dg1v+@andrew.cmu.edu (David Greene) (06/29/89)
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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Carnegie Mellon University || dpg@isl1.ri.cmu.edu
"You're welcome to use my opinion || BITNET: dg1v%andrew@vb.cc.cmu.edu
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-------------------------------------------------------------------------joh@wright.EDU (Jae Chan Oh) (07/14/89)
In article <EYeUmVy00V46I12El4@andrew.cmu.edu>, dg1v+@andrew.cmu.edu (David Greene) writes: > 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 > ... original text has been removed between the above and the below... > 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 pattern recognition and discovery using genetic algorithms, there are several works one might want to consider. 1) Englander, A. C., ``Machine learning of visual recognition using genetic algorithms'', First G.A. conference proceedings. 1985 2) Schaffer, J. D., ``Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms'', Ph. D. Dissertation, Dept. of Electrical Engineering, Vanderbilt University. 3) Gillies, A. M. ``Machine Learning Procedures for Generating Image Domain Feature Detectors'', Computer and Communication Science in The University of Michigan. And one can find some of works from The second G.A. conference by Schaffer and others. I, myself have done some research on image learning and classifications using genetic algorithms for my thesis and published couple of papers. I used Holland type classifier system architecture to attack the problem domain that deals with large class image learning and classification. To accomplish the task, I needed to add some new strategies that prevent good rules from being replaced (some rules can be good to classify some objects but the same rules can be bad for the other images. In this case, we may want to keep the rules.) and the techniques that can delve into the interested area to distinguish similar image objects but in different classes. I have succeeded on training the system to learn and recognize up to 26 different classes (all the alphabet images) with the new strategies. Currently, I'm starting to use CRAY-XMP to perform bigger problems such as 50 classes or even more. If anyone interested in my work, one could contact my department for the copy of the thesis or request an inter-library loan. The thesis is entitled as ``Improved Classifier System Using Genetic Algorithms Applied to Image Learning''. One can send e-mail to `nblair@wright.edu' or contact the Department of Computer Science and Engineering, Wright State Univ., Dayton, OH, 45435. One of my paper on a conference entitled ``Image Learning Classifier System Using Genetic Algorithms'' by McAulay and Oh in the proceedings of IEEE NAECON conference 89' might be interesting too. Hope this helps. -- Jae Chan Oh (Rm. 109, Computer Sci. Dept.) | Disclaimer : Wright State University Research Building | All mine ... 3171 Research Blvd., Kettering, Ohio 45420 | As far as I CSNET: joh@CS.wright.EDU UUCP: ...!osu-cis!wright!joh | type..