MATLAB实现评价函数法：用遗传算法优化Rosenbrock函数

以下是一个用MATLAB编写的关于评价函数法的代码示例： ```matlab % 定义评价函数 function obj_val = evaluation_func(x) % 评价函数为 Rosenbrock 函数 obj_val = 100 * (x(2) - x(1)^2)^2 + (1 - x(1))^2; end % 主函数 function main() % 初始化参数 pop_size = 50; % 种群大小 max_gen = 100; % 最大迭代次数 dim = 2; % 解向量的维度 lb = -10; % 解向量的下界 ub = 10; % 解向量的上界 % 初始化种群 pop = lb + (ub-lb) * rand(pop_size, dim); % 初始化最优解 best_solution = pop(1, :); best_fitness = evaluation_func(best_solution); % 迭代优化 for gen = 1:max_gen % 计算适应度值 fitness = arrayfun(@evaluation_func, pop); % 更新最优解 [max_fitness, max_index] = max(fitness); if max_fitness > best_fitness best_fitness = max_fitness; best_solution = pop(max_index, :); end % 选择操作 probability = fitness / sum(fitness); cum_prob = cumsum(probability); selection_index = zeros(pop_size, 1); for i = 1:pop_size rand_num = rand(); for j = 1:pop_size if rand_num <= cum_prob(j) selection_index(i) = j; break; end end end selected_pop = pop(selection_index, :); % 交叉操作 child_pop = zeros(pop_size, dim); for i = 1:2:pop_size-1 cross_prob = rand(); if cross_prob <= 0.8 cross_site = randi([2,dim-1]); child_pop(i,:) = [selected_pop(i,1:cross_site), selected_pop(i+1,cross_site+1:end)]; child_pop(i+1,:) = [selected_pop(i+1,1:cross_site), selected_pop(i,cross_site+1:end)]; else child_pop(i,:) = selected_pop(i,:); child_pop(i+1,:) = selected_pop(i+1,:); end end % 变异操作 for i = 1:pop_size mutate_prob = rand(); if mutate_prob <= 0.01 mutate_site = randi([1,dim]); child_pop(i, mutate_site) = lb + (ub-lb) * rand(); end end % 更新种群 pop = child_pop; end % 输出结果 disp('Best Solution:'); disp(best_solution); disp('Best Fitness:'); disp(best_fitness); end % 运行主函数 main(); ``` 这个代码实现了一个简单的遗传算法来优化评价函数Rosenbrock函数。主要包括初始化种群、计算适应度值、选择、交叉和变异操作等步骤。运行主函数`main()`后，会输出最优解和最优适应度值。你可以根据需要修改评价函数、参数设置和操作等部分来适应其他的评价函数。 ######[AI写代码神器 | 1813点数解答 | 2024-10-22 16:14:10]