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Algorithm::Evolutionary::Simple cpan:JMERELO last updated on 2019-02-19


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


use Algorithm::Evolutionary::Simple;


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.


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.

sub evaluate-nocache( :@population, :$evaluator --> Mix )

Evaluates the population, returning a Mix, but does not use a cache. Intended mainly for concurrent operation.

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

Returns $need elements with probability proportional to its weight, which is fitness in this case.

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.

multi sub generation( :@population, :%fitness-of, :$evaluator, :$population-size = $population.elems, Bool :$auto-t --> Mix )

Single generation of an evolutionary algorithm. The initial Mix has to be evaluated before entering here using the evaluate function. Will use auto-threading if $auto-t is True.

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

Mixes the two populations, returning a single one of the indicated size and with type Mix.

sub pack-individual( @individual --> Int )

Packs the individual in a single Int. The invidual must be binary, and the maximum length is 64.

sub unpack-individual( Int $packed, UInt $bits --> Array(Seq))

Unpacks the individual that has been packed previously using pack-individual

sub pack-population( @population --> Buf)

Packs a population, producing a buffer which can be sent to a channel or stored in a compact form.

sub unpack-population( Buf $buffer, UInt $bits --> Array )

Unpacks the population that has been packed using pack-population

multi sub frequencies( $population)

$population can be an array or a Mix, in which case the keys are extracted. This returns the per-bit (or gene) frequency of one (or True) for the population.

multi sub frequencies-best( $population, $proportion = 2)

$population is a Mix, in which case the keys are extracted. This returns the per-bit (or gene) frequency of one (or True) for the population of the best part of the population; the size of the population will be divided by the $proportion variable.

sub generate-by-frequencies( $population-size, @frequencies )

Generates a population of that size with every gene according to the indicated frequency.

sub crossover-frequencies( @frequencies, @frequencies-prime --> Array )

Generates a new array with random elements of the two arrays that are used as arguments.


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, but it's not simply a port, since most of the code is completely different.


JJ Merelo


Copyright 2018, 2019 JJ Merelo

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