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。
请您参考如下方法:
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
