Python中遗传算法的问题

我有this遗传算法应该给我 010010010010 或最好的解决方案，突变它工作正常，但当我尝试添加交叉有时它显示这个错误： 'NoneType' object has no attribute 'genes' . 我've tried redoing it from scratch three times and it'总是一样的错误 .
调试也没有随机，有时在找到解决方案之前会出错，有时候没有错误 .

半翻译代码（建议查看original一个，用葡萄牙语中的一些单词）：

import random as r

BASE = '010010010010' #solution


class Populacao: #population
    MAX_POPULACAO = 8 #max population
    TAMANHO_TORNEIO = 5 #ignore
    TAXA_UNIFORME = 0.5 #uniform rate
    TAXA_MUTACAO = 0.015 #mutation rate
    elitismo = True #elitism

    #                  solution generate    chromossomes
    def __init__(self, solucao, gerar=True, cromossomos=None):
        self.solucao = solucao
        if gerar == True:
            self.cromossomos = self._gerar()
        else:
            if cromossomos == None:
                self.cromossomos = []
            else:
                self.cromossomos = cromossomos

        for c in self.cromossomos:
            c.calcularaptidao(self.solucao) # calculate fitness

    def setsolucao(self, solucao):
        self.solucao = solucao

    def _gerar(self):
        return [Cromossomo() for cromossomo in range(0, self.MAX_POPULACAO)]

    def setcromossomo(self, index, cromo):
        self.cromossomos[index] = cromo

    #   getbetter
    def getmelhor(self):
        c1 = self.cromossomos[0]
        for c in self.cromossomos:
            if c.aptidao > c1.aptidao: # c.fitness > c1.fitness
                c1 = c
        return c1

    #   getworst
    def getpior(self):
        #                            getindexofworst
        return self.cromossomos[self.getindicepior()]

    #   getindexofworst
    def getindicepior(self):
        indice = 0 #index
        c1 = self.cromossomos[0]
        for i in range(0, len(self.cromossomos)):
            if self.cromossomos[i].aptidao < c1.aptidao:
                c1 = self.cromossomos[i]
                indice = i
        return indice

    def __str__(self):
        return self.cromossomos

    #   mutation
    def mutacao(self, cromo):
        for i in range(0, len(cromo.genes)):
            if r.random() <= self.TAXA_MUTACAO:
                gene = r.choice([0, 1])
                cromo.setgene(i, gene)

    #   rouletteselection
    def selecaoroleta(self):
        somaaptidao = 0  # fitness sum
        for cromo in self.cromossomos:
            somaaptidao += cromo.aptidao

        #start
        comeco = 0
        for cromo in self.cromossomos:
            #porc = percentage
            porc = (cromo.aptidao * 360) / somaaptidao
            cromo.setporcao(porc)
            cromo.calcularintervalo(comeco) # calculate interval
            comeco += cromo.porcao #portion

        numaleatorio = r.randint(0, 360) #random number
        for cromo in self.cromossomos:
            if numaleatorio > cromo.intervalo[0] and numaleatorio <= cromo.intervalo[1]:
                return cromo

    #   evolve        population
    def evoluir(self, pop):
        newPop = Populacao(pop.solucao, True)

        #offset_elitism
        offset_elitismo = 0
        if pop.elitismo:
            #worst = newPop.getindexofworst()
            pior = newPop.getindicepior()
            newPop.cromossomos[pior] = pop.getmelhor()
            offset_elitismo = 1
        else:
            offset_elitismo = 0

        for i in range(offset_elitismo, len(pop.cromossomos)):
            cromo1 = pop.selecaoroleta() # roulette selection
            cromo2 = pop.selecaoroleta()

            newCromo = self.crossover(cromo1, cromo2)
            newCromo.calcularaptidao(pop.solucao)

            newPop.cromossomos.append(newCromo)

        for i in range(offset_elitismo, len(newPop.cromossomos)):
            self.mutacao(newPop.cromossomos[i])
            newPop.cromossomos[i].calcularaptidao(pop.solucao)
        return newPop

    def crossover(self, cromo1, cromo2):
        assert type(cromo1) != 'NoneType'
        assert type(cromo2) != 'NoneType'

        newCromo = cromo1
        for i in range(0, len(cromo1)):              #<--- error usually here
            if r.random() <= self.TAXA_UNIFORME:
                newCromo.setgene(i, cromo1.genes[i])
            else:
                newCromo.setgene(i, cromo2.genes[i])
        return newCromo


#     chromossomes
class Cromossomo:
    MAX_GENES = 12   #max genes
    aptidao = 0      #fitness
    porcao = 0       #portion
    intervalo = []   #interval

    def __init__(self, genes=None):
        self.genes = genes or self._gerar()

    def _gerar(self):
        cromo = []
        for i in range(0, self.MAX_GENES):
            cromo.append(r.choice([0, 1]))
        return ''.join(map(str, cromo))

    def calcularaptidao(self, solucao):
        apt = 0
        for i in range(0, self.MAX_GENES):
            if self.genes[i] == solucao[i]:
                apt += 1
        self.aptidao = apt

    def setporcao(self, porc):
        self.porcao = porc

    def calcularintervalo(self, comeco):
        self.intervalo = [comeco, comeco + self.porcao]

    def setgene(self, index, gene):
        s = ''
        for i in range(len(self.genes)):
            if i == index:
                s += str(gene)
            else:
                s += self.genes[i]
        self.genes = s

    def __str__(self):
        return self.genes

    def __len__(self):
        return len(self.genes)


if __name__ == '__main__':
    pop = Populacao(BASE, True)
    geracoes = 0              #generations
    geracoes_max = 100        #max generations

    melhor = None             #best chromossomes
    while pop.getmelhor().aptidao < len(BASE) and geracoes < geracoes_max:
        geracoes += 1
        pop = pop.evoluir(pop)
        melhor = pop.getmelhor()

        print('GENERATION ' + str(geracoes) + ', BEST: ' + str(melhor) +
              ', FITNESS: ' + str(melhor.aptidao))

    print('')
    if melhor.aptidao < len(BASE):
        print('BEST SOLUTION: ' + str(melhor))
    else:
        print('SOLUTION FOUND IN ' + str(geracoes) + ' GENERATIONS: ' +
              str(melhor))

    print('FITNESS: ' + str(melhor.aptidao))