EA算法Python源程序

EA（Evolutionary Algorithm，进化算法）是一种模拟自然选择过程的计算优化技术，常用于解决复杂的搜索、优化问题，如遗传算法(GA)和粒子群优化(PSO)等。在Python中，有许多库支持这些算法，比如`deap`（Distributed Evolutionary Algorithms in Python）、`pyevolve`等。

下面是一个简单的遗传算法（GA）的Python基础代码示例，使用了`deap`库：

import random
from deap import base, creator, tools

# 定义基因型和表现型
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

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

toolbox = base.Toolbox()
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=40)
toolbox.register("population", tools.initRepeat, list, toolbox.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)

pop = toolbox.population(n=300)
for gen in range(50): # 进化50代
    offspring = toolbox.select(pop, len(pop))
    offspring = [toolbox.clone(ind) for ind in offspring]

    for child1, child2 in zip(offspring[::2], offspring[1::2]):
        if random.random() < 0.7:
            toolbox.mate(child1, child2)
            del child1.fitness.values
            del child2.fitness.values

    for mutant in offspring:
        if random.random() < 0.2:
            toolbox.mutate(mutant)
            del mutant.fitness.values

    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
    fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
    for ind, fit in zip(invalid_ind, fitnesses):
        ind.fitness.values = fit

    pop[:] = offspring

best_ind = tools.selBest(pop, k=1)[0]
print(f"Best individual: {best_ind}, Fitness: {best_ind.fitness.values[0]}")