matlab 遗传算法代码

根据提供的引用内容，我们可以了解到遗传算法是一种优化算法，可以用于求解复杂的问题。下面是一个简单的matlab遗传算法代码示例：

% 遗传算法求解函数最大值
function [x, y] = GA(f, lb, ub, n, m, pc, pm, maxgen)
% f: 目标函数
% lb: 自变量下界
% ub: 自变量上界
% n: 种群大小
% m: 自变量个数
% pc: 交叉概率
% pm: 变异概率
% maxgen: 最大迭代次数

% 初始化种群
pop = rand(n, m) .* (ub - lb) + lb;

% 迭代
for i = 1:maxgen
    % 计算适应度
    fit = feval(f, pop);
    % 选择
    newpop = sel(pop, fit);
    % 交叉
    newpop = cro(newpop, pc);
    % 变异
    newpop = mut(newpop, pm, lb, ub);
    % 更新种群
    pop = newpop;
end

% 计算最优解
fit = feval(f, pop);
[y, idx] = max(fit);
x = pop(idx, :);
end

% 选择操作
function newpop = sel(pop, fit)
% 轮盘赌选择
n = size(pop, 1);
p = fit ./ sum(fit);
psum = cumsum(p);
newpop = zeros(n, size(pop, 2));
for i = 1:n
    r = rand;
    for j = 1:n
        if r <= psum(j)
            newpop(i, :) = pop(j, :);
            break;
        end
    end
end
end

% 交叉操作
function newpop = cro(pop, pc)
% 单点交叉
n = size(pop, 1);
m = size(pop, 2);
newpop = zeros(n, m);
for i = 1:2:n
    if rand < pc
        k = randi([1, m-1]);
        newpop(i, :) = [pop(i, 1:k), pop(i+1, k+1:end)];
        newpop(i+1, :) = [pop(i+1, 1:k), pop(i, k+1:end)];
    else
        newpop(i, :) = pop(i, :);
        newpop(i+1, :) = pop(i+1, :);
    end
end
end

% 变异操作
function newpop = mut(pop, pm, lb, ub)
% 随机变异
n = size(pop, 1);
m = size(pop, 2);
newpop = pop;
for i = 1:n
    for j = 1:m
        if rand < pm
            newpop(i, j) = rand * (ub(j) - lb(j)) + lb(j);
        end
    end
end
end