Hello,

I’ve been using metavolv to tune parameters of my program, and here is what I have to say.

The program can’t deal with stochastic functions at all. Even when the function shows a clear gradient at bigger scale, but is noisy in small details, metavolv gets stuck in fictional local maxima created by noise. I can provide some logs showing that. Mitchell writes in ‘discussion’ section of the manual about optimization of 4Play parameters: “In looking at the parameter set, we see that most of the parameters changed very little, indicating that our initial choices were not bad.” Well, I don’t think so. It seems that parameters always change very little, because of the noise problem.

Description of what should be in ‘editThis.py’ file is insufficient; it takes time to guess what ‘resultPosition’ means, and I don’t understand some others. Is it worth mentioning in manual that the program requires 2 non-standard python packages: numpy and ctypes.

Overall, the idea is nice, but it needs more sophisticated search procedures. Why don’t you use a GA, for example?

            Kirill

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