遗传算法matlab程序
时间: 2023-10-15 08:05:14 浏览: 19
以下是一个简单的遗传算法matlab程序示例:
```matlab
% 遗传算法参数设置
pop_size = 50; % 种群大小
chrom_length = 20; % 染色体长度
pc = 0.8; % 交叉概率
pm = 0.01; % 变异概率
generation = 100; % 迭代次数
% 初始化种群
pop = round(rand(pop_size, chrom_length));
for i = 1:generation
% 计算个体适应度
fitness = sum(pop, 2);
% 选择操作
idx = roulette_wheel_selection(fitness);
parent_pop = pop(idx, :);
% 交叉操作
child_pop = crossover(parent_pop, pc);
% 变异操作
child_pop = mutation(child_pop, pm);
% 更新种群
pop = [parent_pop; child_pop];
end
% 打印最优解
best_fitness = max(sum(pop, 2));
disp(['最优解:', num2str(best_fitness)]);
% 轮盘赌选择函数
function idx = roulette_wheel_selection(fitness)
total_fitness = sum(fitness);
fitness_prob = fitness / total_fitness;
cum_prob = cumsum(fitness_prob);
idx = zeros(size(fitness));
for i = 1:length(fitness)
r = rand();
for j = 1:length(cum_prob)
if r <= cum_prob(j)
idx(i) = j;
break;
end
end
end
end
% 交叉函数
function child_pop = crossover(parent_pop, pc)
[m, n] = size(parent_pop);
child_pop = zeros(m, n);
for i = 1:2:m
if rand() < pc
% 随机选择交叉点
point = round(rand() * (n - 1)) + 1;
child_pop(i, :) = [parent_pop(i, 1:point), parent_pop(i+1, point+1:end)];
child_pop(i+1, :) = [parent_pop(i+1, 1:point), parent_pop(i, point+1:end)];
else
child_pop(i, :) = parent_pop(i, :);
child_pop(i+1, :) = parent_pop(i+1, :);
end
end
end
% 变异函数
function child_pop = mutation(child_pop, pm)
[m, n] = size(child_pop);
for i = 1:m
for j = 1:n
if rand() < pm
child_pop(i, j) = 1 - child_pop(i, j);
end
end
end
end
```
该程序实现了一个简单的遗传算法,用于求解一个二进制字符串中1的个数最大的问题。在程序中,种群大小为50,染色体长度为20,交叉概率为0.8,变异概率为0.01,迭代100次。在每次迭代中,先计算个体适应度,然后执行选择、交叉、变异操作,最后更新种群。最终输出最优解。
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