160 lines
5.3 KiB
C++
160 lines
5.3 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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Caner Candan <caner.candan@thalesgroup.com>
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*/
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#include <eo>
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//#include <mo>
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#include <eoEvalFuncCounterBounder.h>
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#include <do/make_pop.h>
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#include <do/make_run.h>
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#include <do/make_continue.h>
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#include <do/make_checkpoint.h>
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#include <edo>
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#include "Rosenbrock.h"
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#include "Sphere.h"
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typedef eoReal<eoMinimizingFitness> RealVec;
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typedef edoNormalAdaptive< RealVec > Distrib;
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int main(int ac, char** av)
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{
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eoParser parser(ac, av);
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// Letters used by the following declarations:
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// a d i p t
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std::string section("Algorithm parameters");
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eoState state;
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// Instantiate all needed parameters for EDA algorithm
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//double selection_rate = parser.createParam((double)0.5, "selection_rate", "Selection Rate", 'R', section).value(); // R
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unsigned long max_eval = parser.getORcreateParam((unsigned long)0, "maxEval", "Maximum number of evaluations (0 = none)", 'E', "Stopping criterion").value(); // E
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unsigned int dim = parser.createParam((unsigned int)10, "dimension-size", "Dimension size", 'd', section).value(); // d
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double mu = dim / 2;
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edoNormalAdaptive<RealVec> distribution(dim);
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eoSelect< RealVec >* selector = new eoRankMuSelect< RealVec >( mu );
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state.storeFunctor(selector);
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edoEstimator< Distrib >* estimator = new edoEstimatorNormalAdaptive<RealVec>( distribution );
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state.storeFunctor(estimator);
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eoEvalFunc< RealVec >* plainEval = new Rosenbrock< RealVec >();
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state.storeFunctor(plainEval);
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eoEvalFuncCounterBounder< RealVec > eval(*plainEval, max_eval);
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eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
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state.storeFunctor(gen);
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eoInitFixedLength< RealVec >* init = new eoInitFixedLength< RealVec >( dim, *gen );
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state.storeFunctor(init);
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// (1) Population init and sampler
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// Generation of population from do_make_pop (creates parameters, manages persistance and so on...)
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// ... and creates the parameters: L P r S
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// this first sampler creates a uniform distribution independently from our distribution (it does not use edoUniform).
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eoPop< RealVec >& pop = do_make_pop(parser, state, *init);
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// (2) First evaluation before starting the research algorithm
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apply(eval, pop);
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// Prepare bounder class to set bounds of sampling.
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// This is used by edoSampler.
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edoBounder< RealVec >* bounder =
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new edoBounderRng< RealVec >( RealVec(dim, -5), RealVec(dim, 5), *gen); // FIXME do not use hard-coded bounds
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state.storeFunctor(bounder);
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// Prepare sampler class with a specific distribution
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edoSampler< Distrib >* sampler = new edoSamplerNormalAdaptive< RealVec >( *bounder );
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state.storeFunctor(sampler);
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// stopping criteria
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// ... and creates the parameter letters: C E g G s T
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eoContinue< RealVec >& eo_continue = do_make_continue(parser, state, eval);
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// population output
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eoCheckPoint< RealVec >& pop_continue = do_make_checkpoint(parser, state, eval, eo_continue);
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// distribution output
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edoDummyContinue< Distrib >* dummy_continue = new edoDummyContinue< Distrib >();
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state.storeFunctor(dummy_continue);
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edoCheckPoint< Distrib >* distribution_continue = new edoCheckPoint< Distrib >( *dummy_continue );
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state.storeFunctor(distribution_continue);
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// eoEPRemplacement causes the using of the current and previous
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// sample for sampling.
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eoReplacement< RealVec >* replacor = new eoEPReplacement< RealVec >(pop.size());
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state.storeFunctor(replacor);
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// Some stuff to display helper when we are using -h option
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if (parser.userNeedsHelp())
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{
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parser.printHelp(std::cout);
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exit(1);
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}
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// Help + Verbose routines
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make_verbose(parser);
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make_help(parser);
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eoPopLoopEval<RealVec> popEval( eval );
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// EDA algorithm configuration
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edoAlgo< Distrib >* algo = new edoAlgoAdaptive< Distrib >
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(distribution, popEval, *selector, *estimator, *sampler, *replacor,
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pop_continue, *distribution_continue );
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// Beginning of the algorithm call
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try {
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do_run(*algo, pop);
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} catch (eoEvalFuncCounterBounderException& e) {
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eo::log << eo::warnings << "warning: " << e.what() << std::endl;
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} catch (std::exception& e) {
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eo::log << eo::errors << "error: " << e.what() << std::endl;
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exit(EXIT_FAILURE);
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}
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return 0;
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}
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