[galib] Comparison galib - Matlab genetic algorithm

[gpipc]

I have compiled the galib genetic algorithm library together with some code
that I wrote in order to run as a mex-file im Matlab. I hope that there is
someone who knows enough about both galib and Matlab to give me a hint on
how to proceed in the situation things are now.

I am now in the following situation: the galib code evaluates chromosomes
much faster than the Matlab genetic algorithm ('ga') (I would say at least
10 times faster), but it takes many more fitness function evaluations from
galib in order to obtain the same level of optimization that I obtain with
the Matlab ga; as a result I have not gained a lot in speed (maybe 30% as
fast, maybe twice as fast, but not more).

Part of the problem is that it not straightforward to test the settings of
the two programs with fitness functions that evaluate more quickly - my
impression is that I haven't yet found a simple test fitness function on
which I can optimize the settings such that the optimized settings will be
also optimal for the fitness function I am actually interested in. I am
working on that but on the meantime I look here in the newsgroup for some
hints.

Does anyone know what are the galib settings which would give as a result
an algorithm which is as close as possible as the Matlab ga algorithm?
I have found that for my fitness function (with 32 variables) these ga
settings start to give some reasonable result

ga_options = gaoptimset(ga_options,'PopulationSize', 50);
ga_options = gaoptimset(ga_options,'CrossoverFraction', 0.5);
ga_options = gaoptimset(ga_options,'generations', 30);

In the galib library I have tried both the GASteadyStateGA and the
GASimpleGA. I realize that the question is a bit involved and that it will
take some work on my part to get things to go well, but I hope to get some
hints on which direction to look at.

Thanks in advance for any replies.

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