push_back(& _ls);
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void add(moeoLS < MOEOT > & _mols)
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{
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combinedMOLS.push_back(& _mols);
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}
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/**
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@ -48,10 +51,11 @@ public:
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* @param _eo the solution
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* @param _arch the archive of non-dominated solutions
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*/
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void operator () (const MOEOT & _eo, moeoArchive < MOEOT > & _arch) {
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eval(const_cast < MOEOT & > (_eo));
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void operator () (const MOEOT & _moeo, moeoArchive < MOEOT > & _arch)
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{
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eval(const_cast < MOEOT & > (_moeo));
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for (unsigned i=0; i<combinedLS.size(); i++)
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combinedLS[i] -> operator()(_eo, _arch);
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combinedLS[i] -> operator()(_moeo, _arch);
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}
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@ -10,14 +10,13 @@
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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOPOPSORTER_H_
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#define MOEOPOPSORTER_H_
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#ifndef MOEOCOMPARATOR_H_
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#define MOEOCOMPARATOR_H_
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#include <eoFunctor.h>
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#include <eoPop.h>
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/**
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* Functor allowing to compare two solutions
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* Functor allowing to compare two solutions.
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*/
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template < class MOEOT >
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class moeoComparator : public eoBF < const MOEOT &, const MOEOT &, const bool >
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@ -25,7 +24,7 @@ class moeoComparator : public eoBF < const MOEOT &, const MOEOT &, const bool >
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/**
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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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* Functor allowing to compare two solutions according to their first objective value, then their second, and so on.
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*/
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template < class MOEOT >
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class moeoObjectiveComparator : public moeoComparator < MOEOT >
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@ -44,7 +43,7 @@ public:
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/**
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* Functor allowing to compare two solutions according to their fitness values
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* Functor allowing to compare two solutions according to their fitness values.
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*/
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template < class MOEOT >
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class moeoFitnessComparator : public moeoComparator < MOEOT >
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@ -63,7 +62,7 @@ public:
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/**
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* Functor allowing to compare two solutions according to their diversity values
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* Functor allowing to compare two solutions according to their diversity values.
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*/
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template < class MOEOT >
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class moeoDiversityComparator : public moeoComparator < MOEOT >
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@ -82,7 +81,7 @@ public:
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/**
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* Functor allowing to compare two solutions according to their fitness values, then according to their diversity values
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* Functor allowing to compare two solutions according to their fitness values, then according to their diversity values.
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*/
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template < class MOEOT >
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class moeoFitnessThenDiversityComparator : public moeoComparator < MOEOT >
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@ -108,7 +107,7 @@ public:
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/**
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* Functor allowing to compare two solutions according to their diversity values, then according to their fitness values
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* Functor allowing to compare two solutions according to their diversity values, then according to their fitness values.
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*/
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template < class MOEOT >
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class moeoDiversityThenFitnessComparator : public moeoComparator < MOEOT >
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@ -134,7 +133,7 @@ public:
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/**
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* Functor allowing to compare two solutions according to Pareto dominance relation => USEFULL ???
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* Functor allowing to compare two solutions according to Pareto dominance relation. => USEFULL ???
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*
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template < class MOEOT >
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class moeoParetoDominanceComparator : public moeoComparator < MOEOT >
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@ -179,4 +178,4 @@ public:
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};
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*/
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#endif /*MOEOPOPSORTER_H_*/
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#endif /*MOEOCOMPARATOR_H_*/
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@ -17,7 +17,7 @@
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#include <eoPop.h>
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/**
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* Functor that sets the diversity values of a whole population
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* Functor that sets the diversity values of a whole population.
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*/
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template < class MOEOT >
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class moeoDiversityAssignment : public eoUF < eoPop < MOEOT > &, void >
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@ -16,7 +16,7 @@
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#include <eoAlgo.h>
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/**
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* Abstract class for multi-objective evolutionary algorithms
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* Abstract class for multi-objective evolutionary algorithms.
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*/
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template < class MOEOT >
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class moeoEA : public eoAlgo < MOEOT > {};
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@ -14,15 +14,15 @@
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#define MOEOFASTNONDOMINATEDSORTINGFITNESSASSIGNMENT_H_
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#include <eoPop.h>
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#include <moeoFitnessAssignment.h>
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#include <moeoComparator.h>
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#include <moeoFitnessAssignment.h>
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#include <moeoObjectiveVectorComparator.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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* 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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#include <eoPop.h>
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/**
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* Functor that sets the fitness values of a whole population
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* Functor that sets the fitness values of a whole population.
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*/
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template < class MOEOT >
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class moeoFitnessAssignment : public eoUF < eoPop < MOEOT > &, void >
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@ -25,7 +25,7 @@ class moeoFitnessAssignment : public eoUF < eoPop < MOEOT > &, void >
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/**
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* moeoScalarFitnessAssignment is a moeoFitnessAssignment for scalar strategies
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* moeoScalarFitnessAssignment is a moeoFitnessAssignment for scalar strategies.
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*/
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template < class MOEOT >
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class moeoScalarFitnessAssignment : public moeoFitnessAssignment < MOEOT >
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@ -33,7 +33,7 @@ class moeoScalarFitnessAssignment : public moeoFitnessAssignment < MOEOT >
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/**
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* moeoCriterionBasedFitnessAssignment is a moeoFitnessAssignment for criterion-based strategies
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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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@ -41,7 +41,7 @@ class moeoCriterionBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT
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/**
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* moeoParetoBasedFitnessAssignment is a moeoFitnessAssignment for Pareto-based strategies
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* moeoParetoBasedFitnessAssignment is a moeoFitnessAssignment for Pareto-based strategies.
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*/
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template < class MOEOT >
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class moeoParetoBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
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#include <moeoGenerationalReplacement.h>
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/**
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* Generational replacement: only the new individuals are preserved
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* Generational replacement: only the new individuals are preserved.
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*/
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template < class MOEOT >
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class moeoGenerationalReplacement : public moeoReplacement < MOEOT >, public eoGenerationalReplacement < MOEOT > {};
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* Ctor
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* @param _term stopping criteria
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* @param _select selector
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* @param _ls a multi-objective local search
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* @param _mols a multi-objective local search
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* @param _arch the archive
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*/
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eoHybridLS (eoContinue < MOEOT > & _term, eoSelect < MOEOT > & _select, moeoLS < MOEOT > & _ls, moeoArchive < MOEOT > & _arch) : term(_term), select(_select), ls(_ls), arch(_arch)
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eoHybridLS (eoContinue < MOEOT > & _term, eoSelect < MOEOT > & _select, moeoLS < MOEOT > & _mols, moeoArchive < MOEOT > & _arch) : term(_term), select(_select), mols(_mols), arch(_arch)
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{}
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/**
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eoPop < MOEOT > selectedSolutions;
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select(arch, selectedSolutions);
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// apply the local search to every selected solution
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for (unsigned i=0; i<selectedSolutions.size(); i ++)
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for (unsigned i=0; i<selectedSolutions.size(); i++)
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{
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ls(selectedSolutions[i], arch);
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mols(selectedSolutions[i], arch);
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}
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}
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}
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/** selector */
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eoSelect < MOEOT > & select;
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/** multi-objective local search */
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moeoLS < MOEOT > & ls;
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moeoLS < MOEOT > & mols;
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/** archive */
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moeoArchive < MOEOT > & arch;
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/**
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* This functor class allows to compare 2 objective vectors according to Pareto dominance
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* This functor class allows to compare 2 objective vectors according to Pareto dominance.
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*/
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template < class ObjectiveVector >
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class moeoParetoObjectiveVectorComparator : public moeoObjectiveVectorComparator < ObjectiveVector >
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#include <stdexcept>
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/**
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* A traits class for moeoObjectiveVector to specify the number of objectives and which ones have to be minimized or maximized
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* A traits class for moeoObjectiveVector to specify the number of objectives and which ones have to be minimized or maximized.
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*/
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class moeoObjectiveVectorTraits
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{
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#include <eoRandomSelect.h>
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/**
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* Selection strategy that selects only one element randomly from a whole population
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* Selection strategy that selects only one element randomly from a whole population.
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*/
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template < class MOEOT >
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class moeoRandomSelectOne : public moeoSelectOne < MOEOT >, public eoRandomSelect < MOEOT > {};
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#include <eoReplacement.h>
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/**
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* Replacement strategy for multi-objective optimization
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* Replacement strategy for multi-objective optimization.
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*/
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template < class MOEOT >
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class moeoReplacement : public eoReplacement < MOEOT > {};
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#include <eoSelectOne.h>
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/**
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* Selection strategy for multi-objective optimization that selects only one element from a whole population
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* Selection strategy for multi-objective optimization that selects only one element from a whole population.
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*/
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template < class MOEOT >
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class moeoSelectOne : public eoSelectOne < MOEOT > {};
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/**
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* Elitist selection process that consists in choosing individuals in the archive as well as in the current population.
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*/
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template<class EOT>
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class moeoSelectOneFromPopAndArch : public moeoSelectOne<EOT>
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template < class MOEOT >
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class moeoSelectOneFromPopAndArch : public moeoSelectOne < MOEOT >
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{
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public:
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@ -34,24 +34,25 @@ public:
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* @param _arch the archive
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* @param _ratioFromPop the ratio of selected individuals from the population
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*/
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moeoSelectOneFromPopAndArch (moeoSelectOne<EOT> & _popSelectOne, moeoSelectOne<EOT> _archSelectOne, moeoArchive <EOT> & _arch, double _ratioFromPop=0.5)
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moeoSelectOneFromPopAndArch (moeoSelectOne < MOEOT > & _popSelectOne, moeoSelectOne < MOEOT > _archSelectOne, moeoArchive < MOEOT > & _arch, double _ratioFromPop=0.5)
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: popSelectOne(_popSelectOne), archSelectOne(_archSelectOne), arch(_arch), ratioFromPop(_ratioFromPop)
|
||||
{}
|
||||
|
||||
/**
|
||||
* Ctor - the archive's selection operator is a random selector
|
||||
* Defaulr ctor - the archive's selection operator is a random selector
|
||||
* @param _popSelectOne the population's selection operator
|
||||
* @param _arch the archive
|
||||
* @param _ratioFromPop the ratio of selected individuals from the population
|
||||
*/
|
||||
moeoSelectOneFromPopAndArch (moeoSelectOne<EOT> & _popSelectOne, moeoArchive <EOT> & _arch, double _ratioFromPop=0.5)
|
||||
moeoSelectOneFromPopAndArch (moeoSelectOne < MOEOT > & _popSelectOne, moeoArchive < MOEOT > & _arch, double _ratioFromPop=0.5)
|
||||
: popSelectOne(_popSelectOne), archSelectOne(randomSelectOne), arch(_arch), ratioFromPop(_ratioFromPop)
|
||||
{}
|
||||
|
||||
/**
|
||||
* The selection process
|
||||
*/
|
||||
virtual const EOT & operator () (const eoPop<EOT> & pop) {
|
||||
virtual const MOEOT & operator () (const eoPop < MOEOT > & pop)
|
||||
{
|
||||
if (arch.size() > 0)
|
||||
if (rng.flip(ratioFromPop))
|
||||
return popSelectOne(pop);
|
||||
|
|
@ -64,7 +65,8 @@ public:
|
|||
/**
|
||||
* Setups some population stats
|
||||
*/
|
||||
virtual void setup (const eoPop<EOT> & _pop) {
|
||||
virtual void setup (const eoPop < MOEOT > & _pop)
|
||||
{
|
||||
popSelectOne.setup(_pop);
|
||||
}
|
||||
|
||||
|
|
@ -72,15 +74,15 @@ public:
|
|||
private:
|
||||
|
||||
/** The population's selection operator */
|
||||
moeoSelectOne<EOT> & popSelectOne;
|
||||
moeoSelectOne < MOEOT > & popSelectOne;
|
||||
/** The archive's selection operator */
|
||||
moeoSelectOne<EOT> & archSelectOne;
|
||||
moeoSelectOne < MOEOT > & archSelectOne;
|
||||
/** The archive */
|
||||
moeoArchive <EOT> & arch;
|
||||
moeoArchive < MOEOT > & arch;
|
||||
/** The ratio of selected individuals from the population*/
|
||||
double ratioFromPop;
|
||||
/** A random selection operator */
|
||||
moeoRandomSelectOne<EOT> randomSelectOne;
|
||||
/** A random selection operator (used as default for archSelectOne) */
|
||||
moeoRandomSelectOne< MOEOT > randomSelectOne;
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue