# FreeBSD Manual Pages

```Algorithm::EvolutionarUserpContributedoPerl::Evolutionary::Op::CanonicalGA(3)

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.

Algorithm::Evolutionary::Op::Easy
Algorithm::Evolutionary::Wheel
Algorithm::Evolutionary::Fitness::Base

Probably	you will also be able to find a
canonical-genetic-algorithm.pl example within this
bundle. Check it out	for usage examples