joh@wright.EDU (Jae Chan Oh) (01/25/88)
I am working on a learning system using genetic algorithms. And I'm trying to find some engineering applications using this concept. I would like to have the informations about real details of the CS-1(cognitive system-1) by J. Holland, LS-1 by S.F. Smith, or whatever it is related. ( I have read the articles about thease systems, but the idea of the "classifier" was not clear to me) Any hints, literature references, or even source listings are very appreciated.( source listings will be prefered ) If there is interest, I will summarize the results and will email it. Thanks in advance. P.S.: Does any one know the email addresses of J. Holland( U of Michigan), S.F. Smith ( Carnegie-Mellon, I guess) or anyone who've been related in genetic algorithms ? -- Jae Chan Oh CSNET: joh@CS.wright.EDU UUCP: ...!cbosgd!wright!joh Wright State University Research Building 3171 Research Blvd, Kettering, Ohio 45420
g451252772ea@deneb.ucdavis.edu (0040;0000006866;0;327;142;) (01/25/88)
About a month or so ago I complained of the engineering focus of disserta- tions done by Holland's students. I got a very nice reply from a former student, Lashon Booker, who cited a number of more abstract projects (including his own). All these theses are available through U. Microfilms (which happens to be based in Michigan), at about $25 each. Lashon is still quite active; he's at booker@nrl-aic.ARPA. There is a BBS for genetic algorithms; to subscribe, send mail to GA-List-Request@nrl-aic.ARPA. (I did some time ago but have no reply yet... hmmm) And a standard set of C subroutines for classifier systems is available for media cost from Rick Riolo at U.Mich. Contact him at Rick_Riolo@ub.cc.umich.edu for details - I got mine on a 1.2 meg AT disc (just fits). Other formats available (Sun, Mac, ... ). This is ver 0.98, so it's not totally stable yet. I'm slowly getting acquainted with it all... Oh yes: the books INDUCTION, 1986, by Holland et al; GENETIC ALGORITHMS AND SIMULATED ANNEALING, 1987, L. Davis; and GENETIC ALGORITHMS AND THEIR APPLICATION, Proceed. 2nd Intl. Conf. Gen. Alg. (L. Erlbaum Assoc, Pub), are all of interest. I, for one, would be curious what else you learn, although my interests are more in the theoretical arena (population genetics, et al). Ron Goldthwaite / UC Davis, Psychology and Animal Behavior 'Economics is a branch of ethics, pretending to be a science; ethology is a science, pretending relevance to ethics.'
dwt@zippy.eecs.umich.edu (David West) (02/12/88)
In article <1062@ucdavis.ucdavis.edu> g451252772ea@deneb.ucdavis.edu.UUCP (PUT YOUR NAME HERE) writes: >The author discusses neural nets, >simulated annealing, and one example of GA, all applied to the TSP, but >comments that "... a thorough comparason ... _would be_ very interesting" [...] >o As noted, the TSP is a canonical candidate. I believe the TSP is popular because it is easy and compact to program. The performance of a general method such as GAs can be strongly influenced by the problem representation, and it turns out that the most straightforward representations for genetic operations are particularly badly matched to the most straightforward representations for TSPs. This makes the TSP a rather unfortunate choice of introductory example for people who are unfamiliar with GAs. >Finally, I noted above that the production rules take system inputs as >bit-strings. This representation allows for induction,... It is *one* way of getting a form of induction, and has the property that only very simple operations on the internal representation are used; the extent to which this is useful depends, again, on the joint appropriateness of the representations of the genetic operators and the world. An "appropriate" representation has the property that the expected fitness of the result of (say) a crossover is not severely worse than that of its parents. This is something that must be ensured by the experimenter if (as is most common) the representational mapping itself is not subject to genetic selection. -David West