为遗传算法中的适应度比例选择(轮盘赌)生成概率列表

Generate probabilities list for fitness proportionate selection (roulette wheel) in genetic algorithms

首先,如果我的方法过于愚蠢或过于简单,我深表歉意,我是一名非常努力学习编程的经济学家,因此我缺乏一些特定的技能。无论如何,我有以下代码:

population = [[[0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1], [1], [0]],
 [[0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1], [3], [1]],
 [[0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0], [4], [2]],
 [[1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0], [3], [3]]]

def ProbabilityList(population):
    fitness = chromosome[2] for chromosome in population
    manipulated_fitness = fitness + 1
    total_weight=sum(manipulated_fitness)
    relative_fitness= [chromosome[1]/total_weight for chromosome in population]
    probabilities= [sum(relative_fitness) for i in range(len(relative_fitness))]
    return (probabilities)

人口的逻辑是[[[individual1],[fitness][counter]],[individual3],[fitness][counter]], and so on...计数器只是一个数字,所以我可以对个人进行排序。

所以在这种情况下我需要的是创建一个基于总适应度的选择概率列表。我还需要在基本适应度上加1,因为将来这个值可能为零,我不能使用确定性选择方法(即任何个体都不能有0概率)

有谁知道这样处理的正确方法吗?

您可能会考虑的一个库是 numpy,它有一个函数可以完全满足您的要求: A weighted version of random.choice

编辑:这是一种基于您的代码的方法。

from numpy.random import choice    
def ProbabilityList(population):
    #manipulated fitness in one line
    manipulated_fitness = [chromosome[1]+1 for chromosome in population]
    total_weight=sum(manipulated_fitness)
    #define probabilities - note we should use +1 here too otherwise we won't get a proper distribution
    relative_fitness= [(chromosome[1]+1)/total_weight for chromosome in population]
    #get a list of the ids
    ids = [chromosome[2] for chromosome in population]
    #choose one id based on their relative fitness
    draw = choice(ids, 1, p=relative_fitness)
    #return your choice
    return draw
    #if you want to return the probability distribution you can just return relative_fitness

对于稍微复杂一点的数据,我也提出两个建议 structures/methods 你可以阅读这可能会让你的生活更轻松一些:字典或 类。

编辑:我的意思是做类似的事情:

chromosome_dict={id1:{fitness:4,chromosome:[0,1,1,1,0]},
                 id2:{fitness:3,chromosome:[0,0,0,1,1]}}

这不是出于任何计算原因,而是因为它更易于阅读和操作。