J. J. Merelo-Guervós
and 1 contributors

NAME

Algorithm::Evolutionary::Simple - Run a simple, canonical evolutionary algorithm in Perl

VERSION

This document describes Algorithm::Evolutionary::Simple version 0.1.2

SYNOPSIS

  use Algorithm::Evolutionary::Simple qw( random_chromosome max_ones max_ones_fast
                                        get_pool_roulette_wheel get_pool_binary_tournament produce_offspring single_generation);

  my @population;
  my %fitness_of;
  for (my $i = 0; $i < $number_of_strings; $i++) {
   $population[$i] = random_chromosome( $length);
   $fitness_of{$population[$i]} = max_ones( $population[$i] );
  }

  my @best;
  my $generations=0;
  do {
    my @pool;
    if ( $generations % 2 == 1 ) {
      get_pool_roulette_wheel( \@population, \%fitness_of, $number_of_strings );
    } else {
     get_pool_binary_tournament( \@population, \%fitness_of, $number_of_strings );
    }
    my @new_pop = produce_offspring( \@pool, $number_of_strings/2 );
    for my $p ( @new_pop ) {
        if ( !$fitness_of{$p} ) {
           $fitness_of{$p} = max_ones( $p );
        }
    }
    @best = rnkeytop { $fitness_of{$_} } $number_of_strings/2 => @population;
    @population = (@best, @new_pop);
    print "Best so far $best[0] with fitness $fitness_of{$best[0]}\n";
  } while ( ( $generations++ < $number_of_generations ) and ($fitness_of{$best[0]} != $length ));

DESCRIPTION

Assorted functions needed by an evolutionary algorithm, mainly for demos and simple clients.

INTERFACE

random_chromosome( $length )

Creates a binary chromosome, with uniform distribution of 0s and 1s, and returns it as a string.

max_ones( $string )

Classical function that returns the number of ones in a binary string.

max_ones_fast( $string )

Faster implementation of max_ones.

spin($wheel, $slots )

Mainly for internal use, $wheel has the normalized probability, and $slots the number of individuals to return.

single_generation( $population_arrayref, $fitness_of_hashref )

Applies all steps to arrive to a new generation, except evaluation. Keeps the two best for the next generation.

get_pool_roulette_wheel( $population_arrayref, $fitness_of_hashref, $how_many_I_need )

Obtains a pool of new chromosomes using fitness_proportional selection

get_pool_binary_tournament( $population_arrayref, $fitness_of_hashref, $how_many_I_need )

Obtains a pool of new chromosomes using binary tournament, a greedier method.

produce_offspring( $population_hashref, $how_many_I_need )

Uses mutation first and then crossover to obtain a new population

mutate( $string )

Bitflips a a single point in the binary string

crossover( $one_string, $another_string )

Applies two-point crossover to both strings, returning them changed

DIAGNOSTICS

Will complain if some argument is missing.

Algorithm::Evolutionary::Simple requires no configuration files or environment variables.

DEPENDENCIES

Sort::Key::Top for efficient sorting.

SEE ALSO

There are excellent evolutionary algorithm libraries out there; see for instance AI::Genetic::Pro

BUGS AND LIMITATIONS

It's intended for simplicity, not flexibility. If you want a full-featured evolutionary algorithm library, check Algorithm::Evolutionary

Please report any bugs or feature requests to bug-algorithm-evolutionary-simple@rt.cpan.org, or through the web interface at http://rt.cpan.org.

AUTHOR

JJ Merelo <jj@merelo.net>

LICENCE AND COPYRIGHT

Copyright (c) 2011, JJ Merelo <jj@merelo.net>. All rights reserved.

This module is free software; you can redistribute it and/or modify it under the GPL v3 licence.

DISCLAIMER OF WARRANTY

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