removed useless files for release 1.0
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@677 331e1502-861f-0410-8da2-ba01fb791d7f
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4 changed files with 0 additions and 1036 deletions
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/*
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* <moeoIBMOLS.h>
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* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
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* (C) OPAC Team, LIFL, 2002-2007
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*
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* Arnaud Liefooghe
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*
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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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*
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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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*
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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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*
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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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*/
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//-----------------------------------------------------------------------------
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#ifndef MOEOIBMOLS_H_
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#define MOEOIBMOLS_H_
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#include <math.h>
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#include <eoContinue.h>
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#include <eoEvalFunc.h>
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#include <eoPop.h>
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#include <moMove.h>
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#include <moMoveInit.h>
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#include <moNextMove.h>
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#include <algo/moeoLS.h>
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#include <archive/moeoArchive.h>
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#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
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#include <move/moeoMoveIncrEval.h>
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/**
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* Indicator-Based Multi-Objective Local Search (IBMOLS) as described in
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* Basseur M., Burke K. : "Indicator-Based Multi-Objective Local Search" (2007).
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*/
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template < class MOEOT, class Move >
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class moeoIBMOLS : public moeoLS < MOEOT, eoPop < MOEOT > & >
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{
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public:
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/** The type of objective vector */
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typedef typename MOEOT::ObjectiveVector ObjectiveVector;
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/**
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* Ctor.
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* @param _moveInit the move initializer
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* @param _nextMove the neighborhood explorer
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* @param _eval the full evaluation
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* @param _moveIncrEval the incremental evaluation
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* @param _fitnessAssignment the fitness assignment strategy
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* @param _continuator the stopping criteria
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*/
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moeoIBMOLS(
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moMoveInit < Move > & _moveInit,
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moNextMove < Move > & _nextMove,
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eoEvalFunc < MOEOT > & _eval,
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moeoMoveIncrEval < Move > & _moveIncrEval,
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moeoBinaryIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
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eoContinue < MOEOT > & _continuator
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) :
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moveInit(_moveInit),
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nextMove(_nextMove),
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eval(_eval),
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moveIncrEval(_moveIncrEval),
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fitnessAssignment (_fitnessAssignment),
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continuator (_continuator)
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{}
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/**
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* Apply the local search until a local archive does not change or
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* another stopping criteria is met and update the archive _arch with new non-dominated solutions.
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* @param _pop the initial population
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* @param _arch the (updated) archive
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*/
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void operator() (eoPop < MOEOT > & _pop, moeoArchive < MOEOT > & _arch)
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{
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// evaluation of the objective values
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/*
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for (unsigned int i=0; i<_pop.size(); i++)
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{
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eval(_pop[i]);
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}
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*/
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// fitness assignment for the whole population
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fitnessAssignment(_pop);
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// creation of a local archive
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moeoArchive < MOEOT > archive;
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// creation of another local archive (for the stopping criteria)
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moeoArchive < MOEOT > previousArchive;
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// update the archive with the initial population
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archive.update(_pop);
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do
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{
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previousArchive.update(archive);
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oneStep(_pop);
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archive.update(_pop);
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} while ( (! archive.equals(previousArchive)) && (continuator(_arch)) );
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_arch.update(archive);
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}
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private:
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/** the move initializer */
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moMoveInit < Move > & moveInit;
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/** the neighborhood explorer */
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moNextMove < Move > & nextMove;
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/** the full evaluation */
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eoEvalFunc < MOEOT > & eval;
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/** the incremental evaluation */
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moeoMoveIncrEval < Move > & moveIncrEval;
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/** the fitness assignment strategy */
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moeoBinaryIndicatorBasedFitnessAssignment < MOEOT > & fitnessAssignment;
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/** the stopping criteria */
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eoContinue < MOEOT > & continuator;
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/**
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* Apply one step of the local search to the population _pop
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* @param _pop the population
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*/
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void oneStep (eoPop < MOEOT > & _pop)
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{
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// the move
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Move move;
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// the objective vector and the fitness of the current solution
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ObjectiveVector x_objVec;
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double x_fitness;
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// the index, the objective vector and the fitness of the worst solution in the population (-1 implies that the worst is the newly created one)
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int worst_idx;
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ObjectiveVector worst_objVec;
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double worst_fitness;
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////////////////////////////////////////////
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// the indexes and the objective vectors of the extreme non-dominated points
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int ext_0_idx, ext_1_idx;
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ObjectiveVector ext_0_objVec, ext_1_objVec;
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unsigned int ind;
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////////////////////////////////////////////
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// the index of the current solution to be explored
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unsigned int i=0;
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// initilization of the move for the first individual
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moveInit(move, _pop[i]);
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while (i<_pop.size() && continuator(_pop))
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{
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// x = one neigbour of pop[i]
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// evaluate x in the objective space
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x_objVec = moveIncrEval(move, _pop[i]);
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// update every fitness values to take x into account and compute the fitness of x
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x_fitness = fitnessAssignment.updateByAdding(_pop, x_objVec);
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// extreme solutions (min only!)
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ext_0_idx = -1;
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ext_0_objVec = x_objVec;
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ext_1_idx = -1;
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ext_1_objVec = x_objVec;
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for (unsigned int k=0; k<_pop.size(); k++)
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{
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// ext_0
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if (_pop[k].objectiveVector()[0] < ext_0_objVec[0])
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{
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ext_0_idx = k;
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ext_0_objVec = _pop[k].objectiveVector();
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}
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else if ( (_pop[k].objectiveVector()[0] == ext_0_objVec[0]) && (_pop[k].objectiveVector()[1] < ext_0_objVec[1]) )
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{
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ext_0_idx = k;
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ext_0_objVec = _pop[k].objectiveVector();
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}
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// ext_1
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else if (_pop[k].objectiveVector()[1] < ext_1_objVec[1])
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{
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ext_1_idx = k;
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ext_1_objVec = _pop[k].objectiveVector();
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}
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else if ( (_pop[k].objectiveVector()[1] == ext_1_objVec[1]) && (_pop[k].objectiveVector()[0] < ext_1_objVec[0]) )
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{
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ext_1_idx = k;
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ext_1_objVec = _pop[k].objectiveVector();
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}
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}
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// worst init
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if (ext_0_idx == -1)
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{
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ind = 0;
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while (ind == ext_1_idx)
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{
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ind++;
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}
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worst_idx = ind;
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worst_objVec = _pop[ind].objectiveVector();
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worst_fitness = _pop[ind].fitness();
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}
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else if (ext_1_idx == -1)
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{
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ind = 0;
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while (ind == ext_0_idx)
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{
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ind++;
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}
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worst_idx = ind;
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worst_objVec = _pop[ind].objectiveVector();
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worst_fitness = _pop[ind].fitness();
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}
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else
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{
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worst_idx = -1;
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worst_objVec = x_objVec;
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worst_fitness = x_fitness;
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// who is the worst ?
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for (unsigned int j=0; j<_pop.size(); j++)
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{
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if ( (j!=ext_0_idx) && (j!=ext_1_idx) )
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{
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if (_pop[j].fitness() < worst_fitness)
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{
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worst_idx = j;
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worst_objVec = _pop[j].objectiveVector();
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worst_fitness = _pop[j].fitness();
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}
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}
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}
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// if the worst solution is the new one
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if (worst_idx == -1)
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{
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// if all its neighbours have been explored,
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// let's explore the neighborhoud of the next individual
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if (! nextMove(move, _pop[i]))
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{
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i++;
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if (i<_pop.size())
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{
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// initilization of the move for the next individual
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moveInit(move, _pop[i]);
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}
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}
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}
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// if the worst solution is located before _pop[i]
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else if (worst_idx <= i)
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{
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// the new solution takes place insteed of _pop[worst_idx]
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_pop[worst_idx] = _pop[i];
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move(_pop[worst_idx]);
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_pop[worst_idx].objectiveVector(x_objVec);
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_pop[worst_idx].fitness(x_fitness);
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// let's explore the neighborhoud of the next individual
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i++;
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if (i<_pop.size())
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{
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// initilization of the move for the next individual
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moveInit(move, _pop[i]);
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}
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}
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// if the worst solution is located after _pop[i]
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else if (worst_idx > i)
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{
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// the new solution takes place insteed of _pop[i+1] and _pop[worst_idx] is deleted
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_pop[worst_idx] = _pop[i+1];
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_pop[i+1] = _pop[i];
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move(_pop[i+1]);
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_pop[i+1].objectiveVector(x_objVec);
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_pop[i+1].fitness(x_fitness);
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// let's explore the neighborhoud of the individual _pop[i+2]
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i += 2;
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if (i<_pop.size())
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{
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// initilization of the move for the next individual
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moveInit(move, _pop[i]);
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}
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}
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// update fitness values
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fitnessAssignment.updateByDeleting(_pop, worst_objVec);
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}
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}
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// INUTILE !!!!
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/**
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* Apply one step of the local search to the population _pop
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* @param _pop the population
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*/
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void new_oneStep (eoPop < MOEOT > & _pop)
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{
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// the move
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Move move;
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// the objective vector and the fitness of the current solution
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ObjectiveVector x_objVec;
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double x_fitness;
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// the index, the objective vector and the fitness of the worst solution in the population (-1 implies that the worst is the newly created one)
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int worst_idx;
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ObjectiveVector worst_objVec;
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double worst_fitness;
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////////////////////////////////////////////
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// the index of the extreme non-dominated points
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int ext_0_idx, ext_1_idx;
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unsigned int ind;
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////////////////////////////////////////////
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// the index current of the current solution to be explored
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unsigned int i=0;
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// initilization of the move for the first individual
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moveInit(move, _pop[i]);
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while (i<_pop.size() && continuator(_pop))
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{
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// x = one neigbour of pop[i]
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// evaluate x in the objective space
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x_objVec = moveIncrEval(move, _pop[i]);
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// update every fitness values to take x into account and compute the fitness of x
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x_fitness = fitnessAssignment.updateByAdding(_pop, x_objVec);
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// extremes solutions
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OneObjectiveComparator comp0(0);
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ext_0_idx = std::min_element(_pop.begin(), _pop.end(), comp0) - _pop.begin();
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OneObjectiveComparator comp1(1);
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ext_1_idx = std::min_element(_pop.begin(), _pop.end(), comp1) - _pop.begin();
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// new = extreme ?
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if (x_objVec[0] < _pop[ext_0_idx].objectiveVector()[0])
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{
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ext_0_idx = -1;
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}
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else if ( (x_objVec[0] == _pop[ext_0_idx].objectiveVector()[0]) && (x_objVec[1] < _pop[ext_0_idx].objectiveVector()[1]) )
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{
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ext_0_idx = -1;
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}
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else if (x_objVec[1] < _pop[ext_1_idx].objectiveVector()[1])
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{
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ext_1_idx = -1;
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}
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else if ( (x_objVec[1] == _pop[ext_1_idx].objectiveVector()[1]) && (x_objVec[0] < _pop[ext_1_idx].objectiveVector()[0]) )
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{
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ext_1_idx = -1;
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}
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// worst init
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if (ext_0_idx == -1)
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{
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ind = 0;
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while (ind == ext_1_idx)
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{
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ind++;
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}
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worst_idx = ind;
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worst_objVec = _pop[ind].objectiveVector();
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worst_fitness = _pop[ind].fitness();
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}
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else if (ext_1_idx == -1)
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{
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ind = 0;
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while (ind == ext_0_idx)
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{
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ind++;
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}
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worst_idx = ind;
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worst_objVec = _pop[ind].objectiveVector();
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worst_fitness = _pop[ind].fitness();
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}
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else
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{
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worst_idx = -1;
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worst_objVec = x_objVec;
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worst_fitness = x_fitness;
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// who is the worst ?
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for (unsigned int j=0; j<_pop.size(); j++)
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{
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if ( (j!=ext_0_idx) && (j!=ext_1_idx) )
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{
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if (_pop[j].fitness() < worst_fitness)
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{
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worst_idx = j;
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worst_objVec = _pop[j].objectiveVector();
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worst_fitness = _pop[j].fitness();
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}
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}
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}
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// if the worst solution is the new one
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if (worst_idx == -1)
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{
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// if all its neighbours have been explored,
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// let's explore the neighborhoud of the next individual
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if (! nextMove(move, _pop[i]))
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{
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i++;
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if (i<_pop.size())
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{
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// initilization of the move for the next individual
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moveInit(move, _pop[i]);
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}
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}
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}
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// if the worst solution is located before _pop[i]
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else if (worst_idx <= i)
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{
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// the new solution takes place insteed of _pop[worst_idx]
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_pop[worst_idx] = _pop[i];
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move(_pop[worst_idx]);
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_pop[worst_idx].objectiveVector(x_objVec);
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_pop[worst_idx].fitness(x_fitness);
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// let's explore the neighborhoud of the next individual
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i++;
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if (i<_pop.size())
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{
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// initilization of the move for the next individual
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||||
moveInit(move, _pop[i]);
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||||
}
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||||
}
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||||
// if the worst solution is located after _pop[i]
|
||||
else if (worst_idx > i)
|
||||
{
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||||
// the new solution takes place insteed of _pop[i+1] and _pop[worst_idx] is deleted
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||||
_pop[worst_idx] = _pop[i+1];
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_pop[i+1] = _pop[i];
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move(_pop[i+1]);
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_pop[i+1].objectiveVector(x_objVec);
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_pop[i+1].fitness(x_fitness);
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// let's explore the neighborhoud of the individual _pop[i+2]
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i += 2;
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if (i<_pop.size())
|
||||
{
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// initilization of the move for the next individual
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||||
moveInit(move, _pop[i]);
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||||
}
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}
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// update fitness values
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fitnessAssignment.updateByDeleting(_pop, worst_objVec);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////////////////
|
||||
class OneObjectiveComparator : public moeoComparator < MOEOT >
|
||||
{
|
||||
public:
|
||||
OneObjectiveComparator(unsigned int _obj) : obj(_obj)
|
||||
{
|
||||
if (obj > MOEOT::ObjectiveVector::nObjectives())
|
||||
{
|
||||
throw std::runtime_error("Problem with the index of objective in OneObjectiveComparator");
|
||||
}
|
||||
}
|
||||
const bool operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
|
||||
{
|
||||
if (_moeo1.objectiveVector()[obj] < _moeo2.objectiveVector()[obj])
|
||||
{
|
||||
return true;
|
||||
}
|
||||
else
|
||||
{
|
||||
return (_moeo1.objectiveVector()[obj] == _moeo2.objectiveVector()[obj]) && (_moeo1.objectiveVector()[(obj+1)%2] < _moeo2.objectiveVector()[(obj+1)%2]);
|
||||
}
|
||||
}
|
||||
private:
|
||||
unsigned int obj;
|
||||
};
|
||||
//////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
|
||||
|
||||
};
|
||||
|
||||
#endif /*MOEOIBMOLS_H_*/
|
||||
|
|
@ -1,238 +0,0 @@
|
|||
/*
|
||||
* <moeoIteratedIBMOLS.h>
|
||||
* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
|
||||
* (C) OPAC Team, LIFL, 2002-2007
|
||||
*
|
||||
* Arnaud Liefooghe
|
||||
*
|
||||
* This software is governed by the CeCILL license under French law and
|
||||
* abiding by the rules of distribution of free software. You can use,
|
||||
* modify and/ or redistribute the software under the terms of the CeCILL
|
||||
* license as circulated by CEA, CNRS and INRIA at the following URL
|
||||
* "http://www.cecill.info".
|
||||
*
|
||||
* As a counterpart to the access to the source code and rights to copy,
|
||||
* modify and redistribute granted by the license, users are provided only
|
||||
* with a limited warranty and the software's author, the holder of the
|
||||
* economic rights, and the successive licensors have only limited liability.
|
||||
*
|
||||
* In this respect, the user's attention is drawn to the risks associated
|
||||
* with loading, using, modifying and/or developing or reproducing the
|
||||
* software by the user in light of its specific status of free software,
|
||||
* that may mean that it is complicated to manipulate, and that also
|
||||
* therefore means that it is reserved for developers and experienced
|
||||
* professionals having in-depth computer knowledge. Users are therefore
|
||||
* encouraged to load and test the software's suitability as regards their
|
||||
* requirements in conditions enabling the security of their systems and/or
|
||||
* data to be ensured and, more generally, to use and operate it in the
|
||||
* same conditions as regards security.
|
||||
* The fact that you are presently reading this means that you have had
|
||||
* knowledge of the CeCILL license and that you accept its terms.
|
||||
*
|
||||
* ParadisEO WebSite : http://paradiseo.gforge.inria.fr
|
||||
* Contact: paradiseo-help@lists.gforge.inria.fr
|
||||
*
|
||||
*/
|
||||
//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MOEOITERATEDIBMOLS_H_
|
||||
#define MOEOITERATEDIBMOLS_H_
|
||||
|
||||
#include <eoContinue.h>
|
||||
#include <eoEvalFunc.h>
|
||||
#include <eoOp.h>
|
||||
#include <eoPop.h>
|
||||
#include <utils/rnd_generators.h>
|
||||
#include <moMove.h>
|
||||
#include <moMoveInit.h>
|
||||
#include <moNextMove.h>
|
||||
#include <algo/moeoIBMOLS.h>
|
||||
#include <algo/moeoLS.h>
|
||||
#include <archive/moeoArchive.h>
|
||||
#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
|
||||
#include <move/moeoMoveIncrEval.h>
|
||||
|
||||
|
||||
|
||||
//#include <rsCrossQuad.h>
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* Iterated version of IBMOLS as described in
|
||||
* Basseur M., Burke K. : "Indicator-Based Multi-Objective Local Search" (2007).
|
||||
*/
|
||||
template < class MOEOT, class Move >
|
||||
class moeoIteratedIBMOLS : public moeoLS < MOEOT, eoPop < MOEOT > & >
|
||||
{
|
||||
public:
|
||||
|
||||
/** The type of objective vector */
|
||||
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
|
||||
|
||||
|
||||
/**
|
||||
* Ctor.
|
||||
* @param _moveInit the move initializer
|
||||
* @param _nextMove the neighborhood explorer
|
||||
* @param _eval the full evaluation
|
||||
* @param _moveIncrEval the incremental evaluation
|
||||
* @param _fitnessAssignment the fitness assignment strategy
|
||||
* @param _continuator the stopping criteria
|
||||
* @param _monOp the monary operator
|
||||
* @param _randomMonOp the random monary operator (or random initializer)
|
||||
* @param _nNoiseIterations the number of iterations to apply the random noise
|
||||
*/
|
||||
moeoIteratedIBMOLS(
|
||||
moMoveInit < Move > & _moveInit,
|
||||
moNextMove < Move > & _nextMove,
|
||||
eoEvalFunc < MOEOT > & _eval,
|
||||
moeoMoveIncrEval < Move > & _moveIncrEval,
|
||||
moeoBinaryIndicatorBasedFitnessAssignment < MOEOT > & _fitnessAssignment,
|
||||
eoContinue < MOEOT > & _continuator,
|
||||
eoMonOp < MOEOT > & _monOp,
|
||||
eoMonOp < MOEOT > & _randomMonOp,
|
||||
unsigned int _nNoiseIterations=1
|
||||
) :
|
||||
ibmols(_moveInit, _nextMove, _eval, _moveIncrEval, _fitnessAssignment, _continuator),
|
||||
eval(_eval),
|
||||
continuator(_continuator),
|
||||
monOp(_monOp),
|
||||
randomMonOp(_randomMonOp),
|
||||
nNoiseIterations(_nNoiseIterations)
|
||||
{}
|
||||
|
||||
|
||||
/**
|
||||
* Apply the local search iteratively until the stopping criteria is met.
|
||||
* @param _pop the initial population
|
||||
* @param _arch the (updated) archive
|
||||
*/
|
||||
void operator() (eoPop < MOEOT > & _pop, moeoArchive < MOEOT > & _arch)
|
||||
{
|
||||
_arch.update(_pop);
|
||||
ibmols(_pop, _arch);
|
||||
while (continuator(_arch))
|
||||
{
|
||||
// generate new solutions from the archive
|
||||
generateNewSolutions(_pop, _arch);
|
||||
// apply the local search (the global archive is updated in the sub-function)
|
||||
ibmols(_pop, _arch);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
private:
|
||||
|
||||
/** the local search to iterate */
|
||||
moeoIBMOLS < MOEOT, Move > ibmols;
|
||||
/** the full evaluation */
|
||||
eoEvalFunc < MOEOT > & eval;
|
||||
/** the stopping criteria */
|
||||
eoContinue < MOEOT > & continuator;
|
||||
/** the monary operator */
|
||||
eoMonOp < MOEOT > & monOp;
|
||||
/** the random monary operator (or random initializer) */
|
||||
eoMonOp < MOEOT > & randomMonOp;
|
||||
/** the number of iterations to apply the random noise */
|
||||
unsigned int nNoiseIterations;
|
||||
|
||||
|
||||
/**
|
||||
* Creates new population randomly initialized and/or initialized from the archive _arch.
|
||||
* @param _pop the output population
|
||||
* @param _arch the archive
|
||||
*/
|
||||
void generateNewSolutions(eoPop < MOEOT > & _pop, const moeoArchive < MOEOT > & _arch)
|
||||
{
|
||||
// shuffle vector for the random selection of individuals
|
||||
vector<unsigned int> shuffle;
|
||||
shuffle.resize(std::max(_pop.size(), _arch.size()));
|
||||
// init shuffle
|
||||
for (unsigned int i=0; i<shuffle.size(); i++)
|
||||
{
|
||||
shuffle[i] = i;
|
||||
}
|
||||
// randomize shuffle
|
||||
UF_random_generator <unsigned int> gen;
|
||||
std::random_shuffle(shuffle.begin(), shuffle.end(), gen);
|
||||
// start the creation of new solutions
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
if (shuffle[i] < _arch.size()) // the given archive contains the individual i
|
||||
{
|
||||
// add it to the resulting pop
|
||||
_pop[i] = _arch[shuffle[i]];
|
||||
// apply noise
|
||||
for (unsigned int j=0; j<nNoiseIterations; j++)
|
||||
{
|
||||
monOp(_pop[i]);
|
||||
}
|
||||
}
|
||||
else // a random solution needs to be added
|
||||
{
|
||||
// random initialization
|
||||
randomMonOp(_pop[i]);
|
||||
}
|
||||
// evaluation of the new individual
|
||||
_pop[i].invalidate();
|
||||
eval(_pop[i]);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// A DEVELOPPER RAPIDEMENT POUR TESTER AVEC CROSSOVER //
|
||||
/*
|
||||
void generateNewSolutions2(eoPop < MOEOT > & _pop, const moeoArchive < MOEOT > & _arch)
|
||||
{
|
||||
// here, we must have a QuadOp !
|
||||
//eoQuadOp < MOEOT > quadOp;
|
||||
rsCrossQuad quadOp;
|
||||
// shuffle vector for the random selection of individuals
|
||||
vector<unsigned int> shuffle;
|
||||
shuffle.resize(_arch.size());
|
||||
// init shuffle
|
||||
for (unsigned int i=0; i<shuffle.size(); i++)
|
||||
{
|
||||
shuffle[i] = i;
|
||||
}
|
||||
// randomize shuffle
|
||||
UF_random_generator <unsigned int int> gen;
|
||||
std::random_shuffle(shuffle.begin(), shuffle.end(), gen);
|
||||
// start the creation of new solutions
|
||||
unsigned int i=0;
|
||||
while ((i<_pop.size()-1) && (i<_arch.size()-1))
|
||||
{
|
||||
_pop[i] = _arch[shuffle[i]];
|
||||
_pop[i+1] = _arch[shuffle[i+1]];
|
||||
// then, apply the operator nIterationsNoise times
|
||||
for (unsigned int j=0; j<nNoiseIterations; j++)
|
||||
{
|
||||
quadOp(_pop[i], _pop[i+1]);
|
||||
}
|
||||
eval(_pop[i]);
|
||||
eval(_pop[i+1]);
|
||||
i=i+2;
|
||||
}
|
||||
// do we have to add some random solutions ?
|
||||
while (i<_pop.size())
|
||||
{
|
||||
randomMonOp(_pop[i]);
|
||||
eval(_pop[i]);
|
||||
i++;
|
||||
}
|
||||
}
|
||||
*/
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
};
|
||||
|
||||
#endif /*MOEOITERATEDIBMOLS_H_*/
|
||||
|
|
@ -1,146 +0,0 @@
|
|||
/*
|
||||
* <make_ls_moeo.h>
|
||||
* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
|
||||
* (C) OPAC Team, LIFL, 2002-2007
|
||||
*
|
||||
* Arnaud Liefooghe
|
||||
*
|
||||
* This software is governed by the CeCILL license under French law and
|
||||
* abiding by the rules of distribution of free software. You can use,
|
||||
* modify and/ or redistribute the software under the terms of the CeCILL
|
||||
* license as circulated by CEA, CNRS and INRIA at the following URL
|
||||
* "http://www.cecill.info".
|
||||
*
|
||||
* As a counterpart to the access to the source code and rights to copy,
|
||||
* modify and redistribute granted by the license, users are provided only
|
||||
* with a limited warranty and the software's author, the holder of the
|
||||
* economic rights, and the successive licensors have only limited liability.
|
||||
*
|
||||
* In this respect, the user's attention is drawn to the risks associated
|
||||
* with loading, using, modifying and/or developing or reproducing the
|
||||
* software by the user in light of its specific status of free software,
|
||||
* that may mean that it is complicated to manipulate, and that also
|
||||
* therefore means that it is reserved for developers and experienced
|
||||
* professionals having in-depth computer knowledge. Users are therefore
|
||||
* encouraged to load and test the software's suitability as regards their
|
||||
* requirements in conditions enabling the security of their systems and/or
|
||||
* data to be ensured and, more generally, to use and operate it in the
|
||||
* same conditions as regards security.
|
||||
* The fact that you are presently reading this means that you have had
|
||||
* knowledge of the CeCILL license and that you accept its terms.
|
||||
*
|
||||
* ParadisEO WebSite : http://paradiseo.gforge.inria.fr
|
||||
* Contact: paradiseo-help@lists.gforge.inria.fr
|
||||
*
|
||||
*/
|
||||
//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MAKE_LS_MOEO_H_
|
||||
#define MAKE_LS_MOEO_H_
|
||||
|
||||
#include <eoContinue.h>
|
||||
#include <eoEvalFunc.h>
|
||||
#include <eoGenOp.h>
|
||||
#include <utils/eoParser.h>
|
||||
#include <utils/eoState.h>
|
||||
#include <algo/moeoIBMOLS.h>
|
||||
#include <algo/moeoIteratedIBMOLS.h>
|
||||
#include <algo/moeoLS.h>
|
||||
#include <archive/moeoArchive.h>
|
||||
#include <fitness/moeoBinaryIndicatorBasedFitnessAssignment.h>
|
||||
#include <fitness/moeoExpBinaryIndicatorBasedFitnessAssignment.h>
|
||||
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
|
||||
#include <move/moeoMoveIncrEval.h>
|
||||
|
||||
/**
|
||||
* This functions allows to build a moeoLS from the parser
|
||||
* @param _parser the parser
|
||||
* @param _state to store allocated objects
|
||||
* @param _eval the funtions evaluator
|
||||
* @param _moveIncrEval the incremental evaluation
|
||||
* @param _continue the stopping crietria
|
||||
* @param _op the variation operators
|
||||
* @param _opInit the initilization operator
|
||||
* @param _moveInit the move initializer
|
||||
* @param _nextMove the move incrementor
|
||||
* @param _archive the archive of non-dominated solutions
|
||||
*/
|
||||
template < class MOEOT, class Move >
|
||||
moeoLS < MOEOT, eoPop<MOEOT> & > & do_make_ls_moeo (
|
||||
eoParser & _parser,
|
||||
eoState & _state,
|
||||
eoEvalFunc < MOEOT > & _eval,
|
||||
moeoMoveIncrEval < Move > & _moveIncrEval,
|
||||
eoContinue < MOEOT > & _continue,
|
||||
eoMonOp < MOEOT > & _op,
|
||||
eoMonOp < MOEOT > & _opInit,
|
||||
moMoveInit < Move > & _moveInit,
|
||||
moNextMove < Move > & _nextMove,
|
||||
moeoArchive < MOEOT > & _archive
|
||||
)
|
||||
{
|
||||
/* the objective vector type */
|
||||
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
|
||||
/* the fitness assignment strategy */
|
||||
std::string & fitnessParam = _parser.getORcreateParam(std::string("IndicatorBased"), "fitness",
|
||||
"Fitness assignment strategy parameter: IndicatorBased...", 'F',
|
||||
"Evolution Engine").value();
|
||||
std::string & indicatorParam = _parser.getORcreateParam(std::string("Epsilon"), "indicator",
|
||||
"Binary indicator to use with the IndicatorBased assignment: Epsilon, Hypervolume", 'i',
|
||||
"Evolution Engine").value();
|
||||
double rho = _parser.getORcreateParam(1.1, "rho", "reference point for the hypervolume indicator",
|
||||
'r', "Evolution Engine").value();
|
||||
double kappa = _parser.getORcreateParam(0.05, "kappa", "Scaling factor kappa for IndicatorBased",
|
||||
'k', "Evolution Engine").value();
|
||||
moeoBinaryIndicatorBasedFitnessAssignment < MOEOT > * fitnessAssignment;
|
||||
if (fitnessParam == std::string("IndicatorBased"))
|
||||
{
|
||||
// metric
|
||||
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > *metric;
|
||||
if (indicatorParam == std::string("Epsilon"))
|
||||
{
|
||||
metric = new moeoAdditiveEpsilonBinaryMetric < ObjectiveVector >;
|
||||
}
|
||||
else if (indicatorParam == std::string("Hypervolume"))
|
||||
{
|
||||
metric = new moeoHypervolumeBinaryMetric < ObjectiveVector > (rho);
|
||||
}
|
||||
else
|
||||
{
|
||||
std::string stmp = std::string("Invalid binary quality indicator: ") + indicatorParam;
|
||||
throw std::runtime_error(stmp.c_str());
|
||||
}
|
||||
fitnessAssignment = new moeoExpBinaryIndicatorBasedFitnessAssignment < MOEOT> (*metric, kappa);
|
||||
}
|
||||
else
|
||||
{
|
||||
std::string stmp = std::string("Invalid fitness assignment strategy: ") + fitnessParam;
|
||||
throw std::runtime_error(stmp.c_str());
|
||||
}
|
||||
_state.storeFunctor(fitnessAssignment);
|
||||
// number of iterations
|
||||
unsigned int n = _parser.getORcreateParam(1, "n", "Number of iterations for population Initialization", 'n', "Evolution Engine").value();
|
||||
// LS
|
||||
std::string & lsParam = _parser.getORcreateParam(std::string("I-IBMOLS"), "ls",
|
||||
"Local Search: IBMOLS, I-IBMOLS (Iterated-IBMOLS)...", 'L',
|
||||
"Evolution Engine").value();
|
||||
moeoLS < MOEOT, eoPop<MOEOT> & > * ls;
|
||||
if (lsParam == std::string("IBMOLS"))
|
||||
{
|
||||
ls = new moeoIBMOLS < MOEOT, Move > (_moveInit, _nextMove, _eval, _moveIncrEval, *fitnessAssignment, _continue);;
|
||||
}
|
||||
else if (lsParam == std::string("I-IBMOLS"))
|
||||
{
|
||||
ls = new moeoIteratedIBMOLS < MOEOT, Move > (_moveInit, _nextMove, _eval, _moveIncrEval, *fitnessAssignment, _continue, _op, _opInit, n);
|
||||
}
|
||||
else
|
||||
{
|
||||
std::string stmp = std::string("Invalid fitness assignment strategy: ") + fitnessParam;
|
||||
throw std::runtime_error(stmp.c_str());
|
||||
}
|
||||
_state.storeFunctor(ls);
|
||||
// that's it !
|
||||
return *ls;
|
||||
}
|
||||
|
||||
#endif /*MAKE_LS_MOEO_H_*/
|
||||
|
|
@ -1,134 +0,0 @@
|
|||
/*
|
||||
* <moeoReferencePointIndicatorBasedFitnessAssignment.h>
|
||||
* Copyright (C) DOLPHIN Project-Team, INRIA Futurs, 2006-2007
|
||||
* (C) OPAC Team, LIFL, 2002-2007
|
||||
*
|
||||
* Arnaud Liefooghe
|
||||
*
|
||||
* This software is governed by the CeCILL license under French law and
|
||||
* abiding by the rules of distribution of free software. You can use,
|
||||
* modify and/ or redistribute the software under the terms of the CeCILL
|
||||
* license as circulated by CEA, CNRS and INRIA at the following URL
|
||||
* "http://www.cecill.info".
|
||||
*
|
||||
* As a counterpart to the access to the source code and rights to copy,
|
||||
* modify and redistribute granted by the license, users are provided only
|
||||
* with a limited warranty and the software's author, the holder of the
|
||||
* economic rights, and the successive licensors have only limited liability.
|
||||
*
|
||||
* In this respect, the user's attention is drawn to the risks associated
|
||||
* with loading, using, modifying and/or developing or reproducing the
|
||||
* software by the user in light of its specific status of free software,
|
||||
* that may mean that it is complicated to manipulate, and that also
|
||||
* therefore means that it is reserved for developers and experienced
|
||||
* professionals having in-depth computer knowledge. Users are therefore
|
||||
* encouraged to load and test the software's suitability as regards their
|
||||
* requirements in conditions enabling the security of their systems and/or
|
||||
* data to be ensured and, more generally, to use and operate it in the
|
||||
* same conditions as regards security.
|
||||
* The fact that you are presently reading this means that you have had
|
||||
* knowledge of the CeCILL license and that you accept its terms.
|
||||
*
|
||||
* ParadisEO WebSite : http://paradiseo.gforge.inria.fr
|
||||
* Contact: paradiseo-help@lists.gforge.inria.fr
|
||||
*
|
||||
*/
|
||||
//-----------------------------------------------------------------------------
|
||||
|
||||
#ifndef MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_
|
||||
#define MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_
|
||||
|
||||
#include <math.h>
|
||||
#include <eoPop.h>
|
||||
#include <fitness/moeoFitnessAssignment.h>
|
||||
#include <metric/moeoNormalizedSolutionVsSolutionBinaryMetric.h>
|
||||
|
||||
/**
|
||||
* Fitness assignment sheme based a Reference Point and a Quality Indicator.
|
||||
*/
|
||||
template < class MOEOT >
|
||||
class moeoReferencePointIndicatorBasedFitnessAssignment : public moeoFitnessAssignment < MOEOT >
|
||||
{
|
||||
public:
|
||||
|
||||
/** The type of objective vector */
|
||||
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
|
||||
|
||||
/**
|
||||
* Ctor
|
||||
* @param _refPoint the reference point
|
||||
* @param _metric the quality indicator
|
||||
*/
|
||||
moeoReferencePointIndicatorBasedFitnessAssignment (ObjectiveVector & _refPoint, moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & _metric) :
|
||||
refPoint(_refPoint), metric(_metric)
|
||||
{}
|
||||
|
||||
|
||||
/**
|
||||
* Sets the fitness values for every solution contained in the population _pop
|
||||
* @param _pop the population
|
||||
*/
|
||||
void operator()(eoPop < MOEOT > & _pop)
|
||||
{
|
||||
// 1 - setting of the bounds
|
||||
setup(_pop);
|
||||
// 2 - setting fitnesses
|
||||
setFitnesses(_pop);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Updates the fitness values of the whole population _pop by taking the deletion of the objective vector _objVec into account.
|
||||
* @param _pop the population
|
||||
* @param _objVec the objective vector
|
||||
*/
|
||||
void updateByDeleting(eoPop < MOEOT > & _pop, ObjectiveVector & _objVec)
|
||||
{
|
||||
// nothing to do ;-)
|
||||
}
|
||||
|
||||
|
||||
protected:
|
||||
|
||||
/** the reference point */
|
||||
ObjectiveVector & refPoint;
|
||||
/** the quality indicator */
|
||||
moeoNormalizedSolutionVsSolutionBinaryMetric < ObjectiveVector, double > & metric;
|
||||
|
||||
|
||||
/**
|
||||
* Sets the bounds for every objective using the min and the max value for every objective vector of _pop (and the reference point)
|
||||
* @param _pop the population
|
||||
*/
|
||||
void setup(const eoPop < MOEOT > & _pop)
|
||||
{
|
||||
double min, max;
|
||||
for (unsigned int i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
|
||||
{
|
||||
min = refPoint[i];
|
||||
max = refPoint[i];
|
||||
for (unsigned int j=0; j<_pop.size(); j++)
|
||||
{
|
||||
min = std::min(min, _pop[j].objectiveVector()[i]);
|
||||
max = std::max(max, _pop[j].objectiveVector()[i]);
|
||||
}
|
||||
// setting of the bounds for the objective i
|
||||
metric.setup(min, max, i);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the fitness of every individual contained in the population _pop
|
||||
* @param _pop the population
|
||||
*/
|
||||
void setFitnesses(eoPop < MOEOT > & _pop)
|
||||
{
|
||||
for (unsigned int i=0; i<_pop.size(); i++)
|
||||
{
|
||||
_pop[i].fitness(- metric(_pop[i].objectiveVector(), refPoint) );
|
||||
}
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
#endif /*MOEOREFERENCEPOINTINDICATORBASEDFITNESSASSIGNMENT_H_*/
|
||||
Loading…
Add table
Add a link
Reference in a new issue