Python遗传算法

1 回答

• 2

  代码导致的错误应该足够提供信息 . 切片完成：

  male[:half] + female[half:]
  

  当时正在使用一半，这是一个浮动 . 主要区别是：

  half = int(len(male) / 2)
  

  这可能是预期的功能 . 您不能使用浮点数来索引数组，只能使用整数 .

  这应该是什么：

  from random import randint, random
  from functools import reduce
  from operator import add
  def individual(length, min, max):
      'Create a member of the population.'
      return [randint(min,max) for x in range(length)]
  
  
  def population(count, length, min, max):
      'Create a number of individuals (i.e. a population).'
      return [ individual(length, min, max) for x in range(count) ]
  
  
  def fitness(individual, target):
      'Determine the fitness of an individual. Lower is better.'
      sum = reduce(add, individual, 0)
      return abs(target-sum)
  
  
  def grade(pop, target):
      'Find average fitness for a population.'
      summed = reduce(add, (fitness(x, target) for x in pop), 0)
      return summed / (len(pop) * 1.0)
  
  
  def evolve(pop, target, retain=0.2, random_select=0.05, mutate=0.01):
      graded = [(fitness(x, target), x) for x in pop]
      graded = [x[1] for x in sorted(graded)]
      retain_length = int(len(graded)*retain)
      parents = graded[:retain_length]
  
  # randomly add other individuals to promote genetic diversity
  for individual in graded[retain_length:]:
      if random_select > random():
          parents.append(individual)
  
  # mutate some individuals
  for individual in parents:
      if mutate > random():
          pos_to_mutate = randint(0, len(individual)-1)
          # this mutation is not ideal, because it
          # restricts the range of possible values,
          # but the function is unaware of the min/max
          # values used to create the individuals,
          individual[pos_to_mutate] = randint(
              min(individual), max(individual))
  
  # crossover parents to create children
  parents_length = len(parents)
  desired_length = len(pop) - parents_length
  children = []
  while len(children) < desired_length:
      male = randint(0, parents_length-1)
      female = randint(0, parents_length-1)
      if male != female:
          male = parents[male]
          female = parents[female]
          half = int(len(male) / 2)
          child = male[:half] + female[half:]
          children.append(child)
  
  parents.extend(children)
  return parents
  
  target = 371
  p_count = 100
  i_length = 5
  i_min = 0
  i_max = 100
  p = population(p_count, i_length, i_min, i_max)
  fitness_history = [grade(p, target),]
  chance_to_mutate = 0.01
  for i in p:
      if chance_to_mutate > random():
          place_to_modify = randint(0,len(i))
          i[place_to_modify] = randint(min(i), max(i))
  for i in range(100):
      p = evolve(p, target)
      fitness_history.append(grade(p, target))
  
  for datum in fitness_history:
     print(datum)
  

  回复于 2024-05-17T02:24:43+08:00