class GA(object):
def __init__(self,
xRate=0.7,
mutationRate=0.005,
lifeCount=50,
geneLength=100,
judge=lambda lf, av: 1,
save=lambda: 1,
mkLife=lambda: None,
xFunc=None,
mFunc=None):
self.xRate = xRate
self.mutationRate = mutationRate
self.mutationCount = 0
self.generation = 0
self.lives = []
self.bounds = 0.0 # 得分总数
self.best = None
self.lifeCount = lifeCount
self.geneLength = geneLength
self.__judge = judge
self.save = save
self.mkLife = mkLife # 默认的产生生命的函数
self.xFunc = (xFunc, self.__xFunc)[xFunc == None] # 自定义交叉函数
self.mFunc = (mFunc, self.__mFunc)[mFunc == None] # 自定义变异函数
for i in range(lifeCount):
self.lives.append(Life(self, self.mkLife()))
def __xFunc(self, p1, p2):
# 默认交叉函数
r = random.randint(0, self.geneLength)
gene = p1.gene[0:r] + p2.gene[r:]
return gene
def __mFunc(self, gene):
# 默认突变函数
r = random.randint(0, self.geneLength - 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.xRate:
# 交叉
gene = self.xFunc(p1, p2)
else:
gene = p1.gene
r = random.random()
if r < self.mutationRate:
# 突变
gene = self.mFunc(gene)
self.mutationCount += 1
return Life(self, gene)
def __getOne(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 __newChild(self):
# 产生新的后代
return self.__bear(self.__getOne(), self.__getOne())
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
lastAvg = self.bounds / float(self.lifeCount)
self.bounds = 0.0
self.best = Life(self)
self.best.setScore(-1.0)
for lf in self.lives:
lf.score = f(lf, lastAvg)
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)
newLives = []
newLives.append(Life(self, self.best.gene)) # 将最好的父代加入竞争
# self.bestHistory.append(self.best)
while (len(newLives) < self.lifeCount):
newLives.append(self.__newChild())
self.lives = newLives
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
class GA(object):
# initialization
def __init__(self,
xRate=0.7,
mutationRate=0.005,
lifeCount=50,
geneLength=50,
judge=lambda lf, av: 1,
save=lambda: 1,
mkLife=lambda: None,
xFunc=None,
mFunc=None):
self.xRate = xRate
self.mutationRate = mutationRate
self.mutationCount = 0
self.generation = 0
self.lives = []
self.bounds = 0.0
self.best = None
self.lifeCount = lifeCount
self.geneLength = geneLength
self.__judge = judge
self.save = save
self.mkLife = mkLife
self.xFunc = (xFunc, self.__xFunc)[xFunc == None]
self.mFunc = (mFunc, self.__mFunc)[mFunc == None]
# Creating a life set
for i in range(lifeCount):
self.lives.append(Life(self, self.mkLife()))
# Default cross function
def __xFunc(self, p1, p2):
r = random.randint(0, self.geneLength)
gene = p1.gene[0:r] + p2.gene[r:]
return gene
# Default mutation function
def __mFunc(self, gene):
r = random.randint(0, self.geneLength - 1)
gene = gene[:r] + ("0", "1")[gene[r:r] == "1"] + gene[r + 1:]
return gene
# Produce offspring
def __bear(self, p1, p2):
# cross
r = random.random()
if r < self.xRate:
gene = self.xFunc(p1, p2)
else:
gene = p1.gene
# mutation
r = random.random()
if r < self.mutationRate:
gene = self.mFunc(gene)
self.mutationCount += 1
# Return to the living body
return Life(self, gene)
# According to the score situation, randomly obtain an individual, the probability is proportional to the individual's score attribute
def __getOne(self):
# Roulette
r = random.uniform(0, self.bounds)
for lf in self.lives:
r -= lf.score
if r <= 0:
return lf
# Generate new offspring
def __newChild(self):
return self.__bear(self.__getOne(), self.__getOne())
# According to the incoming method f, find the optimal life and life set points
def judge(self, f=lambda lf, av: 1):
# The average score
lastAvg = self.bounds / float(self.lifeCount)
self.bounds = 0.0
self.best = Life(self)
self.best.setScore(-1.0)
for lf in self.lives:
lf.score = f(lf, lastAvg)
if lf.score > self.best.score:
self.best = lf
self.bounds += lf.score
# Evolution to the next n generation
def next(self, n=1):
while n > 0:
# Evaluation group
self.judge(self.__judge)
# New life collection
newLives = []
newLives.append(Life(
self,
self.best.gene)) # Join the best parents in the competition
# Generating new life set individuals
while (len(newLives) < self.lifeCount):
newLives.append(self.__newChild())
# Update new life set
self.lives = newLives
self.generation += 1
self.save(self.best, self.generation)
n -= 1
class GA(object):
# 初始化
def __init__(self, xRate = 0.7, mutationRate = 0.005, lifeCount = 50, geneLength = 50, judge = lambda lf, av: 1, save = lambda: 1, mkLife = lambda: None, xFunc = None, mFunc = None):
self.xRate = xRate # 交叉率
self.mutationRate = mutationRate # 突变率
self.mutationCount = 0 # 突变次数
self.generation = 0 # 进化代数
self.lives = [] # 生命集合
self.bounds = 0.0 # 得分总数
self.best = None # 最优解
self.lifeCount = lifeCount # 生命个数
self.geneLength = geneLength # 基因长度
self.__judge = judge # 评价函数
self.save = save # 保存函数
self.mkLife = mkLife # 默认的产生生命的函数
self.xFunc = (xFunc, self.__xFunc)[xFunc == None] # 自定义交叉函数
self.mFunc = (mFunc, self.__mFunc)[mFunc == None] # 自定义变异函数
# 创造生命集
for i in range(lifeCount):
self.lives.append(Life(self, self.mkLife()))
# 默认交叉函数
def __xFunc(self, p1, p2):
r = random.randint(0, self.geneLength)
gene = p1.gene[0:r] + p2.gene[r:]
return gene
# 默认突变函数
def __mFunc(self, gene):
r = random.randint(0, self.geneLength - 1)
gene = gene[:r] + ("0", "1")[gene[r:r] == "1"] + gene[r + 1:]
return gene
# 产生后代
def __bear(self, p1, p2):
# 交叉
r = random.random()
if r < self.xRate:
gene = self.xFunc(p1, p2)
else:
gene = p1.gene
# 突变
r = random.random()
if r < self.mutationRate:
gene = self.mFunc(gene)
self.mutationCount += 1
# 返回生命体
return Life(self, gene)
# 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
def __getOne(self):
# 轮盘
r = random.uniform(0, self.bounds)
for lf in self.lives:
r -= lf.score;
if r <= 0:
return lf
# 产生新的后代
def __newChild(self):
return self.__bear(self.__getOne(), self.__getOne())
# 根据传入的方法f，求得最优生命体和生命集总分
def judge(self, f = lambda lf, av: 1):
# 平均分
lastAvg = self.bounds / float(self.lifeCount)
self.bounds = 0.0
self.best = Life(self)
self.best.setScore(-1.0)
for lf in self.lives:
lf.score = f(lf, lastAvg)
if lf.score > self.best.score:
self.best = lf
self.bounds += lf.score
# 演化至下n代
def next(self, n = 1):
while n > 0:
# 评估群体
self.judge(self.__judge)
# 新生命集
newLives = []
newLives.append(Life(self, self.best.gene)) # 将最好的父代加入竞争
# 产生新的生命集个体
while (len(newLives) < self.lifeCount):
newLives.append(self.__newChild())
# 更新新的生命集
self.lives = newLives
self.generation += 1
self.save(self.best, self.generation)
n -= 1
class GA(object):
def __init__(self, xRate=0.7, mutationRate=0.005, lifeCount=50, geneLength=100, judge=lambda lf, av: 1,
save=lambda: 1, mkLife=lambda: None, xFunc=None, mFunc=None):
self.xRate = xRate
self.mutationRate = mutationRate
self.mutationCount = 0
self.generation = 0
self.lives = []
self.bounds = 0.0 # 得分总数
self.best = None
self.lifeCount = lifeCount
self.geneLength = geneLength
self.__judge = judge
self.save = save
self.mkLife = mkLife # 默认的产生生命的函数
self.xFunc = (xFunc, self.__xFunc)[xFunc == None] # 自定义交叉函数
self.mFunc = (mFunc, self.__mFunc)[mFunc == None] # 自定义变异函数
for i in range(lifeCount):
self.lives.append(Life(self, self.mkLife()))
def __xFunc(self, p1, p2):
# 默认交叉函数
r = random.randint(0, self.geneLength)
gene = p1.gene[0:r] + p2.gene[r:]
return gene
def __mFunc(self, gene):
# 默认突变函数
r = random.randint(0, self.geneLength - 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.xRate:
# 交叉
gene = self.xFunc(p1, p2)
else:
gene = p1.gene
r = random.random()
if r < self.mutationRate:
# 突变
gene = self.mFunc(gene)
self.mutationCount += 1
return Life(self, gene)
def __getOne(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 __newChild(self):
# 产生新的后代
return self.__bear(self.__getOne(), self.__getOne())
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
lastAvg = self.bounds / float(self.lifeCount)
self.bounds = 0.0
self.best = Life(self)
self.best.setScore(-1.0)
for lf in self.lives:
lf.score = f(lf, lastAvg)
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)
newLives = []
newLives.append(Life(self, self.best.gene)) # 将最好的父代加入竞争
# self.bestHistory.append(self.best)
while (len(newLives) < self.lifeCount):
newLives.append(self.__newChild())
self.lives = newLives
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
