Page 25 - Algorithm Collections for Digital Signal Processing Applications using MATLAB

1. Artificial Intelligence 13

pop1=col2;
s=fcn(pop);
plot(pop,s)
[u,v]=max(fcn(pop1));
BEST=[BEST;pop1(v(1)) fcn(pop1(v(1)))];
hold on
plot(pop1,fcn(pop1),'r.');
M(iter)=getframe;
pause(0.3)
hold off
[iter pop1(v(1)) fcn(pop1(v(1)))]
end
for i=1:1:14
D(:,:,:,i)=M(i).cdata;
end
figure
imshow(M(1).cdata)
figure
imshow(M(4).cdata)
figure
imshow(M(10).cdata)
figure
imshow(M(30).cdata)

___________________________________________________________________________

fcn.m

function [res]=fcn(x)
res=x+10*sin(5*x)+7*cos(4*x)+sin(x);

Note that the m-file fcn.m may be edited for changing the fitness function

2.2.2 Program illustration

The following is the sample results obtained during the execution of the
program geneticgv.m