evolving neural net

[Anthony Yip]

Hello All,

I am a newbie to GA and am wondering if anyone can share their valuable experience with me with regard to evolving a neural net..

i thank you in advance to reading about my situation.

i am currently using a neural net as a non-linear function approximator to learn some target function.. it is a binary target of 0 and 1 and 1 is only in about 15% of all training cases. as far as performance goes the trained net produced good signals at times but the other times the wrong signals can be costly.

therefore now i am turning into GA to hopefully able to evolve a set of weights that can produce better signals. i have heard arguments on both sides with regard to GA being able to produce better results.. so i am willing to give it a try.

now the detail of my situation. the objective function in which i have to run for each net would probably take 5-7 mins. i have a couple of nets that produce "semi-satisfactory results". my net is currently 25x50x1. so there are quite a bit of weights to evolve/train.. 

i am planning on using my own genome class which composed of solely 2 2dgenomes of double/float. (one for inputxhidden weights another for hiddenxoutput weights). initalize the population size to a small size (any suggestions? time could be an issue) and initialize some (about 10) of the genomes with my own trained net weights.. and use steady state GA, tournment selection.. 

my goal is to be able to maximize GA's potential to help me.. given my current situation and my plan can someone please give me some pointers or suggestions as to how or what else to do to approach this? any advice from experienced GAers (especially those with NN experience) would be a treasure. Thanks greatly in advance. I would deeply appreciate any help.

sincerely,

yip



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