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From: bill@wayback.unm.edu (william horne)
Newsgroups: comp.ai.neural-nets
Subject: Re: Observations on the State of NN theory
Keywords: Genetic Neural Training Pepsi
Message-ID: <1990Aug6.170953.979@ariel.unm.edu>
Date: 6 Aug 90 17:09:53 GMT
References: <spoffojj.649607641@lgn> <1990Aug3.175023.28210@ariel.unm.edu> <12173@sdcc6.ucsd.edu>
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In article <12173@sdcc6.ucsd.edu> pluto@zaius.ucsd.edu (Mark Plutowski) writes:

	(In response to my flame on the usefullness of GAs in NNs)
>
>Well, OK, if you insist!  Actually, this is not much of a flame, but more of 
>a memo that GAs can help a great deal, with that one aspect of network
>learning we all know and love:  The Restart Method!  Yes, you too have 
>used it, if you've done any network training at all.  Now, what's wrong 
>with utilizing a bit of knowledge (or, a byte or two even) from past 
>restarts to guide the settings of parameters and initial weights for the 
>next one?  GAs can do this. 
>

This may be possible assuming that your knowledge from previous starting
points could be used to direct your next guess at a starting point.
How would this knowledge help except to not make a guess near your old
starting point?  As far as I know there is no type of known regularity
concerning local minima which is particularly suitable towards GAs.
As far a setting of parameters, I prefer something like delta-bar-delta
which uses a local measure of the surface to guide setting of parameters.

One interesting application of GAs seems to be trying to find the best
topology, i.e. number of nodes, layers etc...  I could see how you
could combine the best of two networks maybe....

-Bill