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  2. def objective_function(vector)
  3. return vector.inject(0.0) {|sum, x| sum + (x ** 2.0)}
  4. end
  6. def random_vector(minmax)
  7. return Array.new(minmax.size) do |i|
  8. minmax[i][0] + ((minmax[i][1] - minmax[i][0]) * rand())
  9. end
  10. end
  12. def random_gaussian(mean=0.0, stdev=1.0)
  13. u1 = u2 = w = 0
  14. begin
  15. u1 = 2 * rand() - 1
  16. u2 = 2 * rand() - 1
  17. w = u1 * u1 + u2 * u2
  18. end while w >= 1
  19. w = Math.sqrt((-2.0 * Math.log(w)) / w)
  20. return mean + (u2 * w) * stdev
  21. end
  23. def mutate(candidate, search_space)
  24. child = {:vector=>[], :strategy=>[]}
  25. candidate[:vector].each_with_index do |v_old, i|
  26. s_old = candidate[:strategy][i]
  27. v = v_old + s_old * random_gaussian()
  28. v = search_space[i][0] if v < search_space[i][0]
  29. v = search_space[i][1] if v > search_space[i][1]
  30. child[:vector] << v
  31. child[:strategy] << s_old + random_gaussian() * s_old.abs**0.5
  32. end
  33. return child
  34. end
  36. def tournament(candidate, population, bout_size)
  37. candidate[:wins] = 0
  38. bout_size.times do |i|
  39. other = population[rand(population.size)]
  40. candidate[:wins] += 1 if candidate[:fitness] < other[:fitness]
  41. end
  42. end
  44. def init_population(minmax, pop_size)
  45. strategy = Array.new(minmax.size) do |i|
  46. [0, (minmax[i][1]-minmax[i][0]) * 0.05]
  47. end
  48. pop = Array.new(pop_size, {})
  49. pop.each_index do |i|
  50. pop[i][:vector] = random_vector(minmax)
  51. pop[i][:strategy] = random_vector(strategy)
  52. end
  53. pop.each{|c| c[:fitness] = objective_function(c[:vector])}
  54. return pop
  55. end
  57. def search(max_gens, search_space, pop_size, bout_size)
  58. population = init_population(search_space, pop_size)
  59. population.each{|c| c[:fitness] = objective_function(c[:vector])}
  60. best = population.sort{|x,y| x[:fitness] <=> y[:fitness]}.first
  61. max_gens.times do |gen|
  62. children = Array.new(pop_size) {|i| mutate(population[i], search_space)}
  63. children.each{|c| c[:fitness] = objective_function(c[:vector])}
  64. children.sort!{|x,y| x[:fitness] <=> y[:fitness]}
  65. best = children.first if children.first[:fitness] < best[:fitness]
  66. union = children+population
  67. union.each{|c| tournament(c, union, bout_size)}
  68. union.sort!{|x,y| y[:wins] <=> x[:wins]}
  69. population = union.first(pop_size)
  70. puts " > gen #{gen}, fitness=#{best[:fitness]}"
  71. end
  72. return best
  73. end
  75. if __FILE__ == $0
  76. # problem configuration
  77. problem_size = 2
  78. search_space = Array.new(problem_size) {|i| [-5, +5]}
  79. # algorithm configuration
  80. max_gens = 200
  81. pop_size = 100
  82. bout_size = 5
  83. # execute the algorithm
  84. best = search(max_gens, search_space, pop_size, bout_size)
  85. puts "done! Solution: f=#{best[:fitness]}, s=#{best[:vector].inspect}"
  86. end
  87.  
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