my.m

用matlab编写的遗传算法程序

%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');