遗传算法求解主从博弈代码

根据提供的引用内容，我了解到你对遗传算法求解主从博弈代码感兴趣。遗传算法是一种优化算法，它模拟了生物进化的过程，通过不断迭代和选择来寻找最优解。在主从博弈中，遗传算法可以用来优化策略或者寻找最佳的博弈方案。

以下是一个使用遗传算法求解主从博弈的简单示例代码：

import random

# 定义主从博弈的策略空间
strategy_space = ['C', 'D']

# 定义遗传算法的参数
population_size = 100  # 种群大小
max_generations = 100  # 最大迭代次数
mutation_rate = 0.1  # 变异率

# 定义主从博弈的收益矩阵
payoff_matrix = {
    ('C', 'C'): (3, 3),
    ('C', 'D'): (0, 5),
    ('D', 'C'): (5, 0),
    ('D', 'D'): (1, 1)
}

# 定义个体类
class Individual:
    def __init__(self, strategy):
        self.strategy = strategy
        self.fitness = 0

    def calculate_fitness(self):
        self.fitness = 0
        for opponent_strategy in strategy_space:
            payoff = payoff_matrix[(self.strategy, opponent_strategy)]
            self.fitness += payoff[0]

    def mutate(self):
        if random.random() < mutation_rate:
            self.strategy = random.choice(strategy_space)

# 初始化种群
population = []
for _ in range(population_size):
    strategy = random.choice(strategy_space)
    individual = Individual(strategy)
    population.append(individual)

# 迭代优化
for generation in range(max_generations):
    # 计算适应度
    for individual in population:
        individual.calculate_fitness()

    # 选择下一代
    population.sort(key=lambda x: x.fitness, reverse=True)
    elite = population[:int(population_size * 0.2)]
    offspring = elite.copy()

    while len(offspring) < population_size:
        parent1 = random.choice(elite)
        parent2 = random.choice(elite)
        child_strategy = random.choice([parent1.strategy, parent2.strategy])
        child = Individual(child_strategy)
        child.mutate()
        offspring.append(child)

    population = offspring

# 输出最优策略
best_individual = max(population, key=lambda x: x.fitness)
print("Best strategy:", best_individual.strategy)

这段代码使用遗传算法来求解主从博弈的最优策略。首先定义了主从博弈的策略空间和收益矩阵。然后定义了个体类，包含策略和适应度属性，并实现了计算适应度和变异的方法。接下来初始化种群，并进行迭代优化，每一代都计算个体的适应度，选择下一代，并进行变异操作。最后输出最优策略。