基于遗传算法的自动组卷系统代码

遗传算法是一种优化算法，可以用于自动组卷系统。以下是一个基于遗传算法的自动组卷系统的代码示例：

import random

class Exam:
    def __init__(self, questions, num_questions, exam_length):
        self.questions = questions
        self.num_questions = num_questions
        self.exam_length = exam_length
        self.fitness = 0

    def __repr__(self):
        return f"Exam - Fitness: {self.fitness}, Questions: {self.questions}"

class GeneticAlgorithm:
    def __init__(self, population_size, mutation_rate, crossover_rate, elitism):
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate
        self.elitism = elitism

    def init_population(self, num_questions, exam_length):
        population = []
        for i in range(self.population_size):
            questions = random.sample(range(num_questions), exam_length)
            exam = Exam(questions, num_questions, exam_length)
            population.append(exam)
        return population

    def evaluate_fitness(self, population):
        for exam in population:
            fitness = 0
            for i in range(exam.num_questions):
                if i in exam.questions:
                    fitness += 1
            exam.fitness = fitness

    def select_parents(self, population):
        parents = []
        for i in range(self.elitism):
            parents.append(max(population, key=lambda x: x.fitness))
        for i in range(self.population_size - self.elitism):
            parent1 = random.choice(population)
            parent2 = random.choice(population)
            if parent1.fitness > parent2.fitness:
                parents.append(parent1)
            else:
                parents.append(parent2)
        return parents

    def crossover(self, parents):
        children = []
        for i in range(0, len(parents), 2):
            parent1 = parents[i]
            parent2 = parents[i+1]
            if random.random() < self.crossover_rate:
                crossover_point = random.randint(1, parent1.exam_length - 1)
                child1 = Exam(parent1.questions[:crossover_point] + parent2.questions[crossover_point:], parent1.num_questions, parent1.exam_length)
                child2 = Exam(parent2.questions[:crossover_point] + parent1.questions[crossover_point:], parent2.num_questions, parent2.exam_length)
                children.append(child1)
                children.append(child2)
            else:
                children.append(parent1)
                children.append(parent2)
        return children

    def mutate(self, population):
        for exam in population:
            for i in range(exam.exam_length):
                if random.random() < self.mutation_rate:
                    exam.questions[i] = random.randint(0, exam.num_questions - 1)

    def evolve_population(self, population):
        parents = self.select_parents(population)
        children = self.crossover(parents)
        self.mutate(children)
        population = parents + children
        self.evaluate_fitness(population)
        return population

    def get_best(self, population):
        return max(population, key=lambda x: x.fitness)

# Example usage
genetic_algorithm = GeneticAlgorithm(population_size=100, mutation_rate=0.01, crossover_rate=0.7, elitism=2)
population = genetic_algorithm.init_population(num_questions=50, exam_length=10)
genetic_algorithm.evaluate_fitness(population)
best_exam = None
for i in range(100):
    population = genetic_algorithm.evolve_population(population)
    best_exam = genetic_algorithm.get_best(population)
    print(f"Generation {i+1}: {best_exam}")

这个代码实现了一个自动组卷系统，使用遗传算法优化试卷的组成。在初始化时，我们随机生成一定数量的试卷，并使用评估函数计算它们的适应度。然后，我们使用选择、交叉和变异操作来进化试卷的种群。在每个进化迭代中，我们根据适应度评估种群，并选择最佳试卷。最终，我们得到了最好的试卷。