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use adaptive operators to implement CMA-ES

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Johann Dreo 2012-07-12 11:25:07 +02:00
commit 546f24295e
1 changed files with 36 additions and 28 deletions
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64
edo/application/cmaes/main.cpp
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@ -41,8 +41,8 @@ Authors:
#include "Sphere.h"
typedef eoReal<eoMinimizingFitness> EOT;
typedef edoNormalMulti< EOT > Distrib;
typedef eoReal<eoMinimizingFitness> RealVec;
typedef edoNormalAdaptive< RealVec > Distrib;
int main(int ac, char** av)
@ -59,24 +59,32 @@ int main(int ac, char** av)
// Instantiate all needed parameters for EDA algorithm
double selection_rate = parser.createParam((double)0.5, "selection_rate", "Selection Rate", 'R', section).value(); // R
eoSelect< EOT >* selector = new eoDetSelect< EOT >( selection_rate );
unsigned long max_eval = parser.getORcreateParam((unsigned long)0, "maxEval", "Maximum number of evaluations (0 = none)", 'E', "Stopping criterion").value(); // E
unsigned int dim = parser.createParam((unsigned int)10, "dimension-size", "Dimension size", 'd', section).value(); // d
double mu = dim / 2;
edoNormalAdaptive<RealVec> distribution(dim);
eoSelect< RealVec >* selector = new eoDetSelect< RealVec >( selection_rate );
state.storeFunctor(selector);
edoEstimator< Distrib >* estimator = new edoEstimatorNormalMulti< EOT >();
edoEstimator< Distrib >* estimator = new edoEstimatorNormalAdaptive<RealVec>( distribution, mu );
state.storeFunctor(estimator);
eoEvalFunc< EOT >* plainEval = new Rosenbrock< EOT >();
eoEvalFunc< RealVec >* plainEval = new Rosenbrock< RealVec >();
state.storeFunctor(plainEval);
unsigned long max_eval = parser.getORcreateParam((unsigned long)0, "maxEval", "Maximum number of evaluations (0 = none)", 'E', "Stopping criterion").value(); // E
eoEvalFuncCounterBounder< EOT > eval(*plainEval, max_eval);
eoEvalFuncCounterBounder< RealVec > eval(*plainEval, max_eval);
eoRndGenerator< double >* gen = new eoUniformGenerator< double >(-5, 5);
state.storeFunctor(gen);
unsigned int dimension_size = parser.createParam((unsigned int)10, "dimension-size", "Dimension size", 'd', section).value(); // d
eoInitFixedLength< EOT >* init = new eoInitFixedLength< EOT >( dimension_size, *gen );
eoInitFixedLength< RealVec >* init = new eoInitFixedLength< RealVec >( dim, *gen );
state.storeFunctor(init);
@ -84,28 +92,28 @@ int main(int ac, char** av)
// Generation of population from do_make_pop (creates parameters, manages persistance and so on...)
// ... and creates the parameters: L P r S
// this first sampler creates a uniform distribution independently from our distribution (it does not use edoUniform).
eoPop< EOT >& pop = do_make_pop(parser, state, *init);
eoPop< RealVec >& pop = do_make_pop(parser, state, *init);
// (2) First evaluation before starting the research algorithm
apply(eval, pop);
// Prepare bounder class to set bounds of sampling.
// This is used by edoSampler.
edoBounder< EOT >* bounder =
new edoBounderRng< EOT >( EOT(pop[0].size(), -5), EOT(pop[0].size(), 5), *gen); // FIXME do not use hard-coded bounds
edoBounder< RealVec >* bounder =
new edoBounderRng< RealVec >( RealVec(dim, -5), RealVec(dim, 5), *gen); // FIXME do not use hard-coded bounds
state.storeFunctor(bounder);
// Prepare sampler class with a specific distribution
edoSampler< Distrib >* sampler = new edoSamplerNormalMulti< EOT >( *bounder );
edoSampler< Distrib >* sampler = new edoSamplerNormalAdaptive< RealVec >( *bounder );
state.storeFunctor(sampler);
// stopping criteria
// ... and creates the parameter letters: C E g G s T
eoContinue< EOT >& eo_continue = do_make_continue(parser, state, eval);
eoContinue< RealVec >& eo_continue = do_make_continue(parser, state, eval);
// population output
eoCheckPoint< EOT >& pop_continue = do_make_checkpoint(parser, state, eval, eo_continue);
eoCheckPoint< RealVec >& pop_continue = do_make_checkpoint(parser, state, eval, eo_continue);
// distribution output
edoDummyContinue< Distrib >* dummy_continue = new edoDummyContinue< Distrib >();
state.storeFunctor(dummy_continue);
@ -115,9 +123,9 @@ int main(int ac, char** av)
// eoEPRemplacement causes the using of the current and previous
// sample for sampling.
eoReplacement< EOT >* replacor = new eoEPReplacement< EOT >(pop.size());
eoReplacement< RealVec >* replacor = new eoEPReplacement< RealVec >(pop.size());
state.storeFunctor(replacor);
// Some stuff to display helper when we are using -h option
if (parser.userNeedsHelp())
{
@ -133,7 +141,7 @@ int main(int ac, char** av)
//
// FIXME: theses objects are instanciated there in order to avoid a folder
// removing as edoFileSnapshot does within ctor.
edoPopStat< EOT >* popStat = new edoPopStat<EOT>;
edoPopStat< RealVec >* popStat = new edoPopStat<RealVec>;
state.storeFunctor(popStat);
pop_continue.add(*popStat);
@ -143,10 +151,10 @@ int main(int ac, char** av)
pop_continue.add(*fileSnapshot);
// distribution output (after helper)
edoDistribStat< Distrib >* distrib_stat = new edoStatNormalMulti< EOT >();
state.storeFunctor(distrib_stat);
// edoDistribStat< Distrib >* distrib_stat = new edoStatNormalAdaptive< RealVec >();
// state.storeFunctor(distrib_stat);
distribution_continue->add( *distrib_stat );
// distribution_continue->add( *distrib_stat );
// eoMonitor* stdout_monitor = new eoStdoutMonitor();
// state.storeFunctor(stdout_monitor);
@ -155,14 +163,14 @@ int main(int ac, char** av)
eoFileMonitor* file_monitor = new eoFileMonitor("eda_distribution_bounds.txt");
state.storeFunctor(file_monitor);
file_monitor->add(*distrib_stat);
// file_monitor->add(*distrib_stat);
distribution_continue->add( *file_monitor );
eoPopLoopEval<EOT> popEval( eval );
eoPopLoopEval<RealVec> popEval( eval );
// EDA algorithm configuration
edoAlgo< Distrib >* algo = new edoEDA< Distrib >
(popEval, *selector, *estimator, *sampler, *replacor,
edoAlgo< Distrib >* algo = new edoCMAES< Distrib >
(distribution, popEval, *selector, *estimator, *sampler, *replacor,
pop_continue, *distribution_continue );