Deap: python中的遗传算法工具箱
Overview 程序概览
官方文档:http://deap.readthedocs.io/en/master/index.html
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
# Types
from deap import base, creator
creator.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 random
from deap import tools
IND_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 must
def evaluate(individual):
return sum(individual),
# use tools in deap to creat our application
toolbox.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.
# Algorithms
def 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
"""
# Types
from deap import base, creator
creator.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 random
from deap import tools
IND_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 must
def evaluate(individual):
return sum(individual),
# use tools in deap to creat our application
toolbox.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
# Algorithms
def 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
if __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)
