thefool@athena.mit.edu (Michael A. de la Maza) (09/18/88)
I am currently working on a genetic learning algorithm(gla) engine that draws inferences from a horse racing database (the results could be enRICHening). Has anyone compiled a bibliography of gla articles/books? If I'm inundated with responses I'll post a summary here. Michael A. de la Maza thefool@athena.mit.edu Query: What is the answer to this question?
smryan@garth.UUCP (Steven Ryan) (09/20/88)
>Michael A. de la Maza thefool@athena.mit.edu >Query: What is the answer to this question? Response: There is no question for the answer.
bwk@mitre-bedford.ARPA (Barry W. Kort) (09/20/88)
>Michael A. de la Maza thefool@athena.mit.edu >Query: What is the answer to this question? Response: "This is the answer to your question." --Barry Kort Zen Student: "What is the very best question that I could ask, and what is the very best answer you can give me?" Zen Master: "The question you just asked is the very best one, and this answer is the very best one I can give you."
brian@caen.engin.umich.edu (Brian Holtz) (11/03/88)
Does anyone know of any references that describe classifier systems whose messages are composed of digits that may take more than two values? For instance, I want to use a genetic algorithm to train a classifier system to induce lexical gender rules in Latin. Has any work been done on managing the complexity of going beyond binary-coded messages, or (better yet) encoding characters in messages in a useful, non-ASCIIish way? I will summarize and post any responses. In article <7104@bloom-beacon.MIT.EDU>, thefool@athena.mit.edu (Michael A. de la Maza) writes: > > Has anyone compiled a bibliography of gla articles/books? In "Classifier Systems and Genetic Algorithms" (Cognitive Science and Machine Intelligence Laboratory Technical Report No. 8) Holland lists some 80 or so applications of GAs, and offers a complete bibliography to interested parties. He can be reached at the EECS Dept., Univ. of Michigan, Ann Arbor MI 48109 (he doesn't seem to have an obvious email address here...). You can get a copy of the technical report from Sharon_Doyle@ub.cc.umich.edu.
pat@cs.strath.ac.uk (Pat Prosser) (11/07/88)
Genetic Algorithms (GA's) traditionally represent the genetic string
(chromosone) using a binary alphabet; Holland has shown this to be
optimal. It is not the only alphabet, a purely symbolic alphabet is
possible if appropriate genetic operators are defined. For example
[1] P. Prosser, "A Hybrid Genetic Algorithm for Pallet Loading"
European Conference on Artificial Intelligence, 1988
[2] Derek Smith, "Bin Packing with Adaptive Search"
Proceedings ICGAA 1985
[3] David Goldberg, "Alleles, Loci and the Travelling Salesman
Problem"
The only problem with non-binary alphabet is the limits of our
imagination.