def find_new_individual(population, num):
# 如果杂交过程中出现重复个体， 替换掉, 概率pp
pp = random.random()
if num / NG < pp:
diff_population = list(set(population))
diff = len(population) - len(diff_population)
new_population = create_population()
while diff:
new_individual = new_population[random.randrange(
len(new_population))]
if new_individual not in diff_population:
diff_population.append(new_individual)
diff -= 1
return population
def out_of_bound(population):
# 越界的舍去， 引入新个体
for individual in population:
INDEX = population.index(individual)
if bound_x[0] <= individual <= bound_x[1]:
pass
else:
while True:
new_population = create_population()
new_individual = new_population[random.randrange(
len(new_population))]
if new_individual not in population:
population[INDEX] = new_individual
# 新个体不在种群，引入
break
else:
pass
return population
pop_ls = proportional_selection(*population)
pp_ls = add_pop(pop_ls, population)
individuals_ls = []
for i in range(len(population)):
pp = random.random()
father = wheel(pp, pp_ls, population)
mother = population[random.randrange(len(population))]
# 配对的母亲, individual是父亲
individual_new = variation(crossing(father, mother))
# 先杂交，再变异，生成新个体
individuals_ls.append(individual_new)
return individuals_ls
if __name__ == '__main__':
population = create_population()
print(population)
# 产生初始种群
NOMG = 0
# number of max generation, 迭代次数统计
while True:
if NOMG >= NG:
print(population)
flag = func(population[0])
optimum = population[0]
for individual in population[1:]:
# 求最适值
Flag = func(individual)
if Flag > flag:
flag = Flag
optimum = individual