特殊

自定义评价函数

同前

def evalKnapsack(individual):weight = 0.0value = 0.0for item in individual:weight += items[item][0]value += items[item][1]if len(individual) > MAX_ITEM or weight > MAX_WEIGHT:return 10000, 0  # Ensure overweighted bags are dominatedreturn weight, value,

自定义交叉函数

def cxSet(ind1, ind2):"""Apply a crossover operation on input sets. The first child is theintersection of the two sets, the second child is the difference of thetwo sets."""temp = set(ind1)  # Used in order to keep typeind1 &= ind2  # Intersection (inplace)ind2 ^= temp  # Symmetric Difference (inplace)return ind1, ind2

&=,^= python中的位运算符
建议在新标签页打开图片

自定义变异函

def mutSet(individual):"""Mutation that pops or add an element."""if random.random() < 0.5:if len(individual) > 0:  # We cannot pop from an empty setindividual.remove(random.choice(sorted(tuple(individual))))else:individual.add(random.randrange(NBR_ITEMS))return individual,

使用短版本的遗传算法

def main():random.seed(64)NGEN = 50MU = 50LAMBDA = 100CXPB = 0.7MUTPB = 0.2pop = toolbox.population(n=MU)hof = tools.ParetoFront()stats = tools.Statistics(lambda ind: ind.fitness.values)stats.register("avg", numpy.mean, axis=0)stats.register("std", numpy.std, axis=0)stats.register("min", numpy.min, axis=0)stats.register("max", numpy.max, axis=0)algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,halloffame=hof)return pop, stats, hof

此处与之前的文章效果类似
- 粒子群优化算法
- 短版本可以参考官网介绍

源代码

#!usr/bin/env python
#-*- coding:utf-8 _*-
"""
@author:fonttian
@file: knapsackProblem.py
@time: 2017/10/15
"""
#    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 <http://www.gnu.org/licenses/>.import randomimport numpyfrom deap import algorithms
from deap import base
from deap import creator
from deap import toolsIND_INIT_SIZE = 5 # 基因编码位数
MAX_ITEM = 50
MAX_WEIGHT = 50
NBR_ITEMS = 20# To assure reproductibility, the RNG seed is set prior to the items
# dict initialization. It is also seeded in main().
random.seed(64)# Create the item dictionary: item name is an integer, and value is
# a (weight, value) 2-uple.
items = {}
# Create random items and store them in the items' dictionary.
for i in range(NBR_ITEMS):items[i] = (random.randint(1, 10), random.uniform(0, 100))creator.create("Fitness", base.Fitness, weights=(-1.0, 1.0))
creator.create("Individual", set, fitness=creator.Fitness)toolbox = base.Toolbox()# Attribute generator
toolbox.register("attr_item", random.randrange, NBR_ITEMS)# Structure initializers
toolbox.register("individual", tools.initRepeat, creator.Individual,toolbox.attr_item, IND_INIT_SIZE)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)def evalKnapsack(individual):weight = 0.0value = 0.0for item in individual:weight += items[item][0]value += items[item][1]if len(individual) > MAX_ITEM or weight > MAX_WEIGHT:return 10000, 0  # Ensure overweighted bags are dominatedreturn weight, value,def cxSet(ind1, ind2):"""Apply a crossover operation on input sets. The first child is theintersection of the two sets, the second child is the difference of thetwo sets."""temp = set(ind1)  # Used in order to keep typeind1 &= ind2  # Intersection (inplace)ind2 ^= temp  # Symmetric Difference (inplace)return ind1, ind2def mutSet(individual):"""Mutation that pops or add an element."""if random.random() < 0.5:if len(individual) > 0:  # We cannot pop from an empty setindividual.remove(random.choice(sorted(tuple(individual))))else:individual.add(random.randrange(NBR_ITEMS))return individual,toolbox.register("evaluate", evalKnapsack)
toolbox.register("mate", cxSet)
toolbox.register("mutate", mutSet)
toolbox.register("select", tools.selNSGA2)def main():random.seed(64)NGEN = 50MU = 50LAMBDA = 100CXPB = 0.7MUTPB = 0.2pop = toolbox.population(n=MU)hof = tools.ParetoFront()stats = tools.Statistics(lambda ind: ind.fitness.values)stats.register("avg", numpy.mean, axis=0)stats.register("std", numpy.std, axis=0)stats.register("min", numpy.min, axis=0)stats.register("max", numpy.max, axis=0)algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,halloffame=hof)return pop, stats, hofif __name__ == "__main__":pop, stats, hof = main()print("最佳装包为(最佳个体) :",hof[-1])print(len(pop))print(len(hof))print("最佳装包时的重量与价值(最佳适应度) :",evalKnapsack(hof[-1]))

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Deap : 遗传算法算法解决背包问题

特殊

自定义评价函数

自定义交叉函数

自定义变异函

使用短版本的遗传算法

源代码

Deap : 遗传算法算法解决背包问题相关推荐

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Deap : 遗传算法算法解决 背包问题

特殊

自定义评价函数

自定义交叉函数

自定义变异函

使用短版本的遗传算法

源代码

Deap : 遗传算法算法解决 背包问题相关推荐

最新文章

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Deap : 遗传算法算法解决背包问题

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