view release on metacpan or  search on metacpan

scripts/tide_float.pl  view on Meta::CPAN

#!/usr/bin/perl

=head1 NAME

tide_float.pl - Optimization of the tide function using A::E

=head1 SYNOPSIS

  prompt% ./tide_float.pl <population> <number of generations>

or

  prompt% perl tide_float.pl <population> <number of generations>

will show 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 qw( Individual::Vector Op::Easy 
				Op::GaussianMutation Op::VectorCrossover);

#----------------------------------------------------------#
my $popSize = shift || 100; #Population size
my $numGens = shift || 100; #Max number of generations
my $dimension = shift || 2; #Vector dimension

#----------------------------------------------------------#
#Fitness function will be Tide
my $funcionMarea = sub {
  my $indi = shift;
  my ( $x, $y ) = @{$indi->{_array}};
  my $sqrt = sqrt( $x*$x+$y*$y);

  if( !$sqrt ){ return 1; }
  return sin( $sqrt )/$sqrt;
};

#----------------------------------------------------------#
#Initial population
my @pop;
#Creamos $popSize individuos
for ( 0..$popSize ) {
  my $indi = Algorithm::Evolutionary::Individual::Vector->new( $dimension );
  push( @pop, $indi );
}

#----------------------------------------------------------#
# Variation operators
my $m = Algorithm::Evolutionary::Op::GaussianMutation->new( 0, 0.1 );
my $c = Algorithm::Evolutionary::Op::VectorCrossover->new( $dimension );

#----------------------------------------------------------#
#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 $generation = Algorithm::Evolutionary::Op::Easy->new( $funcionMarea , 0.2 , [$m, $c] ) ;

#Time