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Algorithm::Evolutionary::Simple cpan:JMERELO last updated on 2018-07-23

NAME

Algorithm::Evolutionary::Simple - A simple evolutionary algorithm

SYNOPSIS

use Algorithm::Evolutionary::Simple;

DESCRIPTION

Algorithm::Evolutionary::Simple is a module for writing simple and quasi-canonical evolutionary algorithms in Perl 6. It uses binary representation, integer fitness (which is needed for the kind of data structure we are using) and a single fitness function.

It is intended mainly for demo purposes. In the future, more versions will be available.

It uses a fitness cache for storing and not reevaluating, so take care of memory bloat.

METHODS

initialize( UInt :$size, UInt :$genome-length --> Array ) is export

Creates the initial population of binary chromosomes with the indicated length; returns an array.

random-chromosome( UInt $length --> List )

Generates a random chromosome of indicated length. Returns a Seq of Bools

max-ones( @chromosome --> Int )

Returns the number of trues (or ones) in the chromosome.

royal-road( @chromosome )

That's a bumpy road, returns 1 for each block of 4 which has the same true or false value.

multi evaluate( :@population, :%fitness-of, :$evaluator, :$auto-t = False --> Mix ) is export

Evaluates the chromosomes, storing values in the fitness cache. If auto-t is set to 'True', uses autothreading for faster operation (if needed). In absence of that parameter, defaults to sequential.

get-pool-roulette-wheel( Mix $population, UInt $need = $population.elems ) is export

Roulette wheel selection.

mutation( @chromosome is copy --> Array )

Returns the chromosome with a random bit flipped.

crossover ( @chromosome1 is copy, @chromosome2 is copy ) returns List

Returns two chromosomes, with parts of it crossed over. Generally you will want to do crossover first, then mutation.

produce-offspring( @pool, $size = @pool.elems --> Seq ) is export

Produces offspring from an array that contains the reproductive pool; it returns a Seq.

best-fitness( $population )

Returns the fitness of the first element. Mainly useful to check if the algorithm is finished.

generation( :@population, :%fitness-of, :$evaluator, :$population-size = $population.elems --> Mix )

Single generation of an evolutionary algorithm. The initial Mix has to be evaluated before entering here using the evaluate function.

mix( $population1, $population2, $size --> Mix ) is export

Mixes the two populations, returning a single one of the indicated size

SEE ALSO

There is a very interesting implementation of an evolutionary algorithm in Algorithm::Genetic. Check it out.

This is also a port of Algorithm::Evolutionary::Simple to Perl6, which has a few more goodies.

AUTHOR

JJ Merelo jjmerelo@gmail.com

COPYRIGHT AND LICENSE

Copyright 2018 JJ Merelo

This library is free software; you can redistribute it and/or modify it under the Artistic License 2.0.