ga.rar_GA matlab测试_GA 测试 MATLAB_GA 测试函数_GA测试_基准函数

clear Size=60; CodeL=10; for i=1:1:CodeL MinX(i)=-100*ones(1); MaxX(i)=100*ones(1); end for i=1:1:CodeL x(:,i)=MinX(i)+(MaxX(i)-MinX(i))*rand(Size,1); end G=300; BsJ=0; %*************** Start Running *************** for kg=1:1:G time(kg)=kg; %****** Step 1 : Evaluate BestJ ****** for i=1:1:Size F(i)=0; for j=1:1:CodeL F(i)=F(i)+(x(i,j)^2); end end fi=F; [Oderfi,Indexfi]=sort(fi); %Arranging fi small to bigger Bestfi=Oderfi(1); %Let Bestfi=max(fi) BestS=x(Indexfi(1),:); %Let BestS=E(m), m is the Indexfi belong to max(fi) bfi(kg)=Bestfi; %****** Step 2 : Select and Reproduct Operation****** q=0.1; q2=q/(1-(1-q)^Size); for i=1:1:Size p(i)=q2*(1-q)^(i-1); end pi_sum=sum(p); % 方法1 for k=1:1:Size j=0; sum1=0; rnd=rand(1); while sum1<=rnd j=j+1; sum1=sum1+p(j); end TempE(k,:)=x(Indexfi(j),:); end x=TempE; % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %************ Step 3 : Crossover Operation ************ pc=0.80; for i=1:2:(Size-1) temp=rand; if pc>temp %Crossover Condition alfa=rand; TempE(i,:)=alfa*x(i+1,:)+(1-alfa)*x(i,:); TempE(i+1,:)=alfa*x(i,:)+(1-alfa)*x(i+1,:); end end %************ Step 4: Mutation Operation ************** pm=0.02; Pm_rand=rand(Size,CodeL); for i=1:1:Size for j=1:1:CodeL if pm>Pm_rand(i,j) %Mutation Condition TempE(i,j)=MaxX(j)*rand+MinX(j)*(1-rand); end end end %Guarantee TempE(Size,:) belong to the best individual TempE(1,:)=BestS; x=TempE; end BestS; Bestfi plot(time,bfi)