我有this遗传算法，应该为我提供010010010010或最佳解决方案，通过突变它可以正常工作，但是当我尝试添加交叉时，有时会显示此错误：'NoneType' object has no attribute 'genes'。我尝试从头开始重做它三遍，并且总是相同的错误。
调试也没有用，因为它是随机的，有时在找到解决方案之前会提示错误，有时也没有错误。
半翻译的代码（建议看到original一个，用葡萄牙语写一些词）：
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))
最佳答案
似乎发生错误时，selecaoreleta运行，但最后一个for循环未返回任何内容。例如，如果您输入
for cromo in self.cromossomos:
if numaleatorio > cromo.intervalo[0] and numaleatorio <= cromo.intervalo[1]:
return cromo
else:
print('no cromo found')
错误发生时，它将打印“找不到cromo”。 （是的，您可以放
else来表示如果for循环没有中断而完成的操作：p。）我不知道您要检查的是哪种条件，因为它是葡萄牙语，但是无论如何，有时是流行的cromossomos中的任何cromo都不满意。这不是一个完整的答案，但希望它可以帮助您找出问题所在。
PS，您的断言可能无法正常工作。在交叉功能中尝试使用类似
assert (cromo1 is not None) and (cromo2 is not None)的方法，而不要使用type(cromo1) == 'NoneType'。然后，AssertionErrors应该开始更好地弹出。编辑：
因此，再次在selecaoroleta中，在使用随机numaleatorio的最后一个循环内，打印出numaleatorio和带有
print(numaleatorio, cromo.intervalo)的intervalo，表明当numaleatorio为0 ...或360时总是会发生错误。但是选择cromo的条件是，由于if numaleatorio > cromo.intervalo[0] and numaleatorio <= cromo.intervalo[1]:，即使numaleatorio为0，>也将失败，即使intervalo [0]也为0。在此之上的另一件事是，打印出音调间隔显示有时最高的音调间隔类似于359.9999999，因此360的数字也将失败。因此，解决方法是将numaleatorio更改为numaleatorio = r.randint(1, 359)。或者，为了保持随机性，我可以做numaleatorio = r.randint(0, 359)，然后从>和<=切换为使用>=和<。整个最后一个循环可能看起来像numaleatorio = r.randint(0, 359)
for cromo in self.cromossomos:
if numaleatorio >= cromo.intervalo[0] and numaleatorio < cromo.intervalo[1]:
return cromo
else:
raise ValueError('Intervalo was bad. No cromo found!')
（
else再次与for循环处于同一级别。它是for (blank in blank): ... else: (do stuff here if the for loop finished without being interrupted)。您可以根据需要执行此操作。）这样，您的代码输出：
MELHOR SOLUCAO: 010010011010
APTIDAO: 11
我希望我知道那是什么意思。