基于遗传算法解决组卷问题，给出python代码

以下是一个简单的基于遗传算法解决组卷问题的Python代码示例：

import random

# 定义题目库和需要选择的题目数量
question_bank = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
num_questions = 5

# 定义遗传算法的参数
population_size = 10
mutation_rate = 0.1
generations = 100

# 定义适应度函数，计算一个个体的适应度
def fitness(individual):
    score = 0
    for question in individual:
        if question in ['A', 'B', 'C']:
            score += 3
        elif question in ['D', 'E']:
            score += 4
        elif question in ['F', 'G']:
            score += 5
        elif question == 'H':
            score += 6
        elif question == 'I':
            score += 7
    return score

# 创建初始种群
population = []
for i in range(population_size):
    individual = random.sample(question_bank, num_questions)
    population.append(individual)

# 进化过程
for generation in range(generations):
    # 计算每个个体的适应度
    fitness_scores = [fitness(individual) for individual in population]

    # 找到适应度最高的个体
    best_individual_index = fitness_scores.index(max(fitness_scores))
    best_individual = population[best_individual_index]
    print('Generation', generation, '- Best individual:', best_individual, '- Fitness score:', fitness_scores[best_individual_index])

    # 创建下一代种群
    next_population = []
    for i in range(population_size):
        # 选择两个个体进行交叉
        parent1 = random.choices(population, weights=fitness_scores)[0]
        parent2 = random.choices(population, weights=fitness_scores)[0]
        child = []
        for j in range(num_questions):
            # 随机选择一个父母的基因
            gene = random.choice([parent1[j], parent2[j]])
            # 以一定概率进行变异
            if random.random() < mutation_rate:
                gene = random.choice(question_bank)
            child.append(gene)
        next_population.append(child)

    # 更新种群
    population = next_population

# 找到最终适应度最高的个体
fitness_scores = [fitness(individual) for individual in population]
best_individual_index = fitness_scores.index(max(fitness_scores))
best_individual = population[best_individual_index]
print('Final result:', best_individual, '- Fitness score:', fitness_scores[best_individual_index])

请注意，此代码仅供参考，实际应用中可能需要根据不同的题目库和组卷需求进行修改和调整。