#!/usr/bin/perl =head1 NAME mmdp.pl - Massively multimodal deceptive problem =head1 SYNOPSIS prompt% ./mmdp.pl <population> <number of generations> or prompt% perl mmdp.pl <population> <number of generations> Shows the values of the two floating-point components of the chromosome and finally the best value and fitness reached, which should be as close to 1 as possible. =head1 DESCRIPTION A simple example of how to run an Evolutionary algorithm based on Algorithm::Evolutionary. Tries to find the max of the bidimensional Tide , and outputs the x and y coordinates, along with fitness. Best fitness is close to 1. Around 50 generations should be enough, but default is population and number of generations equal to 100. =cut use warnings; use strict; use Time::HiRes qw( gettimeofday tv_interval); use lib qw(lib ../lib); use Algorithm::Evolutionary::Individual::BitString; use Algorithm::Evolutionary::Op::Easy; use Algorithm::Evolutionary::Op::Mutation; use Algorithm::Evolutionary::Op::Crossover; use Algorithm::Evolutionary::Fitness::MMDP; #----------------------------------------------------------# my $length = shift || 20; my $popSize = shift || 1024; #Population size my $numGens = shift || 1000; #Max number of generations my $selection_rate = shift || 0.1; #----------------------------------------------------------# #Initial population my @pop; #Creamos $popSize individuos my $bits = $length*6; # 6 is the block size for ( 0..$popSize ) { my $indi = Algorithm::Evolutionary::Individual::BitString->new( $bits ); push( @pop, $indi ); } #----------------------------------------------------------# # Variation operators my $m = Algorithm::Evolutionary::Op::Mutation->new( 0.1 ); my $c = Algorithm::Evolutionary::Op::Crossover->new(2); # Fitness function my $mmdp = new Algorithm::Evolutionary::Fitness::MMDP; #----------------------------------------------------------# #Usamos estos operadores para definir una generación del algoritmo. Lo cual # no es realmente necesario ya que este algoritmo define ambos operadores por # defecto. Los parámetros son la función de fitness, la tasa de selección y los # operadores de variación. #my $fitness = sub { $mmdp->apply(@_) }; my $generation = Algorithm::Evolutionary::Op::Easy->new( $mmdp , $selection_rate , [$m, $c] ) ; #Time my $inicioTiempo = [gettimeofday()]; #----------------------------------------------------------# for ( @pop ) { if ( !defined $_->Fitness() ) { $_->evaluate( $mmdp ); } } my $contador=0; do { $generation->apply( \@pop ); print "$contador : ", $pop[0]->asString(), "\n" ; $contador++; } while( ($contador < $numGens) && ($pop[0]->Fitness() < $length)); #----------------------------------------------------------# #leemos el mejor resultado #Mostramos los resultados obtenidos print "El mejor es:\n\t ",$pop[0]->asString()," Fitness: ",$pop[0]->Fitness(),"\n"; print "\n\n\tTime: ", tv_interval( $inicioTiempo ) , "\n"; print "\n\tEvaluaciones: ", $mmdp->evaluations(), "\n"; =head1 AUTHOR Contributed by Pedro Castillo Valdivieso, modified by J. J. Merelo =cut =head1 Copyright This file is released under the GPL. See the LICENSE file included in this distribution, or go to http://www.fsf.org/licenses/gpl.txt CVS Info: $Date: 2009/07/24 08:46:58 $ $Header: /cvsroot/opeal/Algorithm-Evolutionary/examples/mmdp.pl,v 3.0 2009/07/24 08:46:58 jmerelo Exp $ $Author: jmerelo $ $Revision: 3.0 $ $Name $ =cut