Overview 程序概览
官方文档:
1. Types : 选择你要解决的问题类型,确定要求解的问题个数,最大值还是最小值 2. Initialization : 初始化基因编码位数,初始值,等基本信息 3. Operators : 操作,设计evaluate函数,在工具箱中注册参数信息:交叉,变异,保留个体,评价函数 4. Algorithm : 设计main函数,确定参数并运行得到结果Types
# Typesfrom deap import base, creatorcreator.create("FitnessMin", base.Fitness, weights=(-1.0,)) # weights 1.0, 求最大值,-1.0 求最小值# (1.0,-1.0,)求第一个参数的最大值,求第二个参数的最小值creator.create("Individual", list, fitness=creator.FitnessMin) Initialization
import randomfrom deap import toolsIND_SIZE = 10 # 种群数toolbox = base.Toolbox()toolbox.register("attribute", random.random)# 调用randon.random为每一个基因编码编码创建 随机初始值 也就是范围[0,1]toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=IND_SIZE)toolbox.register("population", tools.initRepeat, list, toolbox.individual) Operators
# Operators# difine evaluate function# Note that a comma is a mustdef evaluate(individual): return sum(individual),# use tools in deap to creat our applicationtoolbox.register("mate", tools.cxTwoPoint) # mate:交叉toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1) # mutate : 变异toolbox.register("select", tools.selTournament, tournsize=3) # select : 选择保留的最佳个体toolbox.register("evaluate", evaluate) # commit our evaluate 高斯变异:
这种变异的方法就是，产生一个服从高斯分布的随机数，取代原先基因中的实数数值。这个算法产生的随机数，数学期望当为当前基因的实数数值。
一个模拟产生的算法是，产生6个服从U(0,1)的随机数，以他们的数学期望作为高斯分布随机数的近似。mutate方法
这个函数适用于输入个体的平均值和标准差的高斯突变
mu:python中基于平均值的高斯变异
sigma:python中基于标准差的高斯变异
indpb:每个属性的独立变异概率
mate : 交叉
select : 选择保留的最佳个体
evaluate : 选择评价函数,要注意返回值的地方最后面要多加一个逗号
Algorithms 计算程序
也就是设计主程序的地方,按照官网给的模式,我们要早此处设计其他参数,并设计迭代和取值的代码部分,并返回我们所需要的值.
# Algorithmsdef main(): # create an initial population of 300 individuals (where # each individual is a list of integers) pop = toolbox.population(n=50) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 ''' # CXPB is the probability with which two individuals # are crossed # # MUTPB is the probability for mutating an individual # # NGEN is the number of generations for which the # evolution runs ''' # Evaluate the entire population fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit print(" Evaluated %i individuals" % len(pop)) # 这时候，pop的长度还是300呢 print("-- Iterative %i times --" % NGEN) for g in range(NGEN): if g % 10 == 0: print("-- Generation %i --" % g) # Select the next generation individuals offspring = toolbox.select(pop, len(pop)) # Clone the selected individuals offspring = list(map(toolbox.clone, offspring)) # Change map to list,The documentation on the official website is wrong # Apply crossover and mutation on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offspring: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # The population is entirely replaced by the offspring pop[:] = offspring print("-- End of (successful) evolution --") best_ind = tools.selBest(pop, 1)[0] return best_ind, best_ind.fitness.values # return the result:Last individual,The Return of Evaluate function 要注意的地方就是,官网中给出的Overview代码中有一行代码是错误的,需要把一个数据类型(map)转换为list.
输出结果
Evaluated 50 individuals-- Iterative 40 times ---- Generation 0 ---- Generation 10 ---- Generation 20 ---- Generation 30 ---- End of (successful) evolution --best_ind [-2.402824207878805, -1.5920248739487302, -4.397332290574777, -0.7564815676249151, -3.3478264358788814, -5.900475519316307, -7.739284213710048, -4.469259215914226, 0.35793917907272843, -2.8594709616875256]best_ind.fitness.values (-33.10704010746149,)
- best_ind : 最佳个体
- best_ind.fitness.values : 最佳个体在经过evaluate之后的输出
#!usr/bin/env python# -*- coding:utf-8 _*-"""@author:fonttian @file: Overview.py@time: 2017/10/15 """# Typesfrom deap import base, creatorcreator.create("FitnessMin", base.Fitness, weights=(-1.0,))# weights 1.0, 求最大值,-1.0 求最小值# (1.0,-1.0,)求第一个参数的最大值,求第二个参数的最小值creator.create("Individual", list, fitness=creator.FitnessMin)# Initializationimport randomfrom deap import toolsIND_SIZE = 10 # 种群数toolbox = base.Toolbox()toolbox.register("attribute", random.random)# 调用randon.random为每一个基因编码编码创建 随机初始值 也就是范围[0,1]toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=IND_SIZE)toolbox.register("population", tools.initRepeat, list, toolbox.individual)# Operators# difine evaluate function# Note that a comma is a mustdef evaluate(individual): return sum(individual),# use tools in deap to creat our applicationtoolbox.register("mate", tools.cxTwoPoint) # mate:交叉toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1) # mutate : 变异toolbox.register("select", tools.selTournament, tournsize=3) # select : 选择保留的最佳个体toolbox.register("evaluate", evaluate) # commit our evaluate# Algorithmsdef main(): # create an initial population of 300 individuals (where # each individual is a list of integers) pop = toolbox.population(n=50) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 ''' # CXPB is the probability with which two individuals # are crossed # # MUTPB is the probability for mutating an individual # # NGEN is the number of generations for which the # evolution runs ''' # Evaluate the entire population fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit print(" Evaluated %i individuals" % len(pop)) # 这时候，pop的长度还是300呢 print("-- Iterative %i times --" % NGEN) for g in range(NGEN): if g % 10 == 0: print("-- Generation %i --" % g) # Select the next generation individuals offspring = toolbox.select(pop, len(pop)) # Clone the selected individuals offspring = list(map(toolbox.clone, offspring)) # Change map to list,The documentation on the official website is wrong # Apply crossover and mutation on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offspring: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # The population is entirely replaced by the offspring pop[:] = offspring print("-- End of (successful) evolution --") best_ind = tools.selBest(pop, 1)[0] return best_ind, best_ind.fitness.values # return the result:Last individual,The Return of Evaluate functionif __name__ == "__main__": # t1 = time.clock() best_ind, best_ind.fitness.values = main() # print(pop, best_ind, best_ind.fitness.values) # print("pop",pop) print("best_ind",best_ind) print("best_ind.fitness.values",best_ind.fitness.values) # t2 = time.clock() # print(t2-t1)