请生成一段MOGA做特征选择的matlab代码

好的，以下是使用MOGA进行特征选择的Matlab示例代码：

% 生成数据集
rng(42);
X = randn(100, 10);
y = randi([0 1], 100, 1);

% 划分训练集和测试集
cv = cvpartition(y, 'HoldOut', 0.3);
X_train = X(cv.training,:);
y_train = y(cv.training,:);
X_test = X(cv.test,:);
y_test = y(cv.test,:);

% 定义特征选择问题
nvar = size(X_train, 2);
lb = zeros(1, nvar);
ub = ones(1, nvar);
nobj = 2;
FitnessFunction = @(x) FeatureSelection(x, X_train, y_train, X_test, y_test);
ncon = 0;
options = optimoptions('gamultiobj','Display','iter');

% 定义算法参数
popsize = 100;
crossover = @crossover_binary;
mutation = @mutation_bitflip;
selection = @selection_tournament;
generations = 50;

% 运行MOGA算法
[x, fval] = gamultiobj(FitnessFunction, nvar, [], [], [], [], lb, ub, ...
    options, 'PopulationSize', popsize, 'CrossoverFcn', crossover, ...
    'MutationFcn', mutation, 'SelectionFcn', selection, ...
    'Generations', generations);

% 输出结果
selected_features = find(x(end,:));
fprintf('Selected features: %s\n', mat2str(selected_features));
X_train_selected = X_train(:, selected_features);
X_test_selected = X_test(:, selected_features);
mdl = fitensemble(X_train_selected, y_train, 'Bag', 100, 'Tree', 'Type', 'Classification');
y_pred = predict(mdl, X_test_selected);
acc = sum(y_pred == y_test) / length(y_test);
fprintf('Accuracy: %.2f%%\n', acc * 100);

% 定义特征选择函数
function [f, c] = FeatureSelection(x, X_train, y_train, X_test, y_test)
    selected_features = find(x);
    X_train_selected = X_train(:, selected_features);
    X_test_selected = X_test(:, selected_features);
    mdl = fitensemble(X_train_selected, y_train, 'Bag', 100, 'Tree', 'Type', 'Classification');
    y_pred = predict(mdl, X_test_selected);
    acc = sum(y_pred == y_test) / length(y_test);
    f = [-acc, -sum(x)]; % 最小化分类准确率和特征数量
    c = [];
end

这段代码使用了MOGA算法来进行特征选择，目标是最小化分类准确率和特征数量。算法使用了二进制交叉、二进制位翻转变异和锦标赛选择运算子。最终输出选择的特征和分类准确率。注意，在Matlab中，需要将目标函数和约束函数的返回值分别赋给`f`和`c`变量。