人工智能八段码难题解决代码

以下是一个使用遗传算法解决八段码游戏的代码示例：

import random

POP_SIZE = 100
GENE_SIZE = 8
MUTATION_RATE = 0.01
CROSSOVER_RATE = 0.9
MAX_GENERATION = 100

def generate_individual():
    return [random.randint(0, 9) for _ in range(GENE_SIZE)]

def generate_population():
    return [generate_individual() for _ in range(POP_SIZE)]

def calculate_fitness(individual, target):
    return sum([1 for i in range(GENE_SIZE) if individual[i] == target[i]])

def evaluate_population(population, target):
    fitnesses = [calculate_fitness(individual, target) for individual in population]
    max_fitness = max(fitnesses)
    best_individual = population[fitnesses.index(max_fitness)]
    return fitnesses, max_fitness, best_individual

def select_parents(population, fitnesses):
    total_fitness = sum(fitnesses)
    probabilities = [fitness/total_fitness for fitness in fitnesses]
    parents = []
    for i in range(2):
        r = random.random()
        cumulative_probability = 0
        for j in range(POP_SIZE):
            cumulative_probability += probabilities[j]
            if r <= cumulative_probability:
                parents.append(population[j])
                break
    return parents

def crossover(parents):
    if random.random() < CROSSOVER_RATE:
        crossover_point = random.randint(1, GENE_SIZE - 1)
        child1 = parents[0][:crossover_point] + parents[1][crossover_point:]
        child2 = parents[1][:crossover_point] + parents[0][crossover_point:]
        return child1, child2
    else:
        return parents

def mutation(individual):
    if random.random() < MUTATION_RATE:
        mutation_point = random.randint(0, GENE_SIZE - 1)
        individual[mutation_point] = random.randint(0, 9)
    return individual

def evolve_population(population, target):
    new_population = []
    fitnesses, _, _ = evaluate_population(population, target)
    for _ in range(POP_SIZE):
        parents = select_parents(population, fitnesses)
        offspring = crossover(parents)
        offspring = [mutation(individual) for individual in offspring]
        new_population += offspring
    return new_population

def solve(target):
    population = generate_population()
    for generation in range(MAX_GENERATION):
        fitnesses, max_fitness, best_individual = evaluate_population(population, target)
        if max_fitness == GENE_SIZE:
            return best_individual, generation
        population = evolve_population(population, target)
    return None, MAX_GENERATION

if __name__ == '__main__':
    target = [1, 2, 3, 4, 5, 6, 7, 8]
    solution, generation = solve(target)
    if solution:
        print(f'Solution found in {generation} generations: {solution}')
    else:
        print('Solution not found')

这个代码使用遗传算法来解决八段码游戏。在每一代中，它先使用 `evaluate_population` 函数计算每个个体的适应度，然后使用 `select_parents` 函数选择父代，使用 `crossover` 函数进行交叉操作，使用 `mutation` 函数进行变异操作，最后生成新的子代。这个过程重复多次，直到找到解或达到最大迭代次数。

需要注意的是，这个代码只是一个基本示例，实际使用时可能需要进行一些调整和优化，比如使用不同的遗传算法操作、调整参数等。