=README for gga4r


==Introduction

General Genetic Algorithm for ruby is a Ruby Genetic Algorithm so simple to use:

1) Take a class to evolve it and define fitness, recombine and mutate methods.
class StringPopulation < Array
def fitness
self.select { |pos| pos == 1 }.size.to_f / self.size.to_f
end

def recombine(c2)
cross_point = (rand * c2.size).to_i
c1_a, c1_b = self.separate(cross_point)
c2_a, c2_b = c2.separate(cross_point)
[StringPopulation.new(c1_a + c2_b), StringPopulation.new(c2_a + c1_b)]
end

def mutate
mutate_point = (rand * self.size).to_i
self[mutate_point] = 1
end
end

2) Create a GeneticAlgorithm object with the population.
def create_population_with_fit_all_1s(s_long = 10, num = 10)
population = []
num.times do
chromosome = StringPopulation.new(Array.new(s_long).collect { (rand > 0.2) ? 0:1 })
population << chromosome
end
population
end

ga = GeneticAlgorithm.new(create_population_with_fit_all_1s)

3) Call evolve method as many times as you want and see the best evolution.
100.times { |i| ga.evolve }
p ga.best_fit[0]

==Install

1) Execute:
gem install gga4r

2) Add require in your code headers:
require "rubygems"
require "gga4r"


==Attention

Please note that Gga4r adds shuffle!, each_pair and separate methods to the Array class.

==Documentation

Documentation can be generated using rdoc tool under the source code with:

rdoc README lib


==Copying

This work is developed by Sergio Espeja ( www.upf.edu/pdi/iula/sergio.espeja, sergio.espeja at gmail.com )
mainly in Institut Universitari de Lingüística Aplicada of Universitat Pompeu Fabra ( www.iula.upf.es ),
and also in bee.com.es ( bee.com.es ).

It is free software, and may be redistributed under GPL license.