Genetic Algorithms Digest   Thursday, 15 December 1988    Volume 2 : Issue 25

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Today's Topics:
	- GA implementation problems
	- Re: Classifier systems problems

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From: thefool@ATHENA.MIT.EDU
Date: Mon, 12 Dec 88 08:59:31 EST
Subject: GA implementation problems

As part of a horse racing genetic learning algorithm system I developed,
I created several ga diagnosis techniques that  might be helpful to others.

I am interested in hearing from people who 
  1) have had difficulty setting up a ga system 
  2) have solved hard *implementation* problems with gas

-- Michael de la Maza				thefool@athena.mit.edu

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From: "Leonard" <XT.A08@Forsythe.Stanford.EDU>
Date: Wed, 14 Dec 88 15:39:58 PST
Subject: Re: Classifier systems problems

Mr. Michael R. Hall  <hall%nvuxh.UUCP@bellcore.com> writes:

     "[I would generally advise against Michigan approach
      classifier systems for program induction as well, but
      that's another story.]"

Inasmuch as I am working with a Michigan style classifier system
-- altered to be sure, but still a system that applies genetic
operators to single production rules rather than to sets of them
in the manner of either Smith or Grefenstette -- this is a story
that I would honestly like to hear.  If I'm on the wrong track,
I want to know sooner rather than later.

     I appreciated Mr. Hall's remarks about the problem of
premature convergence and his recommendation of speciation as the
preferred solution. There are, however, a number of approaches to
speciation.

     Holland, in "Adaptation in Natural and Artificial Systems,"
pp. 164-170, discusses the concept in terms of one-armed bandits and
stable queues and makes a point of the fact that, given his model,
speciation can take place in the absence of isolation.

     Other researchers, explicitly interested in techniques for
promoting speciation (or niche formation? -- the concepts get
conflated), have used either some measure of a string's similarity
to the rest of the population to condition mating (preselection,
crowding, sharing functions) or a restrictive mating strategy
(mating templates).  References to all of these approaches are
found in Goldberg's "Genetic Algorithms."

     There is yet another method that I've tried with some success
-- multiple reproductive schedules.  Each classifier has a string
that specifies WHEN it will reproduce.  Each position in the string
is associated with a prime number or a multiple of primes.  When a
classifier has a '1' in a given position it will reproduce in every
'year' that is divisible without remainder by the number associated
with that position.  A 'year' is defined a some number of iterations
through the problem set.

     This arrangement has good effects.  The influence of strong
individuals is buffered by the fact that they cannot mate with the
entire population.  Their influence must spread through classifiers
that have a variegated reproductive schedule.  In other words, the
classifier population is graded between those that have high
mutability/responsiveness and those that are comparatively staid.
This prevents premature convergence.  In the earlier cycles of a
run, presuming that you begin with a randomly generated population,
this grading provides a kind of time-released randomness as
succesive sets of classifiers hit their first '1'.

     It is interesting to observe that sometimes the functional
role of a set of classifiers or their trophic depth CAN become
highly correlated with a particular reproductive schedule (i.e.,
niche and species will converge).

     The use of multiple reproductive schedules can incidentally
give you many of the benefits of having competing rule populations,
but you let the system (for better or worse) define the size and
membership of those groups on its own.

     If all goes well I should have a paper about this ready for the
3rd ICGA deadline.  Comments from anyone who has tried something
similar would be deeply appreciated -- especially, of course, if
this strategy has been abandoned for reasons that I have yet to
discover.

--------------------------------------------------------------------
Leonard Lutomski      AIR Center for Applied Artificial Intelligence
xt.a08@stanford       1791 Arastradero Road
415-493-3550          Palo Alto, CA 94302

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End of Genetic Algorithms Digest
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