ga system written in Ruby

ga_ruby.rb

module GaRuby
    class GaRubyError < StandardError ; end

    class Gene
        attr_acessor :data,:value
        def initialize(gen_num=nil)
            @value=0
            if block_given?
                @data=Array.new(gen_num){yield}
            else
                @data=[]
            end
        end
    end

    class Genes
        attr_accessor :parent
        def initialize(gen_num,parent_num,
                       elete_num,probability_of_mutate,probability_of_crossover,
                       gene_initializer,evaluate_func,mutate_func,crossover_func,
                       generation_num)
            #遺伝子のサイズ
            @gen_num=gen_num
            #遺伝子(個体)数
            @parent_num=parent_num
            #エリート数
            @elete_num=elete_num
            #突然変異する確率
            @probability_of_mutate=probability_of_mutate
            #交叉する確率
            @probability_of_crossover=probability_of_crossover
            #評価関数
            @evaluate_func=evaluate_func
            #突然変異関数
            @mutate_func=mutate_func
            #交叉関数
            @crossover_func=crossover_func
            #世代数
            @generation_num = generation_num
            @parent = Array.new(@parent_num){Gene.new(@gen_num){gene_initializer.call}}
            @children = []
        end

        def start
            @generation_num.times do
                mark
                normalize_score
                p @parent.first.data
                @children = @parent.take(@elete_num).clone
                begin
                    r = rand
                    @children << if r < @probability_of_mutate
                                     indexes = roullete(1)
                                     @mutate_func.call(@parent[indexes[0]])
                    elsif r < @probability_of_crossover
                        indexes = roullete(2)
                        @crossover_func.call(@parent[indexes[0]],@parent[indexes[1]])
                    else
                        @parent[roullete(1)[0]]
                    end
                end until @children.size == @parent.size
                @parent = @children.clone
            end
            p @children.sort!{|a,b| b.value <=> a.value}.first.data
        end

        private
        def mark
            @parent.each do |gene|
                gene.value = @evaluate_func.call(gene)
            end
            @parent.sort!{|a,b| b.value <=> a.value}
        end
        
        def normalize_score
            @probs = []
            sum = @parent.inject(0){|result,gene| result += gene.value}
            @parent.each{|gene| @probs << gene.value/sum}
            1.step(@probs.size-1) do |idx|
                @probs[idx] += @probs[idx-1]
            end
            @probs[@probs.size-1] = 1.0
        end

        #num: 選択する遺伝子数
        def roullete(num)
            ret = []
            begin
                r = rand
                i = 0
                while r > @probs[i]
                    i+=1
                end
                ret << i
            end until ret.size == num
            ret
        end
    end
end

test.rb

$:.unshift File.expand_path(File.join(File.dirname(__FILE__), *%w<.. lib>))
require 'ga_ruby'

extend GaRuby

evaluate_func = ->(gene){
    1 / ([0,0,1,1,0,0,1,1].zip(gene.data).inject(0) do |result,elem|
        result + (elem[0] - elem[1])**2
    end + 0.1)
}

crossover_func = ->(gene1,gene2){
    gene = Gene.new
    r = rand(8)
    gene.data = gene1.data[0..r] + gene2.data[r+1..7] 
    gene
}

mutate_func2 = ->(gene){
    homu = rand(8)
    if gene.data[homu] == 1
        gene.data[homu] =0
    else
        gene.data[homu]=1
    end
    return gene
}
mutate_func = ->(gene){
    homu = rand(8)
    if rand < 0.5
        gene.data[homu] += rand * gene.data[homu]
    else
        gene.data[homu] -= rand * gene.data[homu]
    end
    return gene
}

genes = Genes.new(8,16,2,0.01,0.2,->{rand},evaluate_func,mutate_func2,crossover_func,10000)
genes.start