本文整理汇总了Python中DataBase.updateFitness方法的典型用法代码示例。如果您正苦于以下问题：Python DataBase.updateFitness方法的具体用法？Python DataBase.updateFitness怎么用？Python DataBase.updateFitness使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类DataBase的用法示例。
在下文中一共展示了DataBase.updateFitness方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: GeneticAlgorithm
# 需要导入模块: import DataBase [as 别名]
# 或者: from DataBase import updateFitness [as 别名]
#.........这里部分代码省略.........
if (i <= cut):
child[i] = p2[i]
else:
child[i] = p1[i]
else:
if(brother):
child = p1
else:
child = p2
return child
def mutation(self, childs):
global heuristic_data_domain
for i in range(len(childs)):
for j in range(heuristic_data_dim):
pr = random.random()
if pr < 0.01:
childs[i][j] = random.uniform(heuristic_data_domain[j][0], heuristic_data_domain[j][1])
def initialization(self):
global heuristic_data_domain
for i in range(self.dim):
data = self.db.getRow(i)
if (data):
for j in range(heuristic_data_dim):
self.parents[i][j] = data[j+1]
else:
for j in range(heuristic_data_dim):
self.parents[i][j] = random.uniform(heuristic_data_domain[j][0], heuristic_data_domain[j][1])
self.db.insert([i, self.parents[i], None])
def replacement(self, childs, childs_fit):
better_fitness = self.parents_fit[0]
posP = 0
for i in range(1, self.dim):
if (self.parents_fit[i] < better_fitness):
better_fitness = self.parents_fit[i]
posP = i
worst_fitness = 0
posC = 0
for i in range(self.dim):
if(childs_fit[i] > worst_fitness ):
worst_fitness = childs_fit[i]
posC = i
childs[posC] = self.parents[posP]
self.parents = childs
self.parents_fit = childs_fit
def fitness(self, p):
queueGA.put([True, p])
fitness = queueBMO.get()
if fitness == None:
self.stop = True
return None
else:
return fitness
def mainLoop(self):
childs_fit = [None] * self.dim
while not self.stop:
childs = self.crossover()
self.mutation(childs)
self.db.deleteAll()
for i in range(self.dim):
self.db.insert([i, childs[i], None])
for i in range(self.dim):
childs_fit[i] = self.fitness(childs[i])
if stop:
return
self.db.updateFitness(i, childs_fit[i])
self.replacement(childs, childs_fit)
def run(self):
self.db = DataBase()
self.db.start()
self.initialization()
for i in range(self.dim):
if(self.db.getFitness(i) == None):
self.parents_fit[i] = self.fitness(self.parents[i])
if not self.stop:
self.db.updateFitness(i, self.parents_fit[i])
else:
break
else:
self.parents_fit[i] = self.db.getFitness(i)
self.mainLoop()
print ("Stoping GA")