README for gga4r¶ ↑

Introduction¶ ↑

General Genetic Algorithm for Ruby is a Ruby Genetic Algorithm that is very 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 = 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 the evolve method as many times as you want and see the best evolution. 100.times { |i| ga.evolve } p ga.best_fit

Install¶ ↑

1) Execute: gem install gga4r

2) Add require in your code headers: require “rubygems” require “gga4r”

Attention¶ ↑

Please note that Gga4r adds shuffle, 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 ).