遗传算法求解函数最大值(matlab).docx

F(x1 ， x2)=100*(x1^2-x2)^2+(1-x1)^2;遗传算法求解函数的最大值 (MATLAB)%Generic Algorithm for function f(x1,x2) optimum clear all;close all;（最大值）%Parameters Size=80; G=100;CodeL=10;umax=2.048;umin=-2.048; F(x1 ， x2)=100*(x1^2-x2)^2+(1-x1)^2; 遗传算法求解函数 的最大值 (MATLAB) %Generic Algorithm for function f(x1,x2) optimum clear all; close all; （最大值） %Parameters Size=80; G=100; CodeL=10; umax=2.048; umin=-2.048; E=round(rand(Size,2*CodeL)); %Initial Code %Main Program for k=1:1:G time(k)=k; for s=1:1:Size m=E(s,:); y1=0;y2=0; %Uncoding m1=m(1:1:CodeL); for i=1:1:CodeL y1=y1+m1(i)*2^(i-1); end x1=(umax-umin)*y1/1023+umin; m2=m(CodeL+1:1:2*CodeL); for i=1:1:CodeL y2=y2+m2(i)*2^(i-1); end x2=(umax-umin)*y2/1023+umin; F(s)=100*(x1^2-x2)^2+(1-x1)^2; end Ji=1./(F+1); %****** Step 1 : Evaluate BestJ ******BestJ(k)=min(Ji);fi=F; [Oderfi,Indexfi]=sort(fi); Bestfi=Oderfi(Size); BestS=E(Indexfi(Size),:); max(fi)bfi(k)=Bestfi;%Fitness Function%Arranging fi small to bigger%Let Bestfi=max(fi)%Let BestS=E(m), m is the Indexfi belong to%****** Step 2 : Select and Reproduct Operation****** fi_sum=sum(fi);fi_Size=(Oderfi/fi_sum)*Size;fi_S=floor(fi_Size);%Selecting Bigger fi value (取整)kk=1;for i=1:1:Sizefor j=1:1:fi_S(i) %****** Step 1 : Evaluate BestJ ****** BestJ(k)=min(Ji); fi=F; [Oderfi,Indexfi]=sort(fi); Bestfi=Oderfi(Size); BestS=E(Indexfi(Size),:); max(fi) bfi(k)=Bestfi; %Fitness Function %Arranging fi small to bigger %Let Bestfi=max(fi) %Let BestS=E(m), m is the Indexfi belong to %****** Step 2 : Select and Reproduct Operation****** fi_sum=sum(fi); fi_Size=(Oderfi/fi_sum)*Size; fi_S=floor(fi_Size); %Selecting Bigger fi value (取整) kk=1; for i=1:1:Size for j=1:1:fi_S(i) %Select and Reproduce TempE(kk,:)=E(Indexfi(i),:); kk=kk+1; end end %kk is used to reproduce %************ Step 3 : Crossover Operation ************ pc=0.60; n=ceil(20*rand); for i=1:2:(Size-1) temp=rand; if pctemp for j=n:1:20 TempE(i,j)=E(i+1,j); TempE(i+1,j)=E(i,j); end end %Crossover Condition end TempE(Size,:)=BestS; E=TempE; %*****