用最好理解的方式给我一个matlab遗传算法的代码示例，条件要多一些的

当然，我可以给你一个简单的 MATLAB 遗传算法代码示例，其中包含多个条件。以下是一个基于遗传算法求解函数最小值的示例代码：

% 遗传算法参数
popSize = 50; % 种群大小
numGenes = 10; % 染色体长度
numIterations = 100; % 迭代次数
mutationRate = 0.02; % 变异率

% 初始化种群
population = randi([0, 1], popSize, numGenes);

% 迭代
for iter = 1:numIterations
    % 计算适应度函数值
    fitness = calculateFitness(population);
    
    % 选择操作：轮盘赌选择
    selectedIndices = rouletteWheelSelection(fitness);
    selectedPopulation = population(selectedIndices, :);
    
    % 染色体交叉
    offspringPopulation = crossover(selectedPopulation);
    
    % 变异操作
    mutatedPopulation = mutation(offspringPopulation, mutationRate);
    
    % 更新种群
    population = mutatedPopulation;
end

% 计算最优解
fitness = calculateFitness(population);
[~, bestIndex] = min(fitness);
bestSolution = population(bestIndex, :);

% 输出结果
disp('最优解:');
disp(bestSolution);

% 定义适应度函数（示例函数为 Rosenbrock 函数）
function fitness = calculateFitness(population)
    [numIndividuals, ~] = size(population);
    fitness = zeros(numIndividuals, 1);
    
    for i = 1:numIndividuals
        x = bi2de(population(i, :));
        fitness(i) = (1 - x(1))^2 + 100 * (x(2) - x(1)^2)^2;＊＊＊