Maybe you can try to implement http://www.mathworks.nl/matlabcentral/fileexchange/45490-generate-k-random-numbers-which-sum-m inside the godlike algortihm when the new population is created.

Best regards.

15 Nov 2012

Pareto Front
Two efficient algorithms to find Pareto Front
Author: Yi Cao

Hello

Something strange happened I'm working on Matlab 7.11 under Win 7 (32b). On hp intel i5.

Then i used the mex file given and the example works really fine. But then i decided to follow what is stated in other requirements. It compiles and runs properly no errors. The issue is that the reported CPU times after recompiling are double. Isn't that odd? Obviously i'll use the original file from the posts, but the new compilations shouldn't be more efficient?. What am i missing here?

Dear Paula,
I will try to help with your problem.
The problems that SCE-UA focused is mainly on the constrained problems, which have a fixed parameter bounds,such as [2,54]. To solve your problem, i think it is able to set a very small number (10e-7) as the fixed lower bound for both a and b. Same way to deal with the upper bound. It might need to play with the algorithm parameter (Number of complexes or simplexes) to improve the computation efficiency. Hope this can help.

Hi, really great work. My only problem is getting the options to work. For example if I put 'MaxFunEvals',10 or 'Display','On' it seems to just ignore the option. Anyone know why this might be? Other than that it is running well.

I have to use this optimization algorithm to minimizing a objective function like this:
error = ((((a./(a+b))-mean).^2)./(mean.^2))+(((((a.*b)./((a+b+1).*((a+b).^2)))-variance).^2)./(variance.^2))+(((((2.*(b-a).*(sqrt(a+b+1)))./((a+b+2).*(sqrt(a.*b))))-skewness).^2)./(skewness.^2))
This is a function beta with parameters a and b. I want to calculate a and b minimizing this fuction error.
I dont know how I can impose the limits of a and b in the algorithm. The limits are, a>0,b>0 and b>0.
Thanks all

Hello again,
Just a comment: in addition to the modification I made below, I also require that a significant portions of the individual parameters be zero (which I call MAXNUMPAR), i.e. pop{1}.individuals = [ 0 0.2 0 0 0.5 0 0.3 ];. I initially included this change in the line before my previous modification.
This seems to cause problems with the genetic algorithm, as it returns NANs in some of the individuals.
I correct this by moving my MAXNUMPAR to before pop{i}.iterate;.
I'm not sure why the genetic algorithm does this (possibly combinations of the individuals results in an individual of all zeros), and isn't in need of a fix. Just an FYI in case someone else ever tries to do the same thing.
Again, this download is awesome.

Thank you, Mario.
Thank you, Rody.
I managed to get the modification I wanted to GODLIKE, which makes me very glad, because this program seems very awesome.
To do it, I went to line 198 of GODLIKE and inserted the following code
X = size(pop{i}.individuals,1);
for j = 1:X
pop{i}.individuals(j,:) = ...
NORMTOTOTAL(pop{i}.individuals(j,:)));
end
clear X
where NORMTOTOTAL is a simple function I wrote to normalize each number in a row vector to the sum of the entire vector.
Thank you!

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