NAME

Algorithm::Evolutionary::Op::CanonicalGA - Canonical Genetic Algorithm, with any representation

SYNOPSIS

  # Straightforward instance, with all defaults (except for fitness function)
  my $algo = new Algorithm::Evolutionary::Op::CanonicalGA( $eval ); 

  #Define an easy single-generation algorithm with predefined mutation and crossover
  my $m = new Algorithm::Evolutionary::Op::Bitflip; #Changes a single bit
  my $c = new Algorithm::Evolutionary::Op::QuadXOver; #Classical 2-point crossover
  my $generation = new Algorithm::Evolutionary::Op::CanonicalGA( $rr, 0.2, [$m, $c] );

Base Class

Algorithm::Evolutionary::Op::Base

DESCRIPTION

The canonical classical genetic algorithm evolves a population of bitstrings until they reach the optimum fitness. It performs mutation on the bitstrings by flipping a single bit, crossover interchanges a part of the two parents.

The first operator should be unary (a la mutation) and the second binary (a la crossover) they will be applied in turn to couples of the population.

METHODS

new( $fitness[, $selection_rate][,$operators_ref_to_array] )

Creates an algorithm, with the usual operators. Includes a default mutation and crossover, in case they are not passed as parameters. The first element in the array ref should be an unary, and the second a binary operator.

apply( $population)

Applies a single generation of 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