% 选择下一代种群 newpop = []; frontIndex = 1; while size(newpop, 1) + size(fronts{frontIndex}, 1) <= popsize % 对当前层个体按照拥挤度排序 [~, sortIndex] = sort(crowdingDist(fronts{frontIndex}), 'descend'); sortedFront = fronts{frontIndex}(sortIndex, :); newpop = [newpop; pop(sortedFront, :)]; frontIndex = frontIndex + 1; end % 处理最后一层个体 if size(newpop, 1) < popsize [~, sortIndex] = sort(crowdingDist(fronts{frontIndex}), 'descend'); sortedFront = fronts{frontIndex}(sortIndex, :); % 选择拥挤度大的个体 n = popsize - size(newpop, 1); newpop = [newpop; pop(sortedFront(1:n), :)]; end % 更新种群 pop = newpop; end这段代码如何定义为一个函数

可以将这段代码定义为一个函数，函数的输入参数为pop和popsize，输出参数为更新后的pop。

函数定义如下：

function pop = NSGAII_selection(pop, popsize)
    fronts = NSGAII_fast_non_dominated_sort(pop);
    crowdingDist = NSGAII_crowding_distance(pop, fronts);

    newpop = [];
    frontIndex = 1;

    while size(newpop, 1) + size(fronts{frontIndex}, 1) <= popsize
        [~, sortIndex] = sort(crowdingDist(fronts{frontIndex}), 'descend');
        sortedFront = fronts{frontIndex}(sortIndex, :);
        newpop = [newpop; pop(sortedFront, :)];
        frontIndex = frontIndex + 1;
    end

    if size(newpop, 1) < popsize
        [~, sortIndex] = sort(crowdingDist(fronts{frontIndex}), 'descend');
        sortedFront = fronts{frontIndex}(sortIndex, :);
        n = popsize - size(newpop, 1);
        newpop = [newpop; pop(sortedFront(1:n), :)];
    end

    pop = newpop;
end

在函数中，调用了两个子函数：`NSGAII_fast_non_dominated_sort`和`NSGAII_crowding_distance`，这两个函数需要根据具体问题进行实现。