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From: dg1v+@andrew.cmu.edu (David Greene)
Newsgroups: comp.ai
Subject: Re: GA's, references, this 'n that, etc...
Message-ID: <EYeUmVy00V46I12El4@andrew.cmu.edu>
Date: 29 Jun 89 11:48:49 GMT
References: <1020@cb.ecn.purdue.edu>
Organization: Carnegie Mellon, Pittsburgh, PA
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In-Reply-To: <1020@cb.ecn.purdue.edu>

This post contains:
        1. an address for a Genetic Algorithm mailing list/ bboard
        2. a list of general GA references
        3. a response to: 26-Jun-89 Discover Patterns in GAs Ioannis
Androulakis@cb.e


1) There is a mailing list/ bboard for GA's:
 - Send submissions to GA-List@AIC.NRL.NAVY.MIL
 - Send administrative requests to GA-List-Request@AIC.NRL.NAVY.MIL

2) The following are several good references to GA's.  The Goldberg book is
probably the most useful and thorough as an intro to GA's and as a pointer to
other work.  The first Holland citation is primarily historical as the original
source for the field.  The second Holland citation is probably already accesible
on most AI bookshelves :-)  The 3 proceedings (85, 87 89 - the conference is
every other year) present the state of GA development (theory and application)
as well as insight into the various extensions and variations that have been
created.

References:

    Holland,J.H. "Adaptation in Natural and Artificial Systems", University of
    Michigan Press, 1975.
        (this is the original treatise which presented the GA idea and the
    'schema theory')

    Holland,J.H.  "Escaping Brittleness: the Possibilities of General Purpose
    Learning Algorithms Applied to Parallel Rule-Based Systems" in Machine
    Learning: An Artificial Intelligence Approach, volume II,  R. Michalski, J.
    Carbonell, and T. Mitchell (Eds.), Morgan Kaufmann, 1986.
        (since it is in 'ML:vol 2' many will already have it on their shelves...
        it describes the GA as well as Holland's "classifer" system (GA-based))

    Goldberg, David "Genetic Algorithms in Search, Optimization and Machine
    Learning", Addison-Wesley, 1988.
        (good general reference -- basically a textbook on GA's)


    Proceedings of an International Conference on Genetic Algorithms and their
    Applications , CMU - Pittsburgh, Pa., ed. John Grefenstette, 1985 (Lawrence
    Erlbaum pub.)

    Proceedings of the Second International Conference on Genetic Algorithms and
    their Applications, MIT - Boston, Mass., 1987 ed. John Greffenstette
    (Lawrence Erlbaum pub.)

    Proceedings of the Third International Conference on Genetic Algorithms and
    their Applications, George Mason U. - Washington DC, 1989 ed. J. David
    Schaffer, (Morgan Kaufman pub.)
3) > *Excerpts from ext.nn.comp.ai: 26-Jun-89 Discover Patterns in GAs Ioannis*
> *Androulakis@cb.e (620)*
>  I would like to know if there has been any work done  attempting
>  to discover patterns in GA search. My basic concern is how I can
>  draw knowledge  from the  system  and maybe I could do that if I
>  were able to study how patterns behave, while they are formed.
>  This might contradict the basic  notion of "implicit parallelism"
>  in GA search, but could also  help in understanding why a particular
>  system evolved that way or another.


This is rather unclear as to what patterns you are looking for (ie. patterns in
some underlying object of study or patterns in the GA search itself).  For the
former, you'll find many references to pattern recognition problems including
vision systems (see above sources).  For the latter, there are a number of ways
to study GA behavior and the effects of various representations, evaluation
functions and parameter settings.  At another level, an interesting example is
Stewart Wilson's Boole system (Machine Learning vol 2, num 3, Nov. 87)  which
studies GA behavior patterns for a very simple ecology of artificial animals.

I hope this is of some use.


-David
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