二进制数的Python遗传算法

Question

我被要求制作一个遗传算法，目标是确定一个包含最多 1 和 0 的 8 位字符串。 eval 函数应返回更改数加 1。因此，例如 00000000 返回 1，00011100 返回 3，01100101 返回 6。这就是我所拥有的:

def random_population():
    from random import choice

    pop = ''.join(choice(('0','1')) for _ in range(8))
    return pop   

def mutate(dna):   
    """   For each gene in the DNA, there is a 1/mutation_chance chance 
    that it will be   switched out with a random character. This ensures 
    diversity in the   population, and ensures that is difficult to get stuck in 
    local minima.   """   
    dna_out = ""   
    mutation_chance = 100   
    for c in xrange(DNA_SIZE):
        if int(random.random()*mutation_chance) == 1:
            dna_out += random_char()
        else:
            dna_out += dna[c]   return dna_out

def crossover(dna1, dna2):   
    """   Slices both dna1 and dna2 into two parts at a random index within their   
    length and merges them. Both keep their initial sublist up to the crossover   
    index, but their ends are swapped.   """   
    pos = int(random.random()*DNA_SIZE)
    return (dna1[:pos]+dna2[pos:], dna2[:pos]+dna1[pos:])

def eval(dna):
    changes = 0
    for index, bit in enumerate(dna):
        if(index == 0):
            prev = bit
        else:
            if(bit != prev):
                changes += 1
        prev = bit
    return changes+1


#============== End Functions =======================#


#============== Main ================# changes = 0

prev = 0
dna = random_population()
print "dna: "
print dna
print eval(dna)

我在实际弄清楚遗传算法部分(交叉/突变)时遇到了麻烦。我应该随机配对数字，然后随机选择一对，留下一对不变，然后在随机点交叉。然后它将通过从整个种群中随机变异一位来结束。当前的交叉和变异代码取 self 发现并试图理解的遗传算法示例。欢迎任何帮助。

Answer 1

我建议的部分内容:

该代码无法正常工作，但它可能会传输信息。

# a population consists of many individuals
def random_population(population_size = 10):
    from random import choice

    pop = [''.join(choice(('0','1')) for _ in range(8)) for i in range(population_size)]
    return pop   

# you need a fitness function
def fitness(individual):
    return # a value from 0 up

def algorithm():
    # a simple algorithm somehow alike
    # create population
    population = random_population()
    # this loop must stop after some rounds because the best result may never be reached
    while goal_not_reached(population) and not time_is_up():
        # create the roulette wheel
        roulette_wheel = map(fitness, population)
        # highest value of roulette wheel
        max_value = sum(roulette_wheel)
        # the new generation
        new_population = []
        for i in range(len(population) - len(new_population)):
             # create children from the population
                 # choose 2 random values from 0 to max_value and find the individuals
                 # for it in the roulette wheel, combine them to new individuals 
             new_population.append(new_individual)
        # mutate the population
        population = map(mutate, new_population)             # a new generation is created