用遗传算法解TSP问题(matlab)

function [bestpop,trace]=ga(D,termOps,num,pc,cxOps,pm,alpha)
% D:距离矩阵
% termOps:种群代数
% num：每代染色体的个数
% pc 交叉概率
% cxOps：交叉点，随机产生
% pm：变异概率
% alpha：评价函数 eval（Vi）=alpha*(1-alpha).^(i-1);
% bestpop:返回的最优种群
% trace:进化轨迹
%---------------------------------
citynum=size(D,2); % 城市数目
n=nargin;
if n<2
    termOps=500;
    num=50;
    pc=0.25;
    cxOps=3;
    pm=0.30;
    alpha=0.10;
end
if n<3
    num=50;
    pc=0.25;
    cxOps=3;
    pm=0.30;
    alpha=0.1;
end
if n<4
    pc=0.25;

    cxOps=3;
    pm=0.30;
    alpha=0.10;
end
if n<5
    cxOps=3;
    pm=0.30;
    alhpa=0.1;
end
if n<6
    pm=0.30;
    alpha=0.1;
end
if n<7
    alpha=0.1;
end
if isempty(cxOps)
    cxOps=3;
end
[T]=initializega(num,citynum);
for i=1:termOps
    [L]=f(D,T);
    [x,y]=find(L==max(L));
    trace(i)=-L(y(1));
    bestpop=T(y(1),:);
    [T]=select(T,L,alpha);
    [G]=grefenstette(T);
    [G1]=crossover(G,pc,cxOps);
    [G]=mutation(G1,pm);     %均匀变异
    [T]=congrefenstette(G);