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%Generic Algorithm for function f(x1,x2) optimum 
clear all; 
close all; 
 
%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; 
    %***************Step 1: Evaluate BestJ ************** 
    BestJ(k)=min(Ji); 
     
    fi=F;                               % Fitness Function 
    [Oderfi,Indexfi]=sort(fi);         % Arranging fi small to bigger 
    Bestfi=Oderfi(Size);                % Let Bestfi=max(fi) 
    BestS=E(Indexfi(Size),:);          % Let BestS=E(m),m is the Indexfi belong to max(fi) 
    bfi(k)=Bestfi;                       
     
    % *************Step 2 : Select and Reproduct Operation ********** 
    fi_sum=sum(fi); 
    fi_Size=(Oderfi/fi_sum)*Size; 
     
    fi_S=floor(fi_Size);               %Selceting Bigger fi value 
    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;                   % kk is used to reproduce  
        end 
    end 
    % ************** Step 3: Crossover Operation ********* 
    pc=0.60; 
    n=ceil(20*rand); 
    for i=1:2:(Size-1) 
        temp=rand; 
        if pc>temp 
            for j=n:1:20 
                TempE(i,j)=E(i+1,j); 
                TempE(i+1,j)=E(i,j); 
            end 
        end 
    end 
    TempE(Size,: )=BestS; 
    E=TempE; 
    % ************************Step 4: Mutation Operation **************** 
    % pm=0.001; 
    % pm=0.001-[1:1:Size]*(0.001)/Size;     % Bigger fi,smaller pm 
    % pm=0.0                                % NO mutation 
    pm=0.1;                                 % Big mutation 
    for i=1:1:Size; 
        for j=1:1:2*CodeL 
            temp=rand; 
            if pm>temp 
                if TempE(i,j)==0 
                    TempE(i,j)=1; 
                else 
                    TempE(i,j)=0; 
                end 
            end 
        end 
    end 
    % Guarantee TempPop(30,:) is the code belong to the best individual (max(fi)) 
    TempE(Size,: )=BestS; 
    E=TempE; 
end 
 
Max_Value=Bestfi 
BestS 
x1 
x2 
figure(1); 
plot(time,BestJ); 
xlabel('Times');ylabel('BestJ'); 
figure(2); 
plot(time,bfi); 
xlabel('times');ylabel('BestF');