271 lines
8.7 KiB
C++
271 lines
8.7 KiB
C++
/*
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The Evolving Distribution Objects framework (EDO) is a template-based,
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ANSI-C++ evolutionary computation library which helps you to write your
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own estimation of distribution algorithms.
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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 2.1 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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Copyright (C) 2010 Thales group
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*/
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/*
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Authors:
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Johann Dréo <johann.dreo@thalesgroup.com>
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Pierre Savéant <pierre.saveant@thalesgroup.com>
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*/
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#ifndef _edoAdaptiveAlgo_h
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#define _edoAdaptiveAlgo_h
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#include <eo>
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#include <utils/eoRNG.h>
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#include "edoAlgo.h"
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#include "edoEstimator.h"
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#include "edoModifierMass.h"
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#include "edoSampler.h"
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#include "edoContinue.h"
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//! edoEDA< D >
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// FIXME factoriser edoAdaptiveAlgo et edoEDA, la seule différence est la référence _distrib !
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template < typename EOD >
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class edoAdaptiveAlgo : public edoAlgo< EOD >
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{
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public:
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//! Alias for the type EOT
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typedef typename EOD::EOType EOType;
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//! Alias for the atom type
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typedef typename EOType::AtomType AtomType;
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//! Alias for the fitness
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typedef typename EOType::Fitness Fitness;
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public:
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/*!
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Takes algo operators, all are mandatory
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\param evaluation Evaluate a population
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\param selector Selection of the best candidate solutions in the population
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\param estimator Estimation of the distribution parameters
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\param sampler Generate feasible solutions using the distribution
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\param replacor Replace old solutions by new ones
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\param pop_continuator Stopping criterion based on the population features
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\param distribution_continuator Stopping criterion based on the distribution features
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*/
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edoAdaptiveAlgo(
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EOD & distrib,
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eoPopEvalFunc < EOType > & evaluator,
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eoSelect< EOType > & selector,
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edoEstimator< EOD > & estimator,
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edoSampler< EOD > & sampler,
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eoReplacement< EOType > & replacor,
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eoContinue< EOType > & pop_continuator,
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edoContinue< EOD > & distribution_continuator
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) :
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_dummy_distrib(),
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_distrib(distrib),
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_evaluator(evaluator),
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_selector(selector),
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_estimator(estimator),
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_sampler(sampler),
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_replacor(replacor),
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_pop_continuator(pop_continuator),
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_dummy_continue(),
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_distribution_continuator(distribution_continuator)
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{}
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/*!
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Without a distribution
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\param evaluation Evaluate a population
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\param selector Selection of the best candidate solutions in the population
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\param estimator Estimation of the distribution parameters
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\param sampler Generate feasible solutions using the distribution
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\param replacor Replace old solutions by new ones
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\param pop_continuator Stopping criterion based on the population features
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\param distribution_continuator Stopping criterion based on the distribution features
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*/
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edoAdaptiveAlgo(
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eoPopEvalFunc < EOType > & evaluator,
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eoSelect< EOType > & selector,
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edoEstimator< EOD > & estimator,
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edoSampler< EOD > & sampler,
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eoReplacement< EOType > & replacor,
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eoContinue< EOType > & pop_continuator,
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edoContinue< EOD > & distribution_continuator
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) :
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_dummy_distrib(),
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_distrib( _dummy_distrib ),
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_evaluator(evaluator),
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_selector(selector),
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_estimator(estimator),
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_sampler(sampler),
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_replacor(replacor),
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_pop_continuator(pop_continuator),
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_dummy_continue(),
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_distribution_continuator(distribution_continuator)
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{}
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//! constructor without an edoContinue
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/*!
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Takes algo operators, all are mandatory
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\param evaluation Evaluate a population
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\param selector Selection of the best candidate solutions in the population
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\param estimator Estimation of the distribution parameters
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\param sampler Generate feasible solutions using the distribution
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\param replacor Replace old solutions by new ones
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\param pop_continuator Stopping criterion based on the population features
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*/
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edoAdaptiveAlgo (
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EOD & distrib,
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eoPopEvalFunc < EOType > & evaluator,
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eoSelect< EOType > & selector,
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edoEstimator< EOD > & estimator,
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edoSampler< EOD > & sampler,
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eoReplacement< EOType > & replacor,
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eoContinue< EOType > & pop_continuator
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) :
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_dummy_distrib(),
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_distrib( distrib ),
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_evaluator(evaluator),
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_selector(selector),
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_estimator(estimator),
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_sampler(sampler),
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_replacor(replacor),
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_pop_continuator(pop_continuator),
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_dummy_continue(),
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_distribution_continuator( _dummy_continue )
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{}
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//! constructor without an edoContinue nor a distribution
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/*!
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Takes algo operators, all are mandatory
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\param evaluation Evaluate a population
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\param selector Selection of the best candidate solutions in the population
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\param estimator Estimation of the distribution parameters
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\param sampler Generate feasible solutions using the distribution
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\param replacor Replace old solutions by new ones
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\param pop_continuator Stopping criterion based on the population features
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*/
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edoAdaptiveAlgo (
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eoPopEvalFunc < EOType > & evaluator,
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eoSelect< EOType > & selector,
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edoEstimator< EOD > & estimator,
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edoSampler< EOD > & sampler,
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eoReplacement< EOType > & replacor,
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eoContinue< EOType > & pop_continuator
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) :
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_dummy_distrib(),
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_distrib( _dummy_distrib ),
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_evaluator(evaluator),
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_selector(selector),
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_estimator(estimator),
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_sampler(sampler),
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_replacor(replacor),
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_pop_continuator(pop_continuator),
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_dummy_continue(),
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_distribution_continuator( _dummy_continue )
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{}
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/** Covariance Matrix Adaptation Evolution Strategies
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*
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* \param pop the population of candidate solutions
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* \return void
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*/
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void operator ()(eoPop< EOType > & pop)
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{
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assert(pop.size() > 0);
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eoPop< EOType > current_pop;
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eoPop< EOType > selected_pop;
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// FIXME one must instanciate a first distrib here because there is no empty constructor, see if it is possible to instanciate Distributions without parameters
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_distrib = _estimator(pop);
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// Evaluating a first time the candidate solutions
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// The first pop is not supposed to be evaluated (@see eoPopLoopEval).
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// _evaluator( current_pop, pop );
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do {
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// (1) Selection of the best points in the population
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//selected_pop.clear(); // FIXME is it necessary to clear?
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_selector(pop, selected_pop);
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assert( selected_pop.size() > 0 );
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// TODO: utiliser selected_pop ou pop ???
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// (2) Estimation of the distribution parameters
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_distrib = _estimator(selected_pop);
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// (3) sampling
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// The sampler produces feasible solutions (@see edoSampler that
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// encapsulate an edoBounder)
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current_pop.clear();
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for( unsigned int i = 0; i < pop.size(); ++i ) {
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current_pop.push_back( _sampler(_distrib) );
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}
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// (4) Evaluate new solutions
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_evaluator( pop, current_pop );
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// (5) Replace old solutions by new ones
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_replacor(pop, current_pop); // e.g. copy current_pop in pop
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} while( _distribution_continuator( _distrib ) && _pop_continuator( pop ) );
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} // operator()
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protected:
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EOD _dummy_distrib;
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EOD & _distrib;
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//! A full evaluation function.
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eoPopEvalFunc<EOType> & _evaluator;
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//! A EOType selector
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eoSelect<EOType> & _selector;
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//! A EOType estimator. It is going to estimate distribution parameters.
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edoEstimator<EOD> & _estimator;
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//! A D sampler
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edoSampler<EOD> & _sampler;
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//! A EOType replacor
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eoReplacement<EOType> & _replacor;
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//! A EOType population continuator
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eoContinue<EOType> & _pop_continuator;
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//! A D continuator that always return true
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edoDummyContinue<EOD> _dummy_continue;
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//! A D continuator
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edoContinue<EOD> & _distribution_continuator;
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};
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#endif // !_edoAdaptiveAlgo_h
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