an ai structure

import random

# 遗传算法的基本参数
POPULATION_SIZE = 100
MUTATION_RATE = 0.1
NUM_GENERATIONS = 10

# 代码和诗歌的范围
CODE_RANGE = (0, 255)
POEM_RANGE = ('a', 'z')

# 评估函数，用于评估代码和诗歌的质量
def evaluate_code(code):
    # TODO: 实现评估函数，返回一个得分
    return 0

def evaluate_poem(poem):
    # TODO: 实现评估函数，返回一个得分
    return 0

# 随机生成初始种群
def generate_population(size):
    population = []
    for i in range(size):
        code = ''.join(chr(random.randint(*CODE_RANGE)) for _ in range(100))
        poem = ''.join(random.choice(POEM_RANGE) for _ in range(100))
        population.append((code, poem))
    return population

# 选择操作，根据评估函数的得分选择优秀的个体
def selection(population):
    scores = [evaluate_code(code) + evaluate_poem(poem) for code, poem in population]
    selected = []
    for i in range(len(population)):
        p1 = random.choices(population, weights=scores)[0]
        p2 = random.choices(population, weights=scores)[0]
        selected.append((p1[0], p2[1]))
    return selected

# 变异操作，随机改变个体的基因
def mutation(population):
    mutated = []
    for code, poem in population:
        if random.random() < MUTATION_RATE:
            new_code = ''.join(chr(random.randint(*CODE_RANGE)) for _ in range(100))
            new_poem = ''.join(random.choice(POEM_RANGE) for _ in range(100))
            mutated.append((new_code, new_poem))
        else:
            mutated.append((code, poem))
    return mutated

# 主函数，运行遗传算法
def run_ga():
    population = generate_population(POPULATION_SIZE)
    for i in range(NUM_GENERATIONS):
        population = selection(population)
        population = mutation(population)
    best = max(population, key=lambda x: evaluate_code(x[0]) + evaluate_poem(x[1]))
    return best

# 测试
best = run_ga()
print(best[0])
print(best[1])