请给我一个开源的遗传算法matlab代码

以下是一个基于遗传算法的matlab代码：

% 遗传算法参数设置 generation_size = 100; % 种群大小 chromosome_length = 20; % 染色体长度 crossover_rate = 0.8; % 交叉率 mutation_rate = 0.01; % 变异率 max_generation = 200; % 最大迭代次数

% 初始化种群 population = randi([0, 1], generation_size, chromosome_length);

% 计算适应度 fitness = zeros(generation_size, 1); for i = 1:generation_size fitness(i) = sum(population(i, :)); end

% 进化 for generation = 1:max_generation % 选择 selected_population = zeros(generation_size, chromosome_length); for i = 1:generation_size % 轮盘赌选择 selected_population(i, :) = population(find(rand <= cumsum(fitness/sum(fitness)), 1), :); end

% 交叉
crossover_population = zeros(generation_size, chromosome_length);
for i = 1:2:generation_size
    if rand <= crossover_rate
        % 单点交叉
        crossover_point = randi([2, chromosome_length-1]);
        crossover_population(i, :) = [selected_population(i, 1:crossover_point), selected_population(i+1, crossover_point+1:end)];
        crossover_population(i+1, :) = [selected_population(i+1, 1:crossover_point), selected_population(i, crossover_point+1:end)];
    else
        crossover_population(i, :) = selected_population(i, :);
        crossover_population(i+1, :) = selected_population(i+1, :);
    end
end

% 变异
mutation_population = crossover_population;
for i = 1:generation_size
    for j = 1:chromosome_length
        if rand <= mutation_rate
            % 按位取反
            mutation_population(i, j) = ~mutation_population(i, j);
        end
    end
end

% 计算适应度
fitness = zeros(generation_size, 1);
for i = 1:generation_size
    fitness(i) = sum(mutation_population(i, :));
end

% 更新种群
population = mutation_population;

% 输出结果
fprintf('Generation %d: Best fitness = %d\n', generation, max(fitness));

end

% 最优解 [~, best_index] = max(fitness); best_chromosome = population(best_index, :); fprintf('Best solution found: '); disp(best_chromosome)