matlab 自适应遗传算法源程序，用到了G-S正交化分解，大家可以用QR分解做一下，应该会使程序更简单一点.rar

figure(1); fplot('v.*sin(10*pi*v)+2.0',[-1,2]); NIND=40; MAXGEN=30; PRECI=20; GGAP=0.9; trace=zeros(2,MAXGEN); FieldD=[20;-1;2;1;0;1;1]; Chrom=crtbp(NIND,PRECI); gen=0; v=bs2rv(Chrom,FieldD); ObjV=v.*sin(10*pi*v)+2.0; while gen<MAXGEN, FitnV=ranking(-ObjV); SelCh=select('sus',Chrom,FitnV,GGAP); FitnVmax=max(FitnV); FitnVave=sum(FitnV)/NIND; if(FitnV>= FitnVave) Index1=find(FitnV>= FitnVave); Index2=find(FitnV< FitnVave); Pc1=0.5*(FitnVmax- FitnV(Index,1))/(FitnVmax- FitnVave); Pm1=0.02*(FitnVmax- FitnV(Index,1))/(FitnVmax- FitnVave); else Pc2=0.85; Pm2=0.05; end end SelCh=recombin('xovsp',SelCh(Index1,1),Pc1); SelCh=recombin('xovsp',SelCh(Index2,1),Pc2); SelCh=mut(SelCh(Index1,1),Pm1); SelCh=mut(SelCh(Index2,1),Pm2); v=bs2rv(SelCh,FieldD); ObjVSel=v.*sin(10*pi*v)+2.0; [Chrom ObjV]=reins(Chrom,SelCh,1,1,ObjV,ObjVSel); gen=gen+1; variable=bs2rv(Chrom, FieldD) [Y,I]=max(ObjV),hold on; plot(I,Y,'bo'); trace(1,gen)=max(ObjV); trace(2,gen)=sum(ObjV)/length(ObjV); if (gen==20) figure(2); plot(ObjV);hold on; plot(ObjV,'b*');grid; end %end figure(3); plot(trace(1,:)','Pr'); hold on; plot(trace(2,:)','-.');grid; legend('解的变化','种群均值的变化')