python立体库遗传算法

根据提供的引用内容，我了解到遗传算法是一种优化算法，而立体库遗传算法是一种特殊的遗传算法，用于解决立体库问题。在Python中，可以使用DEAP库来实现遗传算法。下面是一个简单的DEAP库实现立体库遗传算法的例子：

import random
from deap import base, creator, tools

# 定义立体库问题的适应度函数
def eval_func(individual):
    # 计算适应度值
    return sum(individual),

# 定义遗传算法的参数
POPULATION_SIZE = 100
P_CROSSOVER = 0.9
P_MUTATION = 0.1
MAX_GENERATIONS = 50
HALL_OF_FAME_SIZE = 10
toolbox = base.Toolbox()
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=10)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", eval_func)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)

# 运行遗传算法
population = toolbox.population(n=POPULATION_SIZE)
hall_of_fame = tools.HallOfFame(HALL_OF_FAME_SIZE)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", tools.mean)
stats.register("min", tools.min)
stats.register("max", tools.max)
logbook = tools.Logbook()
for gen in range(MAX_GENERATIONS):
    offspring = algorithms.varAnd(population, toolbox, P_CROSSOVER, P_MUTATION)
    fits = toolbox.map(toolbox.evaluate, offspring)
    for fit, ind in zip(fits, offspring):
        ind.fitness.values = fit
    population = toolbox.select(offspring, k=len(population))
    hall_of_fame.update(population)
    record = stats.compile(population)
    logbook.record(gen=gen, **record)
    print(logbook.stream)
best = hall_of_fame.items[0]
print('Best individual =', best)
print('Fitness =', best.fitness.values[0])

上述代码中，我们首先定义了立体库问题的适应度函数eval_func，然后定义了遗传算法的参数，包括种群大小、交叉概率、变异概率、最大迭代次数等。接着，我们使用DEAP库中的函数注册了遗传算法所需的各种操作，包括个体初始化、种群初始化、评估函数、交叉函数、变异函数、选择函数等。最后，我们使用DEAP库中的算法函数algorithms.varAnd运行遗传算法，并输出了最优解和适应度值。