class GA(object):
def __init__(self,
x_rate=0.7,
mutation_rate=0.005,
life_count=50,
gene_length=100,
judge=lambda lf, av: 1,
save=lambda: 1,
mk_life=lambda: None,
x_func=None,
m_func=None):
self.x_rate = x_rate
self.mutation_rate = mutation_rate
self.mutation_count = 0
self.generation = 0
self.lives = []
self.bounds = 0.0 # 得分总数
self.best = None
self.life_count = life_count
self.gene_length = gene_length
self.__judge = judge
self.save = save
self.mk_life = mk_life # 默认的产生生命的函数
self.x_func = (x_func, self.__x_func)[x_func == None] # 自定义交叉函数
self.m_func = (m_func, self.__m_func)[m_func == None] # 自定义变异函数
for i in range(life_count):
self.lives.append(Life(self, self.mk_life()))
def __x_func(self, p1, p2):
# 默认交叉函数
r = random.randint(0, self.gene_length)
gene = p1.gene[0:r] + p2.gene[r:]
return gene
def __m_func(self, gene):
# 默认突变函数
r = random.randint(0, self.gene_length - 1)
gene = gene[:r] + ("0", "1")[gene[r:r] == "1"] + gene[r + 1:]
return gene
def __bear(self, p1, p2):
# 根据父母 p1, p2 生成一个后代
r = random.random()
if r < self.x_rate:
# 交叉
gene = self.x_func(p1, p2)
else:
gene = p1.gene
r = random.random()
if r < self.mutation_rate:
# 突变
gene = self.m_func(gene)
self.mutation_count += 1
return Life(self, gene)
def __get_one(self):
# 根据得分情况，随机取得一个个体，机率正比于个体的score属性
r = random.uniform(0, self.bounds)
for lf in self.lives:
r -= lf.score
if r <= 0:
return lf
def __new_child(self):
# 产生新的后代
return self.__bear(self.__get_one(), self.__get_one())
def judge(self, f=lambda lf, av: 1):
# 根据传入的方法 f ，计算每个个体的得分
last_avg = self.bounds / float(self.life_count)
self.bounds = 0.0
self.best = Life(self)
self.best.set_score(-1.0)
for lf in self.lives:
lf.score = f(lf, last_avg)
if lf.score > self.best.score:
self.best = lf
self.bounds += lf.score
def next(self, n=1):
# 演化至下n代
while n > 0:
# self.__getBounds()
self.judge(self.__judge)
new_lives = [Life(self, self.best.gene)]
# self.bestHistory.append(self.best)
while len(new_lives) < self.life_count:
new_lives.append(self.__new_child())
self.lives = new_lives
self.generation += 1
# print("gen: %d, mutation: %d, best: %f" % (self.generation, self.mutationCount, self.best.score))
self.save(self.best, self.generation)
n -= 1
