gademo2.m

王小平《遗传算法——理论、应用与软件实现》随书光盘

% GADEMO2 
clf;
figure(gcf);
echo on
clc
% This demonstration show the use of the genetic toolbox to optimize a
% multi-dimensional non-convex function.
% The function is coded in the coranaEval.m file
pause %Strike any key to examine coranaEval
clc
type coranaEval.m
pause %Strike any key to continue
clc
%This function is basically a n dimensional parabola with rectangular
%pockets removed. Let's take a look at the function in 2-dimensions
%This may take a couple of minutes...
i=0;
a=-0.5:0.02:0.5;
for x=a
  i=i+1; j=0;
  for y=a
    j=j+1;
    z(i,j)=coranaEval([x y]);
  end
end
%Done!
%First let's look at it in each dimension independently
clg
plot(z(:,1)) %Plot a slice of the function in x max 250.25
%Notice the range is [250.0-250.25]
pause %Strike any key to continue
clg
plot(z(1,:)) %Plot a slice of the function in y 
%Notice the range is [0-250]
pause %Strike any key to continue
mesh(a,a,z);
view(30,60);
grid;
%Remember the deviation in y is 1000 times that of x.
pause %Strike any key to continue
clc
%Lets minimize this function in 4 dimensions between [-10,000 10,000].
%The ga is set up to maximize only.  Minimization of f(x) is equivalent to
%maximizing -f(x), so we use the negative of the Corana function.
%type coranaMin.m
pause 
%Any key to continue
clc
%First set up the bounds
bounds = ones(4,1)*[-10000 10000];
%Now lets optimize
%This may take some time...
[x,endPop,bestSols,trace]=ga(bounds,'coranaMin');
%Done!
pause %Any key to continue
clc
%The first return is the optimal [x1 x2 x3 x4 val]
x
%Lets take a look at the performance of the ga during the run
plot(trace(:,1),trace(:,3),'y-')
hold on
plot(trace(:,1),trace(:,2),'r-')
xlabel('Generation'); ylabel('Fittness');
%The red line is a track of the best solution, the yellow is a track of the
%average of the population
pause %Any key to continue
clc
%End of gademo2