Overview 程序概覽

官方文檔:http://deap.readthedocs.io/en/master/index.html

1. Types : 選擇你要解決的問題類型,確定要求解的問題個(gè)數(shù),最大值還是最小值

2. Initialization : 初始化基因編碼位數(shù),初始值,等基本信息

3. Operators : 操作,設(shè)計(jì)evaluate函數(shù),在工具箱中注冊(cè)參數(shù)信息:交叉,變異,保留個(gè)體,評(píng)價(jià)函數(shù)

4. Algorithm : 設(shè)計(jì)main函數(shù),確定參數(shù)并運(yùn)行得到結(jié)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,)求第一個(gè)參數(shù)的最大值,求第二個(gè)參數(shù)的最小值

creator.create("Individual", list, fitness=creator.FitnessMin)

Initialization

import random

from deap import tools

IND_SIZE = 10 # 種群數(shù)

toolbox = base.Toolbox()

toolbox.register("attribute", random.random)

# 調(diào)用randon.random為每一個(gè)基因編碼編碼創(chuàng)建 隨機(jī)初始值 也就是范圍[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 : 變異

toolbox.register("select", tools.selTournament, tournsize=3) # select : 選擇保留的最佳個(gè)體

toolbox.register("evaluate", evaluate) # commit our evaluate

高斯變異:

這種變異的方法就是，產(chǎn)生一個(gè)服從高斯分布的隨機(jī)數(shù)，取代原先基因中的實(shí)數(shù)數(shù)值。這個(gè)算法產(chǎn)生的隨機(jī)數(shù)，數(shù)學(xué)期望當(dāng)為當(dāng)前基因的實(shí)數(shù)數(shù)值。

一個(gè)模擬產(chǎn)生的算法是，產(chǎn)生6個(gè)服從U(0,1)的隨機(jī)數(shù)，以他們的數(shù)學(xué)期望作為高斯分布隨機(jī)數(shù)的近似。

mutate方法

這個(gè)函數(shù)適用于輸入個(gè)體的平均值和標(biāo)準(zhǔn)差的高斯突變

mu:python中基于平均值的高斯變異

sigma:python中基于標(biāo)準(zhǔn)差的高斯變異

indpb:每個(gè)屬性的獨(dú)立變異概率

mate : 交叉

select : 選擇保留的最佳個(gè)體

evaluate : 選擇評(píng)價(jià)函數(shù),要注意返回值的地方最后面要多加一個(gè)逗號(hào)

Algorithms 計(jì)算程序

也就是設(shè)計(jì)主程序的地方,按照官網(wǎng)給的模式,我們要早此處設(shè)計(jì)其他參數(shù),并設(shè)計(jì)迭代和取值的代碼部分,并返回我們所需要的值.

# 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)) # 這時(shí)候，pop的長度還是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,官網(wǎng)中給出的Overview代碼中有一行代碼是錯(cuò)誤的,需要把一個(gè)數(shù)據(jù)類型(map)轉(zhuǎn)換為list.

輸出結(jié)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 : 最佳個(gè)體

best_ind.fitness.values : 最佳個(gè)體在經(jīng)過evaluate之后的輸出

#!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,)求第一個(gè)參數(shù)的最大值,求第二個(gè)參數(shù)的最小值

creator.create("Individual", list, fitness=creator.FitnessMin)

# Initialization

import random

from deap import tools

IND_SIZE = 10 # 種群數(shù)

toolbox = base.Toolbox()

toolbox.register("attribute", random.random)

# 調(diào)用randon.random為每一個(gè)基因編碼編碼創(chuàng)建 隨機(jī)初始值 也就是范圍[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 : 變異

toolbox.register("select", tools.selTournament, tournsize=3) # select : 選擇保留的最佳個(gè)體

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)) # 這時(shí)候，pop的長度還是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)

總結(jié)

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