algo VFAS and Weight Strategies added
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trunk/paradiseo-moeo/src/algo/moeoVFAS.h
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trunk/paradiseo-moeo/src/algo/moeoVFAS.h
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/*
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<moeoVFAS.h>
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Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2008
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(C) OPAC Team, LIFL, 2002-2008
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Sébastien Cahon, Jean-Charles Boisson (Jean-Charles.Boisson@lifl.fr)
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François Legillon
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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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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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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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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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#ifndef _moeoVFAS_h
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#define _moeoVFAS_h
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#include <moComparator.h>
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#include <eoEvalFunc.h>
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#include <algo/moeoSolAlgo.h>
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#include <fitness/moeoSingleObjectivization.h>
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#include <fitness/moeoAggregationFitnessAssignment.h>
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#include <explorer/moeoHCMoveLoopExpl.h>
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#include <utils/moeoVariableWeightStrategy.h>
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#include <utils/moeoVariableRefPointStrategy.h>
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#include <utils/moeoDummyWeightStrategy.h>
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#include <utils/moeoDummyRefPointStrategy.h>
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//! Variable fitness assignment search (vfas)
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/*!
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Search using multiple fitness assignment to search solution to a multi objective problem
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*/
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template < class M >
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class moeoVFAS:public moeoPopAlgo < typename M::EOType >
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{
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public:
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typedef typename M::EOType MOEOT;
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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typedef typename MOEOT::Fitness Fitness;
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/*!
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constructor using a moAlgo and a vector of weight
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take a base vector of weight, and modify it to relaunch the algo with a diferent fitness
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use a selectOne to determine which moeot should be the base for the algo launch
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use a eoPop to keep result from each iteration
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@param _algorithm The solution based heuristic to use. It should at least use the fitness value at some point.
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@param _continue The stopping criterion.
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@param _select a selector to choose on which moeot we use the algorithm
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@param _weights a vector containing the base weights, which will be changed at each iteration.
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@param _eval The evaluation function.
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@param _wstrat the strategy to change weights (should be constructed with the same weights as the fitness)
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*/
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moeoVFAS (moeoSolAlgo<MOEOT> & _algorithm, eoContinue <MOEOT> & _continue,moeoSelectOne<MOEOT> &_select,
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std::vector<double> &_weights, eoEvalFunc< MOEOT > &_eval , moeoVariableWeightStrategy<MOEOT> &_wstrat ):
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algo(_algorithm),cont(_continue), select(_select), weights(_weights),eval(_eval),wstrat(_wstrat), refPoint(defaultRefPoint),rstrat(defaultRstrat)
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{}
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/*!
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constructor using a moAlgo an ObjectiveVector and a vector of weight
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take a base vector of weight, and modify it to relaunch the algo with a diferent fitness
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use a selectOne to determine which moeot should be the base for the algo launch
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use a eoPop to keep result from each iteration
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@param _algorithm The solution based heuristic to use. It should at least use the fitness value at some point.
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@param _continue The stopping criterion.
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@param _select a selector to choose on which moeot we use the algorithm
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@param _weights a vector containing the base weights, which will be changed at each iteration.
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@param _refPoint a reference point changed at each iteration
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@param _eval The evaluation function.
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@param _wstrat the strategy to change weights (should be constructed with the same weights as the fitness)
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@param _rstrat the strategy to change the reference point
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*/
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moeoVFAS (moeoSolAlgo<MOEOT> & _algorithm, eoContinue <MOEOT> & _continue,moeoSelectOne<MOEOT> &_select,
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std::vector<double> &_weights, ObjectiveVector &_refPoint, eoEvalFunc< MOEOT > &_eval , moeoVariableWeightStrategy<MOEOT> &_wstrat , moeoVariableRefPointStrategy<MOEOT>& _rstrat):
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algo(_algorithm),cont(_continue), select(_select), weights(_weights),eval(_eval),wstrat(_wstrat), rstrat(_rstrat),refPoint(_refPoint)
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{}
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/**
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constructor without the weights
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@param _algorithm The solution based heuristic to use. It should at least use the fitness value at some point.
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@param _continue The stopping criterion.
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@param _select a selector to choose on which moeot we use the algorithm
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@param _eval The evaluation function.
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**/
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moeoVFAS (moeoSolAlgo<MOEOT> & _algorithm, eoContinue <MOEOT> & _continue,moeoSelectOne<MOEOT> &_select,
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eoEvalFunc< MOEOT > &_eval, moeoVariableWeightStrategy<MOEOT> &_wstrat):
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algo(_algorithm),cont(_continue), select(_select), weights(defaultWeights), eval(_eval), wstrat(defaultWstrat), refPoint(defaultRefPoint), rstrat(defaultRstrat)
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{
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weights.resize(MOEOT::ObjectiveVector::nObjectives(),1.0/MOEOT::ObjectiveVector::nObjectives());
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}
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/**
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* launch the algorithm
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* @param _pop the initial population on which algo will be launched
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**/
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virtual void operator()(eoPop<MOEOT> &_pop){
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uniform_generator<double> rngGen(0.0,1.0);
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for (unsigned int i=0;i<_pop.size();i++){
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if (_pop[i].invalidObjectiveVector())
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eval(_pop[i]);
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}
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moeoObjectiveVectorNormalizer<MOEOT> norm(_pop);
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moeoAggregationFitnessAssignment<MOEOT> fitness(weights,eval);
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bool res=false;
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int iteration=0;
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fitness(_pop);
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MOEOT moeot(select(_pop));
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wstrat(weights,moeot);
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rstrat(refPoint,moeot);
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do {
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norm.update_by_pop(_pop);
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fitness(_pop);
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moeot=(select(_pop));
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res=algo(moeot)||res;
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_pop.push_back(moeot);
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std::cout<<moeot.objectiveVector()<<std::endl;
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wstrat(weights,moeot);
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rstrat(refPoint,moeot);
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}while(cont(_pop));
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}
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private:
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moeoSolAlgo<MOEOT> &algo;
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eoContinue<MOEOT> &cont;
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moeoSelectOne<MOEOT> &select;
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std::vector<double> &weights;
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std::vector<double> defaultWeights;
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ObjectiveVector &refPoint;
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ObjectiveVector defaultRefPoint;
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eoEvalFunc<MOEOT> &eval;
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moeoVariableWeightStrategy<MOEOT> &wstrat;
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moeoVariableRefPointStrategy<MOEOT> &rstrat;
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moeoDummyRefPointStrategy<MOEOT> defaultRstrat;
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moeoDummyWeightStrategy<MOEOT> defaultWstrat;
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};
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#endif
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