# FreeBSD Manual Pages

```Algorithm::EvolutionarUserpContributedhPe:lvDoctionary::Op::Canonical_GA_NN(3)

NAME
Algorithm::Evolutionary::Op::Canonical_GA_NN - Canonical	Genetic
Algorithm that does not	rank population

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

#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::Canonical_GA_NN( 0.2, [\$m, \$c] );

my \$generation = new Algorithm::Evolutionary::Op::Canonical_GA_NN( undef , [\$m, \$c] ); # Defaults to 0.4

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.

This is a fast version of the canonical GA, useful for large
populations, since it avoids the	expensive rank operation. Roulette
wheel selection,	still, is kind of slow.

METHODS
new(	[ \$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.	This binary operator must accept parameters by
reference, not value; it	will modify them. For the time being, just
Algorithm::Evolutionary::Op::QuadXOver works that way.

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.	This
population should be already evaluated. Returns a new population	for
next generation,	unsorted.

Algorithm::Evolutionary::Op::Easy
Algorithm::Evolutionary::Wheel
Algorithm::Evolutionary::Fitness::Base
Of course, Algorithm::Evolutionary::Fitness::CanonicalGA

You will	also find a
canonical-genetic-algorithm.pl example within this
bundle. Check it out	for usage examples