Python遗传算法

Question

演进函数和变异函数一样存在问题。

    from random import randint, random
    from operator import add
    from functools import reduce



   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)


    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))


    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 = 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),]
    for i in range(100):
        p = evolve(p, target)
        fitness_history.append(grade(p, target))

    for datum in fitness_history:
       print(datum)

我正在关注这个网站http://lethain.com/genetic-algorithms-cool-name-damn-simple/。它是为Python2.6编写的，所以它不适用于3。我已经对它进行了大部分更新，但无法让它正常工作。

Answer 1

代码引起的错误应该是足够的信息。切片由以下人员完成：

male[:half] + female[half:] 

使用的是half，这在当时是一个浮点数。主要区别是：

half = int(len(male) / 2)

这很可能是预期的功能。不能使用浮点型来索引数组，只能使用整型。

它应该是这样的：

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)