- Base Class
- SEE ALSO
Algorithm::Evolutionary::Op::Easy_MO - Multiobjecttive evolutionary algorithm, single generation, with variable operators
#Mutation and crossover. Default selection rate is 0.4 my $algo = new Algorithm::Evolutionary::Op::Easy_MO( $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::Crossover; #Classical 2-point crossover my $generation = new Algorithm::Evolutionary::Op::Easy_MO( $rr, 0.2, [$m, $c] );
"Easy" to use, single generation of an evolutionary algorithm. Takes an arrayref of operators as input, or defines bitflip-mutation and 2-point crossover as default. The
apply method applies a single iteration of the algorithm to the population it takes as input
Creates an algorithm that optimizes the handled fitness function and reference to an array of operators. If this reference is null, an array consisting of bitflip mutation and 2 point crossover is generated. Which, of course, might not what you need in case you don't have a binary chromosome. Take into account that in this case the fitness function should return a reference to array.
Sets the instance variables. Takes a ref-to-hash (for options), codehash (for fitness) and opshash (for operators)
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.
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: 2011/02/14 06:55:36 $ $Header: /media/Backup/Repos/opeal/opeal/Algorithm-Evolutionary/lib/Algorithm/Evolutionary/Op/Easy_MO.pm,v 3.6 2011/02/14 06:55:36 jmerelo Exp $ $Author: jmerelo $ $Revision: 3.6 $ $Name $