DEAP Example: One Max Problem: Short Version

DEAP Example: One Max Problem: Short Version

DEAP原文链接
[TOC]
这个简短的例子与之前完整的One Max例子很相似。唯一区别是它采用了实现基本进化算法的算法模块。初始化基本相同，不同的是我们需要导入一些额外的包和模块。

import array
import numpy

from deap import algorithms

为了实现算法中的进化函数，我们需从工具模块中注册一些函数：evaluate(), mate(), mutate(), 和select()。

toolbox.register("evaluate", evalOneMax)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)

然后将工具箱传递给算法，并通过stats参数使用已注册的函数。

def main():
    pop = toolbox.population(n=300)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("std", numpy.std)
    stats.register("min", numpy.min)
    stats.register("max", numpy.max)

    pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40,
                                   stats=stats, halloffame=hof, verbose=True)

这个简短的例子利用了HallOfFame来跟踪进化中出现的最好的个体（使它永不消失），同时，使用Statistics对象来统计进化过程中的种群数据。
算法模块中的每一个算法都可以处理这些对象。最后，verbose关键字表明我们是否希望该算法在每一代之后输出结果。

完整代码

#    This file is part of DEAP.
#
#    DEAP is free software: you can redistribute it and/or modify
#    it under the terms of the GNU Lesser General Public License as
#    published by the Free Software Foundation, either version 3 of
#    the License, or (at your option) any later version.
#
#    DEAP is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#    GNU Lesser General Public License for more details.
#
#    You should have received a copy of the GNU Lesser General Public
#    License along with DEAP. If not, see .

import array
import random

import numpy

from deap import algorithms
from deap import base
from deap import creator
from deap import tools

creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax)

toolbox = base.Toolbox()

# Attribute generator
toolbox.register("attr_bool", random.randint, 0, 1)

# Structure initializers
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

def evalOneMax(individual):
    return sum(individual),

toolbox.register("evaluate", evalOneMax)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)

def main():
    random.seed(64)

    pop = toolbox.population(n=300)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("std", numpy.std)
    stats.register("min", numpy.min)
    stats.register("max", numpy.max)

    pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40,
                                   stats=stats, halloffame=hof, verbose=True)

    return pop, log, hof

if __name__ == "__main__":
    main()