##Introduction
General Genetic Algorithm for Ruby is a Ruby Genetic Algorithm that is very simple to use:
- 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)
end
def mutate
mutate_point = (rand * self.size).to_i
self[mutate_point] = 1
end
end
- 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)
- Call the evolve method as many times as you want and see the best evolution.
100.times { |i| ga.evolve }
p ga.best_fit[0]
##Install
- Execute:
gem install gga4r
- Add require in your code headers:
require "rubygems"
require "gga4r"
##Documentation
Documentation can be generated using rdoc tool under the source code with:
rdoc README lib
##Contributors
- Ben Prew https://github.com/benprew
- Rory O'Kane
- Sergio Espeja https://github.com/spejman
##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.