Ajout du moHillClimberSampling, et ajout des méthodes init dans les stats ;)
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1787 331e1502-861f-0410-8da2-ba01fb791d7f
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9 changed files with 330 additions and 12 deletions
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@ -58,7 +58,7 @@ public:
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*/
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moRandomSearch(eoInit<EOT> & _init, eoEvalFunc<EOT>& _fullEval, unsigned _nbSolMax):
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moLocalSearch<Neighbor>(explorer, trueCont, _fullEval),
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explorer(_init, _fullEval, _nbSolMax)
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explorer(_init, _fullEval, _nbSolMax - 1)
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{}
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/**
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@ -69,7 +69,7 @@ public:
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*/
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moRandomSearch(eoInit<EOT> & _init, eoEvalFunc<EOT>& _fullEval, unsigned _nbSolMax, moContinuator<Neighbor>& _cont):
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moLocalSearch<Neighbor>(explorer, _cont, _fullEval),
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explorer(_init, _fullEval, _nbSolMax)
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explorer(_init, _fullEval, _nbSolMax - 1)
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{}
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private:
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@ -137,6 +137,7 @@
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#include <sampling/moSampling.h>
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#include <sampling/moDensityOfStatesSampling.h>
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#include <sampling/moAutocorrelationSampling.h>
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#include <sampling/moHillClimberSampling.h>
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#include <problems/bitString/moBitNeighbor.h>
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#include <problems/eval/moOneMaxIncrEval.h>
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@ -54,8 +54,8 @@ public:
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* @param _solution to perturb
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*/
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void operator()(EOT& _solution){
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init(solution);
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ls(solution);
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init(_solution);
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ls(_solution);
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}
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private:
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@ -43,10 +43,9 @@
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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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* To compute the density of states:
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* Sample the fitness of random solution in the search space
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* The fitness values of solutions 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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107
trunk/paradiseo-mo/src/sampling/moHillClimberSampling.h
Normal file
107
trunk/paradiseo-mo/src/sampling/moHillClimberSampling.h
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@ -0,0 +1,107 @@
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/*
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<moHillClimberSampling.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 moHillClimberSampling_h
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#define moHillClimberSampling_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 <continuator/moCheckpoint.h>
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#include <perturb/moLocalSearchInit.h>
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#include <algo/moRandomSearch.h>
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#include <algo/moSimpleHC.h>
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#include <continuator/moSolutionStat.h>
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#include <continuator/moCounterStat.h>
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#include <continuator/moStatFromStat.h>
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#include <sampling/moSampling.h>
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/**
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* To compute the length and final solution of an adaptive walk:
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* Perform a simple Hill-climber based on the neighborhood (adaptive walk),
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* The lengths of HC are collected and the final solution which are local optima
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* The adaptive walk is repeated several times
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*
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*/
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template <class Neighbor>
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class moHillClimberSampling : 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 _nbAdaptWalk Number of adaptive walks
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*/
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moHillClimberSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval, moEval<Neighbor>& _eval,
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unsigned int _nbAdaptWalk) :
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moSampling<Neighbor>(initHC, * new moRandomSearch<Neighbor>(initHC, _fullEval, _nbAdaptWalk), copyStat),
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copyStat(lengthStat),
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checkpoint(trueCont),
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hc(_neighborhood, _fullEval, _eval, checkpoint),
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initHC(_init, hc)
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{
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// to count the number of step in the HC
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checkpoint.add(lengthStat);
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// add the solution into statistics
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add(solStat);
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}
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/**
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* default destructor
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*/
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~moHillClimberSampling() {
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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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moSolutionStat<EOT> solStat;
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moCounterStat<EOT> lengthStat;
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moTrueContinuator<Neighbor> trueCont;
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moStatFromStat<EOT, unsigned int> copyStat;
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moCheckpoint<Neighbor> checkpoint;
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moSimpleHC<Neighbor> hc;
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moLocalSearchInit<Neighbor> initHC;
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};
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#endif
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@ -102,7 +102,7 @@ public:
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* the statistics are stored in the moVectorMonitor vector
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*/
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void operator()(void) {
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// clear all statisic vectors
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// clear all statistic vectors
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for(unsigned i = 0; i < monitorVec.size(); i++)
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monitorVec[i]->clear();
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@ -12,6 +12,7 @@ ADD_EXECUTABLE(testRandomNeutralWalk testRandomNeutralWalk.cpp)
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ADD_EXECUTABLE(sampling sampling.cpp)
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ADD_EXECUTABLE(densityOfStates densityOfStates.cpp)
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ADD_EXECUTABLE(autocorrelation autocorrelation.cpp)
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ADD_EXECUTABLE(adaptiveWalks adaptiveWalks.cpp)
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TARGET_LINK_LIBRARIES(testRandomWalk eoutils ga eo)
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TARGET_LINK_LIBRARIES(testMetropolisHasting eoutils ga eo)
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@ -19,3 +20,4 @@ TARGET_LINK_LIBRARIES(testRandomNeutralWalk eoutils ga eo)
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TARGET_LINK_LIBRARIES(sampling eoutils ga eo)
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TARGET_LINK_LIBRARIES(densityOfStates eoutils ga eo)
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TARGET_LINK_LIBRARIES(autocorrelation eoutils ga eo)
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TARGET_LINK_LIBRARIES(adaptiveWalks eoutils ga eo)
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209
trunk/paradiseo-mo/tutorial/Lesson6/adaptiveWalks.cpp
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209
trunk/paradiseo-mo/tutorial/Lesson6/adaptiveWalks.cpp
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@ -0,0 +1,209 @@
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//-----------------------------------------------------------------------------
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/** adaptiveWalks.cpp
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*
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* SV - 05/05/10
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*
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*/
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//-----------------------------------------------------------------------------
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// standard includes
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#define HAVE_SSTREAM
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#include <stdexcept> // runtime_error
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#include <iostream> // cout
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#include <sstream> // ostrstream, istrstream
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#include <fstream>
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#include <string.h>
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// the general include for eo
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#include <eo>
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// declaration of the namespace
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using namespace std;
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//-----------------------------------------------------------------------------
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// representation of solutions, and neighbors
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#include <ga/eoBit.h> // bit string : see also EO tutorial lesson 1: FirstBitGA.cpp
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#include <problems/bitString/moBitNeighbor.h> // neighbor of bit string
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//-----------------------------------------------------------------------------
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// fitness function, and evaluation of neighbors
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#include <eval/oneMaxEval.h>
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#include <problems/eval/moOneMaxIncrEval.h>
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#include <eval/moFullEvalByModif.h>
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//-----------------------------------------------------------------------------
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// neighborhood description
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#include <neighborhood/moOrderNeighborhood.h> // visit all the neighbors
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//-----------------------------------------------------------------------------
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// the sampling class
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#include <sampling/moHillClimberSampling.h>
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// Declaration of types
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//-----------------------------------------------------------------------------
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// Indi is the typedef of the solution type like in paradisEO-eo
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typedef eoBit<unsigned int> Indi; // bit string with unsigned fitness type
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// Neighbor is the typedef of the neighbor type,
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// Neighbor = How to compute the neighbor from the solution + information on it (i.e. fitness)
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// all classes from paradisEO-mo use this template type
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typedef moBitNeighbor<unsigned int> Neighbor ; // bit string neighbor with unsigned fitness type
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void main_function(int argc, char **argv)
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{
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/* =========================================================
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*
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* Parameters
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*
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* ========================================================= */
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// more information on the input parameters: see EO tutorial lesson 3
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// but don't care at first it just read the parameters of the bit string size and the random seed.
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// First define a parser from the command-line arguments
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eoParser parser(argc, argv);
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// For each parameter, define Parameter, read it through the parser,
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// and assign the value to the variable
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// random seed parameter
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eoValueParam<uint32_t> seedParam(time(0), "seed", "Random number seed", 'S');
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parser.processParam( seedParam );
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unsigned seed = seedParam.value();
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// length of the bit string
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eoValueParam<unsigned int> vecSizeParam(20, "vecSize", "Genotype size", 'V');
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parser.processParam( vecSizeParam, "Representation" );
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unsigned vecSize = vecSizeParam.value();
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// the number of adaptive walks
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eoValueParam<unsigned int> solParam(100, "nbSol", "Number of adaptive walks", 'n');
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parser.processParam( solParam, "Representation" );
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unsigned nbSol = solParam.value();
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// the name of the output file
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string str_out = "out.dat"; // default value
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eoValueParam<string> outParam(str_out.c_str(), "out", "Output file of the sampling", 'o');
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parser.processParam(outParam, "Persistence" );
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// the name of the "status" file where all actual parameter values will be saved
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string str_status = parser.ProgramName() + ".status"; // default value
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eoValueParam<string> statusParam(str_status.c_str(), "status", "Status file");
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parser.processParam( statusParam, "Persistence" );
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// do the following AFTER ALL PARAMETERS HAVE BEEN PROCESSED
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// i.e. in case you need parameters somewhere else, postpone these
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if (parser.userNeedsHelp()) {
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parser.printHelp(cout);
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exit(1);
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}
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if (statusParam.value() != "") {
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ofstream os(statusParam.value().c_str());
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os << parser;// and you can use that file as parameter file
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}
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/* =========================================================
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*
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* Random seed
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*
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* ========================================================= */
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// reproducible random seed: if you don't change SEED above,
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// you'll aways get the same result, NOT a random run
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// more information: see EO tutorial lesson 1 (FirstBitGA.cpp)
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rng.reseed(seed);
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/* =========================================================
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*
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* Initialization of the solution
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*
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* ========================================================= */
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// a Indi random initializer: each bit is random
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// more information: see EO tutorial lesson 1 (FirstBitGA.cpp)
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eoUniformGenerator<bool> uGen;
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eoInitFixedLength<Indi> random(vecSize, uGen);
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/* =========================================================
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*
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* Eval fitness function (full evaluation)
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*
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* ========================================================= */
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// the fitness function is just the number of 1 in the bit string
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oneMaxEval<Indi> fullEval;
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/* =========================================================
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*
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* evaluation of a neighbor solution
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*
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* ========================================================= */
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// Use it if there is no incremental evaluation: a neighbor is evaluated by the full evaluation of a solution
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// moFullEvalByModif<Neighbor> neighborEval(fullEval);
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// Incremental evaluation of the neighbor: fitness is modified by +/- 1
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moOneMaxIncrEval<Neighbor> neighborEval;
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/* =========================================================
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*
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* the neighborhood of a solution
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*
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* ========================================================= */
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// Exploration of the neighborhood in order
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// from bit 0 to bit vecSize-1
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moOrderNeighborhood<Neighbor> neighborhood(vecSize);
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/* =========================================================
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*
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* The sampling of the search space
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*
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* ========================================================= */
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// sampling object :
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// - random initialization
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// - local search to sample the search space
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// - one statistic to compute
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moHillClimberSampling<Neighbor> sampling(random, neighborhood, fullEval, neighborEval, nbSol);
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/* =========================================================
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*
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* execute the sampling
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*
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* ========================================================= */
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sampling();
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/* =========================================================
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*
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* export the sampling
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*
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* ========================================================= */
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// to export the statistics into file
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sampling.fileExport(str_out);
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// to get the values of statistics
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// so, you can compute some statistics in c++ from the data
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const std::vector<double> & lengthValues = sampling.getValues(0);
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std::cout << "First values:" << std::endl;
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std::cout << "Length " << lengthValues[0] << std::endl;
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std::cout << "Last values:" << std::endl;
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std::cout << "Length " << lengthValues[lengthValues.size() - 1] << std::endl;
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}
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// A main that catches the exceptions
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int main(int argc, char **argv)
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{
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try {
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main_function(argc, argv);
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}
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catch (exception& e) {
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cout << "Exception: " << e.what() << '\n';
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}
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return 1;
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}
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@ -70,7 +70,7 @@ void main_function(int argc, char **argv)
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parser.processParam( vecSizeParam, "Representation" );
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unsigned vecSize = vecSizeParam.value();
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// the number of steps of the random walk
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// the number of solution sampled
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eoValueParam<unsigned int> solParam(100, "nbSol", "Number of random solution", 'n');
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parser.processParam( solParam, "Representation" );
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unsigned nbSol = solParam.value();
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// sampling object :
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// - random initialization
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// - local search to sample the search space
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// - one statistic to compute
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// - fitness function
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// - number of solutions to sample
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moDensityOfStatesSampling<Neighbor> sampling(random, fullEval, nbSol);
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/* =========================================================
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