Genetic Algorithms Digest    Thursday, 14 April 1988    Volume 2 : Issue 11

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Today's Topics:
	- TSP problems available
	- Analysis of reproduction and crossover
	- GA seminar at BBN

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Date: Sat, 9 Apr 88 13:39:44 edt
From: liepins@UTKCS2.CS.UTK.EDU (Gunar Liepins)
Subject: TSP problems

We have received a number of requests for the collection of TSP problems
mentioned in GAList Vol. 2 Issue 9.  Please specify what format/medium
for these problems from the following list:
1) DOS floppy; 
2) UNIX shell archive (shar), preferred for anyone using a UNIX system;
3) one very large mail file which you will have to break up;
4) one problem per mail file;
5) any other format should be described in detail;

Thanks;

Darnit!  I thought it said 'Survival of the fattest'.

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Date: Tue, 12 Apr 88 10:30:35 edt
From: richards@UTKCS2.CS.UTK.EDU (richardson)
Subject: Analysis of reproduction and crossover

Has anyone out there implemented or thought about implementing a numerical 
solution to the set of equations published by Bridges and Goldberg in the
ICGATA2 proceedings?

I have started to take the plunge several times.  It isn't a trivial task
but I think it would be useful.  Any thoughts?

Also, does anyone know of a classifier system in Lisp for a Symbolics machine 
(has Flavors).
		      Jon Richardson & Mark Palmer.

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[The following appeared on AIList. -- JJG]

Date: Tue 12 Apr 88 08:33:03-EDT
From: Dori Wells <DWELLS@G.BBN.COM>
Subject: Seminar - Adaptive Knowledge for Genetic Algorithms (BBN)


                       BBN Science Development Program
                             AI Seminar Series

            ADAPTIVE KNOWLEDGE REPRESENTATION:  A CONTENT SENSITIVE
                 RECOMBINATION MECHANISM FOR GENETIC ALGORITHMS

                           J. David Schaffer
                          Philips Laboratories
                   North American Philips Corporation
                      Briarcliff Manor, New York


                          BBN Laboratories Inc.
                           10 Moulton Street
                    Large Conference Room, 2nd Floor

                10:30 a.m., Tuesday, April 19, 1988

Abstract: This paper describes ongoing research on content sensitive
recombination operators for genetic algorithms. A motivation behind this
line of inquiry stems from the observation that biological chromosomes appear
to contain special nucleotide sequences whose job is to influence the
recombination of the expressible genes. We think of these as punctuation marks
telling the recombination operators how to do their job. Furthermore, we
assume that the distribution of these marks (part of the representation) in
a gene pool is determined by the same survival-of-the-fittest and genetic
recombination mechanisms that account for the distribution of the expressible
genes (the knowledge). A goal of this project is to devise such mechanisms
for genetic algorithms and thereby to link the adaptation of a representation
to the adaptation of its contents. We hope to do so in a way that capitalizes
on the intrinsically parallel behavior of the traditional genetic algorithm.
We anticipate benefits of this for machine learning.

We describe one mechanism we have devised and present some empirical evidence
that suggests it may be as good as or better than a traditional genetic
algorithm across a range of search problems. We attempt to show that its
action does successfully adapt the search mechanics to the problem space
and provide the beginnings of a theory to explain its good performance.

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End of Genetic Algorithms Digest
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