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发表于 2004-9-9 18:48:38
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function [bestpop,trace]=ga(d,termops,num,pc,cxops,pm,alpha)
%
%————————————————————————
%[bestpop,trace]=ga(d,termops,num,pc,cxops,pm,alpha)
%d:距离矩阵
%termops:种群带数
%num:每带染色体的个数
%pc:交叉概率
%cxops:由于本程序采用单点交叉，交叉点的设置在本程序中没有很好的解决，所以本文了采用定点，即第cxops，可以随机产生。
%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
%------------------------------------------------
%####@@@##版权所有！欢迎广大网友改正，改进！##@@@####
%e-mail:tobysidney33@sohu.com
%####################################################
%
citynum=size(d,2);
n=nargin;
if n<2
disp('缺少变量！！')
disp('^_^开个玩笑^_^')
end
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=0.10;
end
if n<4
pc=0.25;
cxops=3;
pm=0.30;
alpha=0.10;
end
if n<5
cxops=3;
pm=0.30;
alpha=0.10;
end
if n<6
pm=0.30;
alpha=0.10;
end
if n<7
alpha=0.10;
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);
end
---------------------------------------------------------
function [t]=initializega(num,citynum)
for i=1:num
t(i,=randperm(citynum);
end
-----------------------------------------------------------
function [l]=f(d,t)
[m,n]=size(t);
for k=1:m
for i=1:n-1
l(k,i)=d(t(k,i),t(k,i+1));
end
l(k,n)=d(t(k,n),t(k,1));
l(k)=-sum(l(k,);
end
-----------------------------------------------------------
function [t]=select(t,l,alpha)
[m,n]=size(l);
t1=t;
[beforesort,aftersort1]=sort(l,2);%fsort from l to u
for i=1:n
aftersort(i)=aftersort1(n+1-i); %change
end
for k=1:n;
t(k,:)=t1(aftersort(k),:);
l1(k)=l(aftersort(k));
end
t1=t;
l=l1;
for i=1:size(aftersort,2)
evalv(i)=alpha*(1-alpha).^(i-1);
end
m=size(t,1);
q=cumsum(evalv);
qmax=max(q);
for k=1:m
r=qmax*rand(1);
for j=1:m
if j==1&r<=q(1)
t(k,:)=t1(1,:);
elseif j~=1&r>q(j-1)&r<=q(j)
t(k,:)=t1(j,:);
end
end
end
--------------------------------------------------
function [g]=grefenstette(t)
[m,n]=size(t);
for k=1:m
t0=1:n;
for i=1:n
for j=1:length(t0)
if t(k,i)==t0(j)
g(k,i)=j;
t0(j)=[];
break
end
end
end
end
-------------------------------------------
function [g]=crossover(g,pc,cxops)
[m,n]=size(g);
ran=rand(1,m);
r=cxops;
[x,ru]=find(ran<pc);
if ru>=2
for k=1:2:length(ru)-1
g1(ru(k),:)=[g(ru(k),[1:r]),g(ru(k+1),[(r+1):n])];
g(ru(k+1),:)=[g(ru(k+1),[1:r]),g(ru(k),[(r+1):n])];
g(ru(k),:)=g1(ru(k),:);
end
end
--------------------------------------------
function [g]=mutation(g,pm) %均匀变异
[m,n]=size(g);
ran=rand(1,m);
r=rand(1,3); %dai gai jin
rr=floor(n*rand(1,3)+1);
[x,mu]=find(ran<pm);
for k=1:length(mu)
for i=1:length(r)
umax(i)=n+1-rr(i);
umin(i)=1;
g(mu(k),rr(i))=umin(i)+floor((umax(i)-umin(i))*r(i));
end
end
---------------------------------------------------
function [t]=congrefenstette(g)
[m,n]=size(g);
for k=1:m
t0=1:n;
for i=1:n
t(k,i)=t0(g(k,i));
t0(g(k,i))=[];
end
end |
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发表于 2004-9-9 02:21:26
>[em01]最近做一个组合优化问题，但当要用到遗传算法时，就不会了</P>