遗传算法matlab代码 AGA.docx
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遗传算法matlab代码 AGA.docx
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遗传算法matlab代码AGA
遗传算法matlab代码
2008-02-1116:
13
functionyouhuafun
D=code;
N=50; %Tunable
maxgen=50; %Tunable
crossrate=0.5;%Tunable
muterate=0.08;%Tunable
generation=1;
num=length(D);
fatherrand=randint(num,N,3);
score=zeros(maxgen,N);
whilegeneration<=maxgen
ind=randperm(N-2)+2;%随机配对交叉
A=fatherrand(:
ind(1:
(N-2)/2));
B=fatherrand(:
ind((N-2)/2+1:
end));
% 多点交叉
rnd=rand(num,(N-2)/2);
ind=rnd tmp=A(ind);
A(ind)=B(ind);
B(ind)=tmp;
%%两点交叉
% forkk=1:
(N-2)/2
% rndtmp=randint(1,1,num)+1;
% tmp=A(1:
rndtmp,kk);
% A(1:
rndtmp,kk)=B(1:
rndtmp,kk);
% B(1:
rndtmp,kk)=tmp;
% end
fatherrand=[fatherrand(:
1:
2),A,B];
%变异
rnd=rand(num,N);
ind=rnd [m,n]=size(ind);
tmp=randint(m,n,2)+1;
tmp(:
1:
2)=0;
fatherrand=tmp+fatherrand;
fatherrand=mod(fatherrand,3);
% fatherrand(ind)=tmp;
%评价、选择
scoreN=scorefun(fatherrand,D);%求得N个个体的评价函数
score(generation,:
)=scoreN;
[scoreSort,scoreind]=sort(scoreN);
sumscore=cumsum(scoreSort);
sumscore=sumscore./sumscore(end);
childind(1:
2)=scoreind(end-1:
end);
fork=3:
N
tmprnd=rand;
tmpind=tmprnd difind=[0,diff(tmpind)];
if~any(difind)
difind
(1)=1;
end
childind(k)=scoreind(logical(difind));
end
fatherrand=fatherrand(:
childind);
generation=generation+1;
end
%score
maxV=max(score,[],2);
minV=11*300-maxV;
plot(minV,‘*‘);title(‘各代的目标函数值‘);
F4=D(:
4);
FF4=F4-fatherrand(:
1);
FF4=max(FF4,1);
D(:
5)=FF4;
saveDDataD
functionD=code
loadyouhua.mat
%propertiesF2andF3
F1=A(:
1);
F2=A(:
2);
F3=A(:
3);
if(max(F2)>1450)||(min(F2)<=900)
error(‘DATApropertyF2exceedit‘‘srange(900,1450]‘)
end
%getgrouppropertyF1ofdata,accordingtoF2value
F4=zeros(size(F1));
forite=11:
-1:
1
index=find(F2<=900+ite*50);
F4(index)=ite;
end
D=[F1,F2,F3,F4];
functionScoreN=scorefun(fatherrand,D)
F3=D(:
3);
F4=D(:
4);
N=size(fatherrand,2);
FF4=F4*ones(1,N);
FF4rnd=FF4-fatherrand;
FF4rnd=max(FF4rnd,1);
ScoreN=ones(1,N)*300*11;
%这里有待优化
fork=1:
N
FF4k=FF4rnd(:
k);
forite=1:
11
F0index=find(FF4k==ite);
if~isempty(F0index)
tmpMat=F3(F0index);
tmpSco=sum(tmpMat);
ScoreBin(ite)=mod(tmpSco,300);
end
end
Scorek(k)=sum(ScoreBin);
end
ScoreN=ScoreN-Scorek;
遗传算法程序matlab
2006年12月09日星期六20:
53
遗传算法程序
本程序收集于网络,本人并未进行过运行,如有问题请与作者联系,如有侵权请告之
遗传算法程序:
说明:
fga.m为遗传算法的主程序;采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择,均匀交叉,变异操作,而且还引入了倒位操作!
function[BestPop,Trace]=fga(FUN,LB,UB,eranum,popsize,pCross,pMutation,pInversion,options)
%[BestPop,Trace]=fmaxga(FUN,LB,UB,eranum,popsize,pcross,pmutation)
%Findsa maximumofafunctionofseveralvariables.
%fmaxgasolvesproblemsoftheform:
% maxF(X) subjectto:
LB<=X<=UB
% BestPop -最优的群体即为最优的染色体群
% Trace -最佳染色体所对应的目标函数值
% FUN -目标函数
% LB -自变量下限
% UB -自变量上限
% eranum -种群的代数,取100--1000(默认200)
% popsize -每一代种群的规模;此可取50--200(默认100)
% pcross -交叉概率,一般取0.5--0.85之间较好(默认0.8)
% pmutation -初始变异概率,一般取0.05-0.2之间较好(默认0.1)
% pInversion -倒位概率,一般取0.05-0.3之间较好(默认0.2)
% options -1*2矩阵,options
(1)=0二进制编码(默认0),option
(1)~=0十进制编
%码,option
(2)设定求解精度(默认1e-4)
%
% ------------------------------------------------------------------------
T1=clock;
ifnargin<3,error(‘FMAXGArequiresatleastthreeinputarguments‘);end
ifnargin==3,eranum=200;popsize=100;pCross=0.8;pMutation=0.1;pInversion=0.15;options=[01e-4];end
ifnargin==4,popsize=100;pCross=0.8;pMutation=0.1;pInversion=0.15;options=[01e-4];end
ifnargin==5,pCross=0.8;pMutation=0.1;pInversion=0.15;options=[01e-4];end
ifnargin==6,pMutation=0.1;pInversion=0.15;options=[01e-4];end
ifnargin==7,pInversion=0.15;options=[01e-4];end
iffind((LB-UB)>0)
error(‘数据输入错误,请重新输入(LB ‘); end s=sprintf(‘程序运行需要约%.4f秒钟时间,请稍等......‘,(eranum*popsize/1000)); disp(s); globalmnNewPopchildren1children2VarNum bounds=[LB;UB]‘;bits=[];VarNum=size(bounds,1); precision=options (2);%由求解精度确定二进制编码长度 bits=ceil(log2((bounds(: 2)-bounds(: 1))‘./precision));%由设定精度划分区间 [Pop]=InitPopGray(popsize,bits);%初始化种群 [m,n]=size(Pop); NewPop=zeros(m,n); children1=zeros(1,n); children2=zeros(1,n); pm0=pMutation; BestPop=zeros(eranum,n);%分配初始解空间BestPop,Trace Trace=zeros(eranum,length(bits)+1); i=1; whilei<=eranum forj=1: m value(j)=feval(FUN(1,: ),(b2f(Pop(j,: ),bounds,bits)));%计算适应度 end [MaxValue,Index]=max(value); BestPop(i,: )=Pop(Index,: ); Trace(i,1)=MaxValue; Trace(i,(2: length(bits)+1))=b2f(BestPop(i,: ),bounds,bits); [selectpop]=NonlinearRankSelect(FUN,Pop,bounds,bits);%非线性排名选择 [CrossOverPop]=CrossOver(selectpop,pCross,round(unidrnd(eranum-i)/eranum)); %采用多点交叉和均匀交叉,且逐步增大均匀交叉的概率 %round(unidrnd(eranum-i)/eranum) [MutationPop]=Mutation(CrossOverPop,pMutation,VarNum);%变异 [InversionPop]=Inversion(MutationPop,pInversion);%倒位 Pop=InversionPop;%更新 pMutation=pm0+(i^4)*(pCross/3-pm0)/(eranum^4); %随着种群向前进化,逐步增大变异率至1/2交叉率 p(i)=pMutation; i=i+1; end t=1: eranum; plot(t,Trace(: 1)‘); title(‘函数优化的遗传算法‘);xlabel(‘进化世代数(eranum)‘);ylabel(‘每一代最优适应度(maxfitness)‘); [MaxFval,I]=max(Trace(: 1)); X=Trace(I,(2: length(bits)+1)); holdon; plot(I,MaxFval,‘*‘); text(I+5,MaxFval,[‘FMAX=‘num2str(MaxFval)]); str1=sprintf(‘进化到%d代,自变量为%s时,得本次求解的最优值%f\n对应染色体是: %s‘,I,num2str(X),MaxFval,num2str(BestPop(I,: ))); disp(str1); %figure (2);plot(t,p);%绘制变异值增大过程 T2=clock; elapsed_time=T2-T1; ifelapsed_time(6)<0 elapsed_time(6)=elapsed_time(6)+60;elapsed_time(5)=elapsed_time(5)-1; end ifelapsed_time(5)<0 elapsed_time(5)=elapsed_time(5)+60;elapsed_time(4)=elapsed_time(4)-1; end %像这种程序当然不考虑运行上小时啦 str2=sprintf(‘程序运行耗时%d小时%d分钟%.4f秒‘,elapsed_time(4),elapsed_time(5),elapsed_time(6)); disp(str2); %初始化种群 %采用二进制Gray编码,其目的是为了克服二进制编码的Hamming悬崖缺点 function[initpop]=InitPopGray(popsize,bits) len=sum(bits); initpop=zeros(popsize,len);%Thewholezeroencodingindividual fori=2: popsize-1 pop=round(rand(1,len)); pop=mod(([0pop]+[pop0]),2); %i=1时,b (1)=a (1);i>1时,b(i)=mod(a(i-1)+a(i),2) %其中原二进制串: a (1)a (2)...a(n),Gray串: b (1)b (2)...b(n) initpop(i,: )=pop(1: end-1); end initpop(popsize,: )=ones(1,len);%Thewholeoneencodingindividual %解码 function[fval]=b2f(bval,bounds,bits) %fval -表征各变量的十进制数 %bval -表征各变量的二进制编码串 %bounds-各变量的取值范围 %bits -各变量的二进制编码长度 scale=(bounds(: 2)-bounds(: 1))‘./(2.^bits-1);%Therangeofthevariables numV=size(bounds,1); cs=[0cumsum(bits)]; fori=1: numV a=bval((cs(i)+1): cs(i+1)); fval(i)=sum(2.^(size(a,2)-1: -1: 0).*a)*scale(i)+bounds(i,1); end %选择操作 %采用基于轮盘赌法的非线性排名选择 %各个体成员按适应值从大到小分配选择概率: %P(i)=(q/1-(1-q)^n)*(1-q)^i, 其中P(0)>P (1)>...>P(n),sum(P(i))=1 function[selectpop]=NonlinearRankSelect(FUN,pop,bounds,bits) globalmn selectpop=zeros(m,n); fit=zeros(m,1); fori=1: m fit(i)=feval(FUN(1,: ),(b2f(pop(i,: ),bounds,bits)));%以函数值为适应值做排名依据 end selectprob=fit/sum(fit);%计算各个体相对适应度(0,1) q=max(selectprob);%选择最优的概率 x=zeros(m,2); x(: 1)=[m: -1: 1]‘; [yx(: 2)]=sort(selectprob); r=q/(1-(1-q)^m);%标准分布基值 newfit(x(: 2))=r*(1-q).^(x(: 1)-1);%生成选择概率 newfit=cumsum(newfit);%计算各选择概率之和 rNums=sort(rand(m,1)); fitIn=1;newIn=1; whilenewIn<=m ifrNums(newIn) selectpop(newIn,: )=pop(fitIn,: ); newIn=newIn+1; else fitIn=fitIn+1; end end %交叉操作 function[NewPop]=CrossOver(OldPop,pCross,opts) %OldPop为父代种群,pcross为交叉概率 globalmnNewPop r=rand(1,m); y1=find(r y2=find(r>=pCross); len=length(y1); iflen>2&mod(len,2)==1%如果用来进行交叉的染色体的条数为奇数,将其调整为偶数 y2(length(y2)+1)=y1(len); y1(len)=[]; end iflength(y1)>=2 fori=0: 2: length(y1)-2 ifopts==0 [NewPop(y1(i+1),: ),NewPop(y1(i+2),: )]=EqualCrossOver(OldPop(y1(i+1),: ),OldPop(y1(i+2),: )); else [NewPop(y1(i+1),: ),NewPop(y1(i+2),: )]=MultiPointCross(OldPop(y1(i+1),: ),OldPop(y1(i+2),: )); end end end NewPop(y2,: )=OldPop(y2,: ); %采用均匀交叉 function[children1,children2]=EqualCrossOver(parent1,parent2) globalnchildren1children2 hidecode=round(rand(1,n));%随机生成掩码 crossposition=find(hidecode==1); holdposition=find(hidecode==0); children1(crossposition)=parent1(crossposition);%掩码为1,父1为子1提供基因 children1(holdposition)=parent2(holdposition);%掩码为0,父2为子1提供基因 children2(crossposition)=parent2(crossposition);%掩码为1,父2为子2提供基因 children2(holdposition)=parent1(holdposition);%掩码为0,父1为子2提供基因 %采用多点交叉,交叉点数由变量数决定 function[Children1,Children2]=MultiPointCross(Parent1,Parent2) globalnChildren1Children2VarNum Children1=Parent1; Children2=Parent2; Points=sort(unidrnd(n,1,2*VarNum)); fori=1: VarNum Children1(Points(2*i-1): Points(2*i))=Parent2(Points(2*i-1): Points(2*i)); Children2(Points(2*i-1): Points(2*i))=Parent1(Points(2*i-1): Points(2*i)); end %变异操作 function[NewPop]=Mutation(OldPop,pMutation,VarNum) globalmnNewPop r=rand(1,m); position=find(r<=pMutation); len=length(position); iflen>=1 fori=1: len k=unidrnd(n,1,VarNum);%设置变异点数,一般设置1点 forj=1: length(k) ifOldPop(position(i),k(j))==1 OldPop(position(i),k(j))=0; else OldPop(position(i),k(j))=1; end end end end NewPop=OldPop; %倒位操作 function[NewPop]=Inversion(OldPop,pInversion) globalmnNewPop NewPop=OldPop; r=rand(1,m); PopIn=find(r<=pInversion); len=length(PopIn); iflen>=1 fori=1: len d=sort(unidrnd(n,1,2)); ifd (1)~=1&d (2)~=n NewPop(PopIn(i),1: d (1)-1)=OldPo
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