Ajout du Metropolis-Hasting LS, du samplinf MH fitness cloud
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110
trunk/paradiseo-mo/src/algo/moMetropolisHasting.h
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110
trunk/paradiseo-mo/src/algo/moMetropolisHasting.h
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/*
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<moMetropolisHasting.h>
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Copyright (C) DOLPHIN Project-Team, INRIA Lille - Nord Europe, 2006-2010
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Sebastien Verel, Arnaud Liefooghe, Jeremie Humeau
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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 ue,
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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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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 _moMetropolisHasting_h
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#define _moMetropolisHasting_h
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#include <algo/moLocalSearch.h>
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#include <explorer/moMetropolisHastingExplorer.h>
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#include <continuator/moTrueContinuator.h>
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#include <eval/moEval.h>
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#include <eoEvalFunc.h>
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/********************************************************
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* Metropolis-Hasting local search
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* Only the symetric case is considered when Q(x,y) = Q(y,x)
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* Fitness must be > 0
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*
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* At each iteration,
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* one of the random solution in the neighborhood is selected
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* if the selected neighbor have higher or equal fitness than the current solution
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* then the solution is replaced by the selected neighbor
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* if a random number from [0,1] is lower than fitness(neighbor) / fitness(solution)
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* then the solution is replaced by the selected neighbor
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* the algorithm stops when the number of iterations is too large
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********************************************************/
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template<class Neighbor>
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class moMetropolisHasting: public moLocalSearch<Neighbor>
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{
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public:
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typedef typename Neighbor::EOT EOT;
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typedef moNeighborhood<Neighbor> Neighborhood ;
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/**
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* Simple constructor of the Metropolis-Hasting
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* @param _neighborhood the neighborhood
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* @param _fullEval the full evaluation function
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* @param _eval neighbor's evaluation function
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* @param _nbStep maximum step to do
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*/
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moMetropolisHasting(Neighborhood& _neighborhood, eoEvalFunc<EOT>& _fullEval, moEval<Neighbor>& _eval, unsigned int _nbStep):
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moLocalSearch<Neighbor>(explorer, trueCont, _fullEval),
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explorer(_neighborhood, _eval, defaultNeighborComp, defaultSolNeighborComp, _nbStep)
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{}
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/**
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* Simple constructor of the Metropolis-Hasting
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* @param _neighborhood the neighborhood
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* @param _fullEval the full evaluation function
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* @param _eval neighbor's evaluation function
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* @param _nbStep maximum step to do
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* @param _cont an external continuator
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*/
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moMetropolisHasting(Neighborhood& _neighborhood, eoEvalFunc<EOT>& _fullEval, moEval<Neighbor>& _eval, unsigned int _nbStep, moContinuator<Neighbor>& _cont):
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moLocalSearch<Neighbor>(explorer, _cont, _fullEval),
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explorer(_neighborhood, _eval, defaultNeighborComp, defaultSolNeighborComp, _nbStep)
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{}
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/**
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* Simple constructor of the Metropolis-Hasting
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* @param _neighborhood the neighborhood
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* @param _fullEval the full evaluation function
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* @param _eval neighbor's evaluation function
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* @param _nbStep maximum step to do
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* @param _cont an external continuator
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* @param _compN a neighbor vs neighbor comparator
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* @param _compSN a solution vs neighbor comparator
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*/
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moMetropolisHasting(Neighborhood& _neighborhood, eoEvalFunc<EOT>& _fullEval, moEval<Neighbor>& _eval, unsigned int _nbStep, moContinuator<Neighbor>& _cont, moNeighborComparator<Neighbor>& _compN, moSolNeighborComparator<Neighbor>& _compSN):
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moLocalSearch<Neighbor>(explorer, _cont, _fullEval),
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explorer(_neighborhood, _eval, _compN, _compSN, _nbStep)
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{}
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private:
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// always true continuator
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moTrueContinuator<Neighbor> trueCont;
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// compare the fitness values of neighbors: true is strictly greater
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moNeighborComparator<Neighbor> defaultNeighborComp;
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// compare the fitness values of the solution and the neighbor: true if strictly greater
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moSolNeighborComparator<Neighbor> defaultSolNeighborComp;
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// the explorer of the HC with neutral move (equals fitness move)
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moMetropolisHastingExplorer<Neighbor> explorer;
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};
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#endif
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@ -57,6 +57,7 @@ public:
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* @param _neighborhood the neighborhood
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* @param _fullEval the full evaluation function
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* @param _eval neighbor's evaluation function
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* @param _nbStep maximum step to do
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*/
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moNeutralHC(Neighborhood& _neighborhood, eoEvalFunc<EOT>& _fullEval, moEval<Neighbor>& _eval, unsigned int _nbStep):
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moLocalSearch<Neighbor>(explorer, trueCont, _fullEval),
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@ -68,6 +69,7 @@ public:
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* @param _neighborhood the neighborhood
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* @param _fullEval the full evaluation function
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* @param _eval neighbor's evaluation function
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* @param _nbStep maximum step to do
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* @param _cont an external continuator
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*/
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moNeutralHC(Neighborhood& _neighborhood, eoEvalFunc<EOT>& _fullEval, moEval<Neighbor>& _eval, unsigned int _nbStep, moContinuator<Neighbor>& _cont):
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@ -80,6 +82,7 @@ public:
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* @param _neighborhood the neighborhood
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* @param _fullEval the full evaluation function
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* @param _eval neighbor's evaluation function
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* @param _nbStep maximum step to do
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* @param _cont an external continuator
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* @param _compN a neighbor vs neighbor comparator
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* @param _compSN a solution vs neighbor comparator
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@ -37,6 +37,7 @@
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#include <algo/moLocalSearch.h>
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#include <algo/moRandomSearch.h>
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#include <algo/moMetropolisHasting.h>
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#include <algo/moSA.h>
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#include <algo/moSimpleHC.h>
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#include <algo/moFirstImprHC.h>
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@ -143,7 +144,8 @@
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#include <sampling/moHillClimberSampling.h>
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#include <sampling/moFDCsampling.h>
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#include <sampling/moNeutralDegreeSampling.h>
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#include <sampling/moFitnessCouldSampling.h>
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#include <sampling/moFitnessCloudSampling.h>
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#include <sampling/moMHFitnessCloudSampling.h>
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#include <problems/bitString/moBitNeighbor.h>
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#include <problems/eval/moOneMaxIncrEval.h>
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100
trunk/paradiseo-mo/src/sampling/moMHFitnessCloudSampling.h
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100
trunk/paradiseo-mo/src/sampling/moMHFitnessCloudSampling.h
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@ -0,0 +1,100 @@
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/*
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<moMHFitnessCloudSampling.h>
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Copyright (C) DOLPHIN Project-Team, INRIA Lille - Nord Europe, 2006-2010
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Sebastien Verel, Arnaud Liefooghe, Jeremie Humeau
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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 moMHFitnessCloudSampling_h
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#define moMHFitnessCloudSampling_h
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#include <eoInit.h>
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#include <neighborhood/moNeighborhood.h>
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#include <eval/moEval.h>
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#include <eoEvalFunc.h>
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#include <algo/moMetropolisHasting.h>
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#include <continuator/moFitnessStat.h>
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#include <continuator/moNeighborFitnessStat.h>
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#include <sampling/moSampling.h>
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/**
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* To compute an estimation of the fitness cloud,
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* i.e. the scatter plot of solution fitness versus neighbor fitness:
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* Here solution are sampled with Metropolis-Hasting method
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*
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* Sample the fitness of solutions from Metropolis-Hasting sampling
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* and the fitness of one random neighbor
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*
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* The values are collected during the random search
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*
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*/
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template <class Neighbor>
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class moMHFitnessCloudSampling : public moSampling<Neighbor>
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{
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public:
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typedef typename Neighbor::EOT EOT ;
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using moSampling<Neighbor>::localSearch;
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
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* @param _fullEval Fitness function, full evaluation function
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* @param _eval neighbor evaluation, incremental evaluation function
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* @param _nbStep Number of step of the MH sampling
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*/
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moMHFitnessCloudSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
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moEval<Neighbor>& _eval,
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unsigned int _nbStep) :
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moSampling<Neighbor>(_init, * new moMetropolisHasting<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep), fitnessStat),
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neighborFitnessStat(_neighborhood, _eval)
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{
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add(neighborFitnessStat);
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}
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/**
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* default destructor
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*/
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~moMHFitnessCloudSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete &localSearch;
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}
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protected:
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moFitnessStat<EOT> fitnessStat;
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moNeighborFitnessStat< Neighbor > neighborFitnessStat;
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};
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#endif
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@ -62,6 +62,7 @@ public:
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* @param _init initialisation method of the solution
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* @param _localSearch local search to sample the search space
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* @param _stat statistic to compute during the search
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* @param _monitoring the statistic is saved into the monitor if true
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*/
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template <class ValueType>
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moSampling(eoInit<EOT> & _init, moLocalSearch<Neighbor> & _localSearch, moStat<EOT,ValueType> & _stat, bool _monitoring = true) : init(_init), localSearch(_localSearch), continuator(_localSearch.getContinuator())
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@ -85,6 +86,7 @@ public:
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/**
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* Add a statistic
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* @param _stat another statistic to compute during the search
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* @param _monitoring the statistic is saved into the monitor if true
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*/
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template< class ValueType >
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void add(moStat<EOT, ValueType> & _stat, bool _monitoring = true) {
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@ -38,6 +38,7 @@ using namespace std;
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//-----------------------------------------------------------------------------
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// the sampling class
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#include <sampling/moFitnessCloudSampling.h>
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#include <sampling/moMHFitnessCloudSampling.h>
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// Declaration of types
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//-----------------------------------------------------------------------------
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@ -163,7 +164,9 @@ void main_function(int argc, char **argv)
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// - fitness function
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// - neighbor evaluation
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// - number of solutions to sample
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moFitnessCloudSampling<Neighbor> sampling(random, neighborhood, fullEval, neighborEval, nbSol);
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//moFitnessCloudSampling<Neighbor> sampling(random, neighborhood, fullEval, neighborEval, nbSol);
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moMHFitnessCloudSampling<Neighbor> sampling(random, neighborhood, fullEval, neighborEval, nbSol);
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/* =========================================================
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*
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