c60f80b480
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1809 331e1502-861f-0410-8da2-ba01fb791d7f
89 lines
3.3 KiB
C++
89 lines
3.3 KiB
C++
/*
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<moAutocorrelationSampling.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 moAutocorrelationSampling_h
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#define moAutocorrelationSampling_h
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#include <eoInit.h>
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#include <eval/moEval.h>
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#include <eoEvalFunc.h>
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#include <algo/moRandomWalk.h>
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#include <continuator/moFitnessStat.h>
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#include <sampling/moSampling.h>
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/**
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* To compute the autocorrelation function:
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* Perform a random walk based on the neighborhood,
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* The fitness values of solutions are collected during the random walk
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* The autocorrelation can be computed from the serie of fitness values
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*
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*/
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template <class Neighbor>
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class moAutocorrelationSampling : 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 giving neighbor in random order
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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 steps of the random walk
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*/
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moAutocorrelationSampling(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 moRandomWalk<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep), fitnessStat){}
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/**
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* default destructor
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*/
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~moAutocorrelationSampling() {
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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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};
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#endif
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