遗传算法求解迷宫寻宝藏问题，MATLAB代码

以下是一个用MATLAB实现遗传算法求解迷宫寻宝藏问题的代码：

% 迷宫地图
maze = [
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0;
    0, 1, 1, 1, 1, 1, 0, 0, 0, 0;
    0, 0, 0, 0, 0, 1, 0, 0, 0, 0;
    0, 0, 0, 1, 0, 1, 1, 1, 1, 0;
    0, 0, 0, 1, 0, 0, 0, 0, 1, 0;
    0, 0, 0, 1, 1, 1, 1, 0, 1, 0;
    0, 0, 0, 0, 0, 0, 1, 0, 1, 0;
    0, 0, 0, 1, 1, 1, 1, 0, 1, 0;
    0, 0, 0, 1, 0, 0, 0, 0, 1, 0;
    0, 0, 0, 1, 1, 1, 1, 1, 1, 0;
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0
];

% 起点和终点
start_pos = [2, 2];
end_pos = [10, 9];

% 移动操作
moves = {'up', 'down', 'left', 'right'};

% 遗传算法参数
POPULATION_SIZE = 50;
GENERATIONS = 100;
MUTATION_RATE = 0.1;

% 生成随机个体
function individual = generate_individual()
    individual = randi(4, 1, 100);
end

% 计算适应度
function fitness = calculate_fitness(individual)
    x = start_pos(2);
    y = start_pos(1);
    for i = 1:length(individual)
        if strcmp(individual(i), 'up')
            y = y - 1;
        elseif strcmp(individual(i), 'down')
            y = y + 1;
        elseif strcmp(individual(i), 'left')
            x = x - 1;
        elseif strcmp(individual(i), 'right')
            x = x + 1;
        end
        if isequal([y, x], end_pos)
            fitness = 1;
            return;
        end
        if maze(y, x) == 1
            fitness = 0;
            return;
        end
    end
    fitness = 0;
end

% 选择操作
function parents = selection(population)
    fitnesses = zeros(1, length(population));
    for i = 1:length(population)
        fitnesses(i) = calculate_fitness(population{i});
    end
    total_fitness = sum(fitnesses);
    probabilities = fitnesses / total_fitness;
    parents = cell(1, 2);
    for i = 1:2
        idx = randsample(length(population), 1, true, probabilities);
        parents{i} = population{idx};
    end
end

% 交叉操作
function children = crossover(parents)
    crossover_point = randi(99);
    child1 = [parents{1}(1:crossover_point), parents{2}(crossover_point+1:end)];
    child2 = [parents{2}(1:crossover_point), parents{1}(crossover_point+1:end)];
    children = {child1, child2};
end

% 变异操作
function individual = mutation(individual)
    for i = 1:length(individual)
        if rand() < MUTATION_RATE
            individual(i) = randi(4);
        end
    end
end

% 遗传算法主函数
function genetic_algorithm()
    population = cell(1, POPULATION_SIZE);
    for i = 1:POPULATION_SIZE
        population{i} = generate_individual();
    end
    for generation = 1:GENERATIONS
        new_population = cell(1, POPULATION_SIZE);
        for i = 1:2:POPULATION_SIZE
            parents = selection(population);
            children = crossover(parents);
            children{1} = mutation(children{1});
            children{2} = mutation(children{2});
            new_population{i} = children{1};
            new_population{i+1} = children{2};
        end
        population = new_population;
        fitnesses = zeros(1, length(population));
        for i = 1:length(population)
            fitnesses(i) = calculate_fitness(population{i});
        end
        [~, idx] = max(fitnesses);
        best_individual = population{idx};
        fprintf('Generation: %d, Best individual: %s, Fitness: %d\n', generation, mat2str(best_individual), calculate_fitness(best_individual));
    end
end

% 运行遗传算法
genetic_algorithm();

代码说明：

1. 首先定义了迷宫地图、起点和终点、移动操作等必要的变量。

2. 然后定义了几个遗传算法的基本操作函数，包括生成随机个体、计算适应度、选择、交叉、变异等。

3. 最后定义了遗传算法主函数，其中首先生成初始种群，然后进行多代进化，每一代都进行选择、交叉、变异等操作，最后输出每一代的最佳个体和适应度。

4. 运行遗传算法函数即可得到寻宝的最优路径。