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trunk/paradiseo-moeo/src/old/eoIBEA.h
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trunk/paradiseo-moeo/src/old/eoIBEA.h
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// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
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// "eoIBEA.h"
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// (c) OPAC Team, LIFL, June 2006
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/* This library is free software; you can redistribute it and/or
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modify it under the terms of the GNU Lesser General Public
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License as published by the Free Software Foundation; either
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version 2 of the License, or (at your option) any later version.
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This library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public
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License along with this library; if not, write to the Free Software
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Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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Contact: Arnaud.Liefooghe@lifl.fr
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*/
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#ifndef _eoIBEASorting_h
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#define _eoIBEASorting_h
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#include <math.h>
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#include <list>
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#include <eoPop.h>
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#include <eoPerf2Worth.h>
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#include "eoBinaryQualityIndicator.h"
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/**
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* Functor
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* The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm)
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*/
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template < class EOT, class Fitness > class eoIBEA:public eoPerf2WorthCached < EOT,
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double >
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{
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public:
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/** values */
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using eoIBEA < EOT, Fitness >::value;
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eoIBEA (eoBinaryQualityIndicator < Fitness > *_I)
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{
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I = _I;
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}
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/**
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* mapping
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* @param const eoPop<EOT>& _pop the population
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*/
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void calculate_worths (const eoPop < EOT > &_pop)
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{
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/* resizing the worths beforehand */
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value ().resize (_pop.size ());
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/* computation and setting of the bounds for each objective */
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setBounds (_pop);
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/* computation of the fitness for each individual */
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fitnesses (_pop);
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// higher is better, so invert the value
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double max = *std::max_element (value ().begin (), value ().end ());
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for (unsigned i = 0; i < value ().size (); i++)
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value ()[i] = max - value ()[i];
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}
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protected:
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/** binary quality indicator to use in the selection process */
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eoBinaryQualityIndicator < Fitness > *I;
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virtual void setBounds (const eoPop < EOT > &_pop) = 0;
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virtual void fitnesses (const eoPop < EOT > &_pop) = 0;
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};
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/**
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* Functor
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* The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) without uncertainty
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* Adapted from the Zitzler and Künzli paper "Indicator-Based Selection in Multiobjective Search" (2004)
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* Of course, Fitness needs to be an eoParetoFitness object
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*/
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template < class EOT, class Fitness = typename EOT::Fitness > class eoIBEASorting:public eoIBEA < EOT,
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Fitness
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>
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{
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public:
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/**
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* constructor
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* @param eoBinaryQualityIndicator<EOT>* _I the binary quality indicator to use in the selection process
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* @param double _kappa scaling factor kappa
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*/
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eoIBEASorting (eoBinaryQualityIndicator < Fitness > *_I,
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const double _kappa):
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eoIBEA <
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EOT,
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Fitness > (_I)
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{
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kappa = _kappa;
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}
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private:
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/** quality indicator */
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using eoIBEASorting < EOT, Fitness >::I;
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/** values */
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using
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eoIBEA <
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EOT,
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Fitness >::value;
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/** scaling factor kappa */
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double
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kappa;
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/**
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* computation and setting of the bounds for each objective
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* @param const eoPop<EOT>& _pop the population
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*/
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void
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setBounds (const eoPop < EOT > &_pop)
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{
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typedef typename
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EOT::Fitness::fitness_traits
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traits;
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double
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min,
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max;
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for (unsigned i = 0; i < traits::nObjectives (); i++)
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{
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min = _pop[0].fitness ()[i];
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max = _pop[0].fitness ()[i];
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for (unsigned j = 1; j < _pop.size (); j++)
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{
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min = std::min (min, _pop[j].fitness ()[i]);
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max = std::max (max, _pop[j].fitness ()[i]);
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}
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// setting of the bounds for the objective i
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I->setBounds (i, min, max);
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}
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}
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/**
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* computation and setting of the fitness for each individual of the population
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* @param const eoPop<EOT>& _pop the population
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*/
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void
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fitnesses (const eoPop < EOT > &_pop)
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{
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// reprsentation of the fitness components
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std::vector < std::vector < double > >
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fitComponents (_pop.size (), _pop.size ());
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// the maximum absolute indicator value
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double
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maxAbsoluteIndicatorValue = 0;
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// computation of the indicator values and of the maximum absolute indicator value
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for (unsigned i = 0; i < _pop.size (); i++)
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for (unsigned j = 0; j < _pop.size (); j++)
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if (i != j)
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{
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fitComponents[i][j] =
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(*I) (_pop[i].fitness (), _pop[j].fitness ());
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maxAbsoluteIndicatorValue =
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std::max (maxAbsoluteIndicatorValue,
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fabs (fitComponents[i][j]));
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}
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// computation of the fitness components for each pair of individuals
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// if maxAbsoluteIndicatorValue==0, every individuals have the same fitness values for all objectives (already = 0)
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if (maxAbsoluteIndicatorValue != 0)
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for (unsigned i = 0; i < _pop.size (); i++)
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for (unsigned j = 0; j < _pop.size (); j++)
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if (i != j)
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fitComponents[i][j] =
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exp (-fitComponents[i][j] /
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(maxAbsoluteIndicatorValue * kappa));
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// computation of the fitness for each individual
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for (unsigned i = 0; i < _pop.size (); i++)
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{
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value ()[i] = 0;
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for (unsigned j = 0; j < _pop.size (); j++)
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if (i != j)
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value ()[i] += fitComponents[j][i];
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}
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}
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};
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/**
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* Functor
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* The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) under uncertainty
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* Adapted from the Basseur and Zitzler paper "Handling Uncertainty in Indicator-Based Multiobjective Optimization" (2006)
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* Of course, the fitness of an individual needs to be an eoStochasticParetoFitness object
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*/
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template < class EOT, class FitnessEval = typename EOT::Fitness::FitnessEval > class eoIBEAStochSorting:public eoIBEA < EOT,
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FitnessEval
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>
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{
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public:
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/**
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* constructor
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* @param eoBinaryQualityIndicator<EOT>* _I the binary quality indicator to use in the selection process
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*/
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eoIBEAStochSorting (eoBinaryQualityIndicator < FitnessEval > *_I):eoIBEA < EOT,
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FitnessEval >
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(_I)
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{
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}
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private:
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/** quality indicator */
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using eoIBEAStochSorting < EOT, FitnessEval >::I;
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/** values */
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using
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eoIBEAStochSorting <
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EOT,
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FitnessEval >::value;
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/**
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* approximated zero value
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*/
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static double
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zero ()
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{
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return 1e-7;
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}
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/**
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* computation and setting of the bounds for each objective
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* @param const eoPop<EOT>& _pop the population
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*/
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void
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setBounds (const eoPop < EOT > &_pop)
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{
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typedef typename
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EOT::Fitness::FitnessTraits
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traits;
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double
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min,
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max;
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for (unsigned i = 0; i < traits::nObjectives (); i++)
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{
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min = _pop[0].fitness ().minimum (i);
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max = _pop[0].fitness ().maximum (i);
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for (unsigned j = 1; j < _pop.size (); j++)
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{
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min = std::min (min, _pop[j].fitness ().minimum (i));
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max = std::max (max, _pop[j].fitness ().maximum (i));
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}
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// setting of the bounds for the ith objective
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I->setBounds (i, min, max);
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}
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}
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/**
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* computation and setting of the fitness for each individual of the population
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* @param const eoPop<EOT>& _pop the population
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*/
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void
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fitnesses (const eoPop < EOT > &_pop)
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{
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typedef typename
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EOT::Fitness::FitnessTraits
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traits;
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unsigned
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nEval = traits::nEvaluations ();
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unsigned
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index;
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double
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eiv,
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p,
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sumP,
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iValue;
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std::list < std::pair < double, unsigned > > l;
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std::vector < unsigned >
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n (_pop.size ());
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for (unsigned ind = 0; ind < _pop.size (); ind++)
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{
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value ()[ind] = 0.0; // fitness value for the individual ind
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for (unsigned eval = 0; eval < nEval; eval++)
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{
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// I-values computation for the evaluation eval of the individual ind
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l.clear ();
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for (unsigned i = 0; i < _pop.size (); i++)
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{
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if (i != ind)
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{
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for (unsigned j = 0; j < nEval; j++)
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{
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std::pair < double, unsigned >
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pa;
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// I-value
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pa.first =
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(*I) (_pop[ind].fitness ()[eval],
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_pop[i].fitness ()[j]);
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// index of the individual
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pa.second = i;
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// append this to the list
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l.push_back (pa);
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}
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}
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}
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// sorting of the I-values (in decreasing order)
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l.sort ();
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// computation of the Expected Indicator Value (eiv) for the evaluation eval of the individual ind
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eiv = 0.0;
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n.assign (n.size (), 0); // n[i]==0 for all i
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sumP = 0.0;
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while (((1 - sumP) > zero ()) && (l.size () > 0))
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{
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// we use the last element of the list (the greatest one)
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iValue = l.back ().first;
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index = l.back ().second;
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// computation of the probability to appear
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p = (1.0 / (nEval - n[index])) * (1.0 - sumP);
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// eiv update
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eiv += p * iValue;
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// update of the number of elements for individual index
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n[index]++;
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// removing the last element of the list
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l.pop_back ();
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// sum of p update
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sumP += p;
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}
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value ()[ind] += eiv / nEval;
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}
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}
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}
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};
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/**
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* Functor
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* The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) under uncertainty using averaged values for each objective
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* Follow the idea presented in the Deb & Gupta paper "Searching for Robust Pareto-Optimal Solutions in Multi-Objective Optimization", 2005
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* Of course, the fitness of an individual needs to be an eoStochasticParetoFitness object
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*/
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template < class EOT, class FitnessEval = typename EOT::Fitness::FitnessEval > class eoIBEAAvgSorting:public eoIBEA < EOT,
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FitnessEval
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>
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{
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public:
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/**
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* constructor
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* @param eoBinaryQualityIndicator<EOT>* _I the binary quality indicator to use in the selection process
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* @param double _kappa scaling factor kappa
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*/
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eoIBEAAvgSorting (eoBinaryQualityIndicator < FitnessEval > *_I,
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const double _kappa):
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eoIBEA <
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EOT,
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FitnessEval > (_I)
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{
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kappa = _kappa;
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}
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private:
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/** quality indicator */
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using eoIBEAAvgSorting < EOT, FitnessEval >::I;
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/** values */
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using
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eoIBEAAvgSorting <
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EOT,
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FitnessEval >::value;
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/** scaling factor kappa */
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double
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kappa;
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/**
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* computation and setting of the bounds for each objective
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* @param const eoPop<EOT>& _pop the population
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*/
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void
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setBounds (const eoPop < EOT > &_pop)
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{
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typedef typename
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EOT::Fitness::FitnessTraits
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traits;
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double
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min,
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max;
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for (unsigned i = 0; i < traits::nObjectives (); i++)
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{
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min = _pop[0].fitness ().averagedParetoFitnessObject ()[i];
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max = _pop[0].fitness ().averagedParetoFitnessObject ()[i];
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for (unsigned j = 1; j < _pop.size (); j++)
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{
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min =
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std::min (min,
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_pop[j].fitness ().averagedParetoFitnessObject ()[i]);
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max =
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std::max (max,
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_pop[j].fitness ().averagedParetoFitnessObject ()[i]);
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}
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// setting of the bounds for the objective i
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I->setBounds (i, min, max);
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}
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}
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/**
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* computation and setting of the fitness for each individual of the population
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* @param const eoPop<EOT>& _pop the population
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*/
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void
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fitnesses (const eoPop < EOT > &_pop)
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{
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// reprsentation of the fitness components
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std::vector < std::vector < double > >
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fitComponents (_pop.size (), _pop.size ());
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// the maximum absolute indicator value
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double
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maxAbsoluteIndicatorValue = 0;
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// computation of the indicator values and of the maximum absolute indicator value
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for (unsigned i = 0; i < _pop.size (); i++)
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for (unsigned j = 0; j < _pop.size (); j++)
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if (i != j)
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{
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fitComponents[i][j] =
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(*I) (_pop[i].fitness ().averagedParetoFitnessObject (),
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_pop[j].fitness ().averagedParetoFitnessObject ());
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maxAbsoluteIndicatorValue =
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std::max (maxAbsoluteIndicatorValue,
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fabs (fitComponents[i][j]));
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}
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// computation of the fitness components for each pair of individuals
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// if maxAbsoluteIndicatorValue==0, every individuals have the same fitness values for all objectives (already = 0)
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if (maxAbsoluteIndicatorValue != 0)
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for (unsigned i = 0; i < _pop.size (); i++)
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for (unsigned j = 0; j < _pop.size (); j++)
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if (i != j)
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fitComponents[i][j] =
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exp (-fitComponents[i][j] /
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(maxAbsoluteIndicatorValue * kappa));
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// computation of the fitness for each individual
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for (unsigned i = 0; i < _pop.size (); i++)
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{
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value ()[i] = 0;
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for (unsigned j = 0; j < _pop.size (); j++)
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if (i != j)
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value ()[i] += fitComponents[j][i];
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}
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}
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};
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#endif
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