遗传算法求解多目标python

遗传算法是一种优化算法，用于解决多目标问题。在Python中，你可以使用Python的库来实现遗传算法，比如DEAP（Distributed Evolutionary Algorithms in Python）库。DEAP是一个强大的遗传算法和进化计算框架，提供了丰富的功能和工具来解决各种优化问题。

下面是一个使用DEAP库求解多目标问题的简单示例：

import random
from deap import algorithms, base, creator, tools

# 定义多目标问题
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
creator.create("Individual", list, fitness=creator.FitnessMin)

# 初始化遗传算法参数
toolbox = base.Toolbox()
toolbox.register("attribute", random.random)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=2)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)

def evaluate(individual):
    # 计算目标函数值
    return sum(individual),

# 注册评估函数和交叉/变异操作
toolbox.register("evaluate", evaluate)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1)
toolbox.register("select", tools.selNSGA2)

def main():
    # 创建种群
    population = toolbox.population(n=50)
    
    # 评估初始种群
    fitnesses = toolbox.map(toolbox.evaluate, population)
    for ind, fit in zip(population, fitnesses):
        ind.fitness.values = fit
    
    # 开始进化
    num_generations = 50
    for gen in range(num_generations):
        # 选择下一代个体
        offspring = toolbox.select(population, len(population))