遗传算法matlab程序
时间: 2023-09-11 15:07:26 浏览: 34
以下是一个基本的遗传算法的MATLAB程序示例:
```matlab
% 遗传算法参数
pop_size = 100; % 种群大小
num_vars = 10; % 变量数目
num_generations = 50; % 迭代次数
mutation_rate = 0.01; % 变异率
crossover_rate = 0.8; % 交叉率
% 初始化种群
pop = rand(pop_size, num_vars);
for i = 1:num_generations
% 计算适应度函数
fitness = calculate_fitness(pop);
% 选择适应度较高的个体
parents = select_parents(pop, fitness);
% 交叉操作
offspring = crossover(parents, crossover_rate);
% 变异操作
offspring = mutate(offspring, mutation_rate);
% 将子代合并到种群中
pop = [pop; offspring];
% 保持种群大小不变
pop = keep_fittest(pop, pop_size);
end
% 输出最佳个体
best_ind = find(fitness == max(fitness));
disp(['Best individual found: ' num2str(pop(best_ind,:))]);
% 适应度函数计算
function fitness = calculate_fitness(pop)
fitness = sum(pop, 2);
end
% 选择操作
function parents = select_parents(pop, fitness)
[~, sorted_idx] = sort(fitness, 'descend');
parents = pop(sorted_idx(1:round(length(pop)*0.3)), :);
end
% 交叉操作
function offspring = crossover(parents, crossover_rate)
num_parents = size(parents, 1);
num_offspring = round(num_parents * crossover_rate);
offspring = zeros(num_offspring, size(parents, 2));
for i = 1:num_offspring
p1 = parents(randi(num_parents), :);
p2 = parents(randi(num_parents), :);
crossover_point = randi(size(parents, 2));
offspring(i, :) = [p1(1:crossover_point) p2(crossover_point+1:end)];
end
end
% 变异操作
function offspring = mutate(offspring, mutation_rate)
for i = 1:size(offspring, 1)
for j = 1:size(offspring, 2)
if rand < mutation_rate
offspring(i,j) = rand;
end
end
end
end
% 保持种群大小不变
function pop = keep_fittest(pop, pop_size)
[~, sorted_idx] = sort(calculate_fitness(pop), 'descend');
pop = pop(sorted_idx(1:pop_size), :);
end
```
请注意,以上示例程序只是一个基本的遗传算法框架,您需要根据具体的问题进行修改和调整。