J. J. Merelo-Guervós > Algorithm-Evolutionary-0.67 > Algorithm::Evolutionary::Op::FullAlgorithm

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Module Version: 2.3   Source   Latest Release: Algorithm-Evolutionary-0.71

NAME ^

    Algorithm::Evolutionary::Op::FullAlgorithm - Skeleton class for a fully-featured evolutionary algorithm

SYNOPSIS ^

  my $easyEA = Algorithm::Evolutionary::Op::Base->fromXML( $ref->{$xml} );
  $easyEA->apply(\@pop ); 

  #Or using the constructor
  use Algorithm::Evolutionary::Op::Bitflip;
  my $m = new Algorithm::Evolutionary::Op::Bitflip; #Changes a single bit
  my $c = new Algorithm::Evolutionary::Op::Crossover; #Classical 2-point crossover
  my $replacementRate = 0.3; #Replacement rate
  use Algorithm::Evolutionary::Op::RouletteWheel;
  my $popSize = 20;
  my $selector = new Algorithm::Evolutionary::Op::RouletteWheel $popSize; #One of the possible selectors
  use Algorithm::Evolutionary::Op::GeneralGeneration;
  my $onemax = sub { 
    my $indi = shift;
    my $total = 0;
    my $len = $indi->length();
    my $i = 0;
    while ($i < $len ) {
      $total += substr($indi->{'_str'}, $i, 1);
      $i++;
    }
    return $total;
  };
  my $generation = 
    new Algorithm::Evolutionary::Op::GeneralGeneration( $onemax, $selector, [$m, $c], $replacementRate );
  use Algorithm::Evolutionary::Op::GenerationalTerm;
  my $g100 = new Algorithm::Evolutionary::Op::GenerationalTerm 10;
  use Algorithm::Evolutionary::Op::FullAlgorithm;
  my $f = new Algorithm::Evolutionary::Op::FullAlgorithm $generation, $g100;
  print $f->asXML();

Base Class ^

Algorithm::Evolutionary::Op::Base

DESCRIPTION ^

Class Easy-to-use full evolutionary algoritm.It takes a single-generarion algorithm, and mixes it with a termination condition to create a full algorithm. Includes a sensible default (100-generation generational algorithm) if it is issued only an object of class Algorithm::Evolutionary::Op::GeneralGeneration.

new( $single_generation[, $termination_test] [, $verboseness] )

Takes an already created algorithm and a terminator, and creates an object

set( $hashref, $codehash, $opshash )

Sets the instance variables. Takes hashes to the different options of the algorithm: parameters, fitness functions and operators

apply( $reference_to_population_array )

Applies the algorithm to the population; checks that it receives a ref-to-array as input, croaks if it does not. Returns a sorted, culled, evaluated population for next generation.

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: 2009/02/07 18:31:28 $ 
  $Header: /cvsroot/opeal/Algorithm-Evolutionary/lib/Algorithm/Evolutionary/Op/FullAlgorithm.pm,v 2.3 2009/02/07 18:31:28 jmerelo Exp $ 
  $Author: jmerelo $