ga.rb

Genotype = -> size {
  (1..size).map{rand (0..1)} }

Population = -> individualSize, popSize {
  (0..popSize).map{Genotype[individualSize]} }

fitness = -> genotype {
  genotype.inject(:+) }

crossover = -> parents, position {
  [] << parents[0].take(position) + parents[1].drop(position) <<
      parents[1].take(position) + parents[0].drop(position) }

mutate = -> individual, position, genotypeDefinition {
  individual[position] = genotypeDefinition.curry(1)
  individual.flatten }

selection = -> population {
  (population + population).sort_by{ |n| -fitness[n]}.take(population.length) }

nextGeneration = -> population {
  population = selection[population]
  elite = population.take(1).flatten
  nextPop = []

  (0..population.length / 2 - 1)
      .each{crossover[
          [] << population[rand(population.length - 1)] << population[rand(population.length - 1)],
          rand(population[1].length - 1)]
      .map{|g| nextPop << g}}
  nextPop = nextPop
              .map{|n| (0..n.length - 1)
              .map{|p| rand(0.0..1.0) < 0.05 ? Genotype[1] : n[p] }.flatten }
  nextPop.sort_by { |n| -fitness[n]}.take(nextPop.length - 1) << elite }


pop = Population[50,100]
pop.sort_by { |n| -fitness[n]}.take(1)
=>[[1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1]]

(0..100).each{pop = nextGeneration[pop]}
pop.sort_by { |n| -fitness[n]}.take(1)}
=>[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]