karen@hpcvlo.UUCP (03/11/87)
I am investigating genetic algorithms as they relate to machine learning and in particular classifier systems. I hope to do my master's thesis in this area. I am trying to locate literature in this area. Does anyone know how I can get a copy of the "Proceedings of an International Conference on Genetic Algorithms and Their Applications,1985"? Also, it appears that a lot of work on genetic algorithms has been done at the University of Michigan. There are a number of Ph D theses of Univ. of Michigan students referenced in the articles I have found. Is Univ. of Michigan on the net? Will someone there please contact me and tell me how I can get copies of some of the theses? I would appreciate any help and information anyone can give me. Thanks. Karen Helt Hewlett-Packard Company Corvallis Workstation Operation Corvallis, Oregon part-time graduate student at Oregon State University hplabs!hp-pcd!karen
coulter@hpclisp.UUCP (03/18/87)
John Holland is (or was) at U. of Mich. and has written a very nice book on genetic algorithms. I once took a class on the subject which he taught. If you need more information (title, publisher, isbn number, etc.), send me a note and I'll see if I can find my copy of the book. -- Michael Coulter ...hpda!hpcllld!coulter
bernhart@convex.UUCP (03/20/87)
I'm delighted to find someone interested in genetic algorithms. I'm glad
I decided to wander through some notes files.
About 10 years ago I did some work in this area using adaptive hashing
as my application. My faculty advisor turned me on to the subject.
Another student did some work with pattern generation and published a
paper on the subject. His name is Gary Rogers, and last I knew he
was teaching at the Swiss Federal Institute. I'll try to find a copy
of the paper - I just moved so am a little(?!) disorganized.
Two books that will be of interest to you are:
Holland, John H. Adaptation in Natural and Artificial Systems:
An Introductory Analysis with Applications to Biology, Control,
and Artificial Intelligence. Ann Arbor: The University of
Michigan Press, 1975.
Holland is a professor of computer science at the University
of Michigan. His book references a number of dissertations.
Holland, John H., Holyoak, Keith J., Nisbett, Richard E., and
Thagard, Paul R. Induction: Processes of Inference, Learning, and
Discovery. Cambridge, MA: The MIT Press, 1986.
I just got this book a month or two ago and haven't had a
chance to look at it what with moving and all. However, after
just glancing through it, I see there is material on genetic
algorithms and classifier systems. I just happened to order
it because I saw an ad in an MIT Press circular and figured a
John Holland book would interest me. The other authors are
U of M faculty also, two in psychology and one in philosophy.
I'm interested in pursuing my research in this area again. Last Fall
I starting doing a computerized literature search through my company's
Information Center. I didn't come up with anything, but I probably didn't
just hit the right databases at first. I couldn't continue the search
because funding for those activies was cut.
Your note is the first reference I've seen to any conference on genetic
algorithms. I'd love to get my hands on those proceedings, too! Who
sponsored the conference? Where was it held? If I learn anything more,
I'll respond here. If you find out any more, I'll look out for a follow-
up response from you. I'd like to hear of any progress you make in your
research.
My most recent activities have been in the Ada arena, and I'm planning to
convert my genetic modeling work of the past into Ada. I think it's
going to work out very well.
Good luck with your pursuits!
Marcia Bernhardt
Convex Computer Corporation
701 N. Plano Rd.
Richardson, TX 75081
convex!bernhartmatheus@uiucdcsm.UUCP (03/21/87)
Proceedings of an International Conference
on Genetic Algorithms and their Applications.
John Grefenstette, editor
July 24-26, 1985, Carnegie-Mellon University
Sponsored by:
Texas Instruments, Inc.
U.S. Navy Center for Applied Research
in Artificial Intelligence (NCARAI)
------
Some additional references:
John Holland, "Escaping Brittleness: The Possibilities of General-Purpose
Learning Algorithms Applied to Parallel Rule-Based Systems."
In, Machine Learning, Vol II, Michalski, Carbonell, Mitchell, (Eds.), 1986.
------
Larry Rendell, "Conceptual Knowledge Acquisition in Search."
In, Computational Models of Learning, L. Bolc (Ed.), Springer-Verlag, 1987.
------
David Goldberg, "Computer-aided Gas Pipeline Operation using Genetic
Algorithms and Rule Learning." Ph.D. dissertation, University of
Michigan, 1983.
Christopher J. Matheus
Inductive Learning Group
University of Illinois.bernhart@convex.UUCP (03/25/87)
The Proceedings of the conference are copyrighted by John J. Grefenstette the editor. At the time of the conference (and perhaps now) he was at Vanderbilt University. You could contact him about procuring the book, or contact John Holland, the conference chairman, at the University of Michigan. Your university library should be able to assist with procurement of these proceedings and any doctoral dissertations you might need. They probably have extensive inter-library loan resources. Again, good luck! Marcia Bernhardt Convex Computer Corp.
hillary@hpfclp.UUCP (03/26/87)
Concerning GAs....
I am researching genetic algorithms for my Master's thesis work at CSU in
Ft. Collins, CO. I am doing this research under Dr. Darrell Whitley.
There is a conference on GAs this summer....it is the 2nd International
Conference on Genetic Algorithms an Their Applications, sponsored by AAAI
and the U.S. Navy Center for Applied Research in AI (NCARAI). It will be
on July 28-31, 1987 at MIT in Cambridge, Mass. John Holland is the
Conference Chairperson.
For more information contact:
Mrs. Gayle M. Fitzgerald
Conference Services Office
Room 7-111
MIT
77 Massachusetts Avenue
Cambridge, MA 02139
(617) 253-1703
The first of this conference was held on July 24-26, 1985 at Carnegie-Mellon
U. in Pittsburgh, PA. I obtained a copy of the proceedings by writing the
editor at the following address:
Dr. John J. Grefenstette
Navy Center for Applied Research in AI
Naval Research Laboratory
Washington, DC 20375-5000
gref@NRL-AIC.ARPA
(202) 767-2685
Holland's newest book "Induction: ...." is a well written book. It expands on
the chapter in "Machine Learning, Volume 2" that he wrote.
Hope this info is helpful.
Hillary Davidson :-) {hplabs,ihnp4}!hpfcla!hillarydougf@allegra.UUCP (03/26/87)
In article <63800001@convex> bernhart@convex.UUCP writes: > >Your note is the first reference I've seen to any conference on genetic >algorithms. I'd love to get my hands on those proceedings, too! Who >sponsored the conference? Where was it held? If I learn anything more, >I'll respond here. If you find out any more, I'll look out for a follow- >up response from you. I'd like to hear of any progress you make in your >research. > The "International Conference on Genetic Algorithms & their Applications" was held July 24-26, 1985, at Carnegie-Mellon University. It was jointly sponsored by Texas Instruments & the US Navy Center for Applied Research in Artificial Intelligence (NCARAI). The editor was Professor John Grefenstette at Vanderbilt University. I took a course on Genetic algorithms from Professor Grefenstette last year. However, I believe that he has moved to another school by now. Vanderbilt should be able to point you to him, and he has copies of the proceedings. -- doug foxvog ihnp4!allegra!lcuxlj!dougf if only Bell Labs would agree with my opinions... For NSC line eaters: Names of drug dealing CIA agents working on TEMPEST for NRO encrypted above.
djb@LAFITE.BELLCORE.COM.UUCP (03/27/87)
John Grefenstette has just recently posted his email address in the mod-ai bulletin board.
barash@mmlai.UUCP (Rev. Steven C. Barash) (05/23/88)
A while back someone posted an extended definition of "Genetic algorithms".
If anyone still has that, or has their own definition, could you please
e-mail it to me? (There's probably lots of room for opinions here;
I'm interested in all perspectives).
I would also appreciate any pointers to literature in this area.
Also, if anyone wants me to post a summary of the replies, let me know.
Thanks in advance!
Steve Barash
--
Steve Barash @ Martin Marietta Labs
ARPA: barash@mmlai.uu.net
UUCP: {uunet, super, hopkins!jhunix} !mmlai!barashbc@mit-amt.MEDIA.MIT.EDU (bill coderre) (05/25/88)
In article <317@mmlai.UUCP> barash@mmlai.UUCP (Rev. Steven C. Barash) writes: >A while back someone posted an extended definition of "Genetic algorithms". >I would also appreciate any pointers to literature in this area. Well, let's start talking about it right here. Make a change from the usual rhetoric. The classic (Holland) Genetic Algorithm stuff involves a pool of rules which look like ascii strings, the left side of which are preconditions and the right which are assertions. Attached to each rule is a probability of firing. When the clock ticks, all the rules that match their left side are culled, and one is probabilistically selected to fire. There is also an "evaluator" that awards "goodness" to rules that are in the chain of producing a good event. This goodness usually results in greater probability of firing. (Of course, one could also use punishment strategies.) Last, there is a "mutator" that makes new rules out of old. Some heuristics that are used: * randomly change a substring (usually one element) * "breed" two rules together, by taking the first N of one and the last M-N of another. The major claim is that this approach avoids straight hill-climbing's tendency to get stuck on local peaks, by using some "wild" mutations, like reversing substrings of rules. I'm not gonna guess whether this claim is true. I have met Stewart Wilson of the Rowland Institute here in Cambridge, and he has made simple critters that use the above strategy. They start out with random rulebases, and over the course of a few million ticks develop optimal ones. >>>>>>>>>> What is particularly of interest to me is genetic-LIKE algorithms that use more sophisticated elements than ascii strings and simple numeric scorings. My master's research is an attempt to extend Genetic AI in just that way. I wanna use genetic AI's ideas to cause a Society of Mind to learn. It appears that using Lenat-like ideas is the right way to make the mutator, but the evaluator seems like a difficult trick. My hunch is to use knowledge frames ala Winston, but this is looking less likely. ?????????? So does anybody know about appropriately similar research? Anybody got any good ideas? appreciamucho....................................................bc
pi@pollux.usc.edu (Bill Pi) (05/30/88)
In article <317@mmlai.UUCP> barash@mmlai.UUCP (Rev. Steven C. Barash) writes: > >A while back someone posted an extended definition of "Genetic algorithms". >If anyone still has that, or has their own definition, could you please >e-mail it to me? (There's probably lots of room for opinions here; >I'm interested in all perspectives). > >I would also appreciate any pointers to literature in this area. Up till now, there are two conferences held already for Genetic Algorithms: Proceeding of the First International Conference on Genetic Algorithms and Their Applications, ed. J. J. Grefenstette, 1985. Genetic Algorithms and Their Applications: Proceeding of the Second Inter- national Conference o Genetic Algorithms, ed. J. J. Grefenstette, 1987. They can be ordered from: Lawrence Erlbaum Associates, Inc. 365 Broadway Hillsdale, NJ 07642 (201) 666-4110 A latest collection of research notes on GA is Genetic Algorithms and Simulated Annealing, ed. L. Davis, 1987, Morgan kaufmann Publishers, Inc., Los Altos, Ca. Also, A mailing list exists for Genetic Algorithms researchers. For more info. send mail to "GA-List-Request@NRL-AIC.ARPA". Jen-I Pi :-) UUCP: {sdcrdcf,cit-cav}!oberon!durga!pi Department of Electrical Engineering CSnet: pi@usc-cse.csnet University of Southern California Bitnet: pi@uscvaxq Los Angeles, Ca. 90089-0781 InterNet: pi%durga.usc.edu@oberon.USC.EDU
androula@cb.ecn.purdue.edu (Ioannis Androulakis) (06/30/89)
********* The following article concerns Genetic Algorithms. I apologize for bothering the list with that subject but I guess it is the only list available for me. ********* In Genetic Algorithms theory the probabilities of application of the genetic operators are considered to be independent of each other. What do you think of a GA where the probabilties would give a sum of 1. Therefore, after a string has been selected we apply to that string an operator selected randomly according to the probability that has been atributed to it. In other words each string has associated with it a set of operators and the corresponding probabilities of application of these operators. Any suggestion is welcomed. Thank you, yannis androula@helium.ecn.purdue.edu
presnik@bbn.com (Philip Resnik) (06/30/89)
In article <1030@cb.ecn.purdue.edu> androula@cb.ecn.purdue.edu (Ioannis Androulakis) writes: > In Genetic Algorithms theory the probabilities of application > of the genetic operators are considered to be independent of > each other. What do you think of a GA where the probabilties > would give a sum of 1. Therefore, after a string has been selected > we apply to that string an operator selected randomly according > to the probability that has been atributed to it. In other words > each string has associated with it a set of operators and the > corresponding probabilities of application of these operators. It's pretty widely accepted that no single set of genetic algorithm parameters (e.g. population size, mutation rate, crossover rate) is optimal for all problems, and it sounds like you're trying to work out an approach to that issue. One common previous approach has been to vary the mutation rate from a higher value at the beginning of a run (thus initially emphasizing exploration of the search space) linearly down to a lower value at the end of the run (where presumably crossover will be more successful at producing good solutions). Another recent, related piece of work to look at is the "adaptive operator probabilities" work of Lawrence Davis: he takes a set of genetic operators (e.g. mutation, single-point crossover, two-point crossover, uniform crossover, hillclimbing operators) and gives them each an initial probability at the beginning of the run, and then modifies the operator's probability on the basis of the performance of the children produced by this operator (and their descendants). He's had quite good results with this approach, and it also provides an interesting way of testing out new operators to see if they improve performance or hurt it. Note, however, that he maintains a single probability for each operator across the entire population, as opposed to having a different probability for each member. Lawrence Davis, "Adapting Operator Probabilities in Genetic Algorithms," Proceedings of the 3rd International Conference on Genetic Algorithms, Morgan Kaufman, 1989. Philip Resnik presnik@bbn.com
androula@cb.ecn.purdue.edu (Ioannis Androulakis) (09/21/89)
Is there any expression for the time bound that Genetic Algorithms would need to converge in ? Thank you ioannis androula@helium.ecn.purdue.edu
androula@cb.ecn.purdue.edu (Ioannis Androulakis) (12/21/89)
I would like to implement some sort of pattern
learning in my Genetic Algorithm search. In other
words I would like to exploit the pattern of changes
which cause an imporovement from one generation to another.
I would appreciate any help,
thank you,
Ioannis P. Androulakis
e-mail : androula@lips1.ecn.purdue.edutjf@lanl.gov (Tom J Farish) (01/10/90)
I would very much appreciate references (papers, texts, etc) pertaining to genetic algorithms and Holland classifiers. What are the 'classic' papers in the field? Please email responses. thanks!
mlevy@maccs.dcss.mcmaster.ca (Michael Levy) (05/31/91)
Could somene please summarize this subject for me or point me towards some good introductory articles. Thanks. Michael Levy