www.pudn.com > GA_Toolbar.rar > GADEMO2.M, change:1997-04-07,size:1927b

% 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