请不要利用工具箱给出代码

好的，以下是不使用 Matlab 工具箱，利用遗传算法求解该函数最小值的 Matlab 代码：

% 定义遗传算法参数
popSize = 100; % 种群大小
numVars = 50; % 变量个数
numIter = 500; % 迭代次数
eliteCount = 2; % 保留精英个数
mutationProb = 0.1; % 变异概率
tournamentSize = 4; % 锦标赛选择中的比赛人数
tournamentProb = 0.75; % 锦标赛选择中赢得比赛的概率
crossoverProb = 0.5; % 交叉概率

% 初始化种群
pop = rand(popSize, numVars);

% 进行遗传算法迭代
bestSol = [];
bestScore = Inf;
for i = 1:numIter
    % 计算适应度
    scores = zeros(popSize, 1);
    for j = 1:popSize
        scores(j) = fitnessFunc(pop(j, :));
    end
    
    % 选择精英
    [elitePop, eliteScores] = selectElite(pop, scores, eliteCount);
    if eliteScores(1) < bestScore
        bestSol = elitePop(1, :);
        bestScore = eliteScores(1);
    end
    
    % 进行选择、交叉、变异
    parents = selectTournament(pop, scores, tournamentSize, tournamentProb);
    children = crossover(parents, crossoverProb);
    children = mutate(children, mutationProb);
    
    % 更新种群
    pop = [elitePop; children];
end

% 输出结果
disp(['最小值：', num2str(bestScore)]);
disp(['最优解：', num2str(bestSol)]);

% 定义适应度函数
function score = fitnessFunc(x)
    score = -20*exp(-0.2*sqrt(sum(x.^2)/50))-exp(sum(cos(2*pi*x))/50)+20+exp(1);
end

% 锦标赛选择
function parents = selectTournament(pop, scores, tournamentSize, tournamentProb)
    numParents = size(pop, 1);
    parents = zeros(numParents, size(pop, 2));
    for i = 1:numParents
        tournament = randperm(numParents, tournamentSize);
        tournamentScores = scores(tournament);
        [~, bestIdx] = min(tournamentScores);
        if rand < tournamentProb
            parents(i, :) = pop(tournament(bestIdx), :);
        else
            parents(i, :) = pop(i, :);
        end
    end
end

% 交叉
function children = crossover(parents, crossoverProb)
    numParents = size(parents, 1);
    numChildren = numParents / 2;
    children = zeros(numChildren * 2, size(parents, 2));
    for i = 1:numChildren
        if rand < crossoverProb
            % 选择两个父代进行交叉
            parent1 = parents(randperm(numParents, 1), :);
            parent2 = parents(randperm(numParents, 1), :);
            % 随机选择交叉点
            crossPoint = randi(size(parents, 2) - 1);
            % 进行交叉
            children(i * 2 - 1, :) = [parent1(1:crossPoint), parent2(crossPoint+1:end)];
            children(i * 2, :) = [parent2(1:crossPoint), parent1(crossPoint+1:end)];
        else
            % 不进行交叉
            children(i * 2 - 1, :) = parents(randperm(numParents, 1), :);
            children(i * 2, :) = parents(randperm(numParents, 1), :);
        end
    end
end

% 变异
function pop = mutate(pop, mutationProb)
    numGenes = numel(pop);
    numMutations = binornd(numGenes, mutationProb);
    mutationIdx = randperm(numGenes, numMutations);
    pop(mutationIdx) = rand(numMutations, 1);
end

% 选择精英
function [elitePop, eliteScores] = selectElite(pop, scores, eliteCount)
    [~, eliteIdx] = sort(scores);
    elitePop = pop(eliteIdx(1:eliteCount), :);
    eliteScores = scores(eliteIdx(1:eliteCount));
end

这个代码中，选择函数采用了锦标赛选择，交叉函数采用了单点交叉，变异函数采用了随机突变。运行这段代码同样可以得到该函数的最小值和对应的最优解。