Path: utzoo!utgpu!news-server.csri.toronto.edu!mailrus!cs.utexas.edu!usc!ucsd!sdcc6!beowulf!schraudo From: schraudo@beowulf.ucsd.edu (Nici Schraudolph) Newsgroups: comp.ai.neural-nets Subject: Re: Observations on the State of NN theory Keywords: Genetic Neural Training Pepsi Message-ID: Date: 18 Aug 90 20:56:28 GMT References: <1990Aug3.175023.28210@ariel.unm.edu> <12173@sdcc6.ucsd.edu> <1990Aug6.170953.979@ariel.unm.edu> Sender: news@sdcc6.ucsd.edu Lines: 25 Nntp-Posting-Host: beowulf.ucsd.edu bill@wayback.unm.edu (william horne) writes: [on using GAs for searching initial weight space:] >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. I'd phrase it the other way round: most of the GA/NN research is aimed at finding a GA (specifically, a genetic representation of NNs) for which the recombination operator exploits some regularity concerning the basins of attraction for NN gradient descent. The two main questions are: 1) Are there any such regularities in the first place, aside from simple invariances such as flipping the sign of all weights? 2) Can we find genetic encodings and/or recombination operators that exploit them? -- Nicol N. Schraudolph, C-014 nici%cs@ucsd.edu University of California, San Diego nici%cs@ucsd.bitnet La Jolla, CA 92093-0114 ...!ucsd!cs!nici