Genetic Algorithms Digest    Friday, June 27, 1986    Volume 1 : Issue 4

 - Send submissions to GA-List@NRL-AIC.ARPA
 - Send administrative requests to GA-List-Request@NRL-AIC.ARPA

Today's Topics:

	- GA-List status
	- alternative bibliography formats
	- Re: alternative bibliography formats
	- some genetic learning questions
	- GA Clearinghouse
	- Machine Learning Group
	- Holland correspondence

------------------------------

Date:	 Fri June 27 1986
From:	 GA-List Moderator
To:	 GA-List Members
Subject: GA-List status

Ok, as promised, here's the first of smaller, higher frequency digests.
Remember to send material to GA-List.  Material sent to gadistr gets buried
in a mountain of electronic junk mail and may be lost for months.

	Ken

------------------------------

Date:     Thu, 12 Dec 85 15:24 EST
From: JUDD%umass-cs.csnet@CSNET-RELAY.ARPA
To: gadistr%nrl-aic.arpa@csnet-relay.arpa
Subject:  RE: Volume 1, Number 2

I would like to see some discussion as to the format of this bibliographic
database.  I tend to favor a BIBTEK format which allows
for the inclusion of keywords, comments, summaries, etc.  For example,

		title=
		author=
		keywords=
		note=
		abstract=
		.
		.
		.

The BIBTEX standard also includes the type of publication being refered to
 (eg PhD thesis, journal, book, tech.rep, etc.). 
This is useful for a variety of reasons:
1) a retrieval program could answer queries like "give me all books that..."
2) a bibliographical typesetter can make use of the distinctions.
3) further distinctiveness or more redundancy in specification.
4) checking for completeness in the entry (eg. a journal needs a volume#)
5) bigger and more is better and bigger.   etc and so on etc.

sj

------------------------------

Date: Fri, 27 Jun 86 10:15:02 edt
From: Lashon Booker <booker>
To: GA-List
Subject: Bibliography format

I volunteered to put together a bibliography to be
distributed over this list,  and suggested using the
REFER format.  Perhaps that format is not the best choice:

	[ see previous message ]

I suggested REFER because it is available on our VAX, period.  If there
is a strong feeling that BIBTEX is better, and BIBTEX is widely available,
*and* we can get a version that runs under 4bsd Unix, I don't mind using it.
Comments?

Lashon

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To: GA-List@nrl-aic.arpa
Subject: Some genetic learning questions.
Date: Mon, 16 Jun 86 15:47:22 +0000
From: Julian Onions <jpo%computer-science.nottingham.ac.uk@Cs.Ucl.AC.UK>
Sender: jpo%computer-science.nottingham.ac.uk@Cs.Ucl.AC.UK


OK, here's some reasonably dumb questions I've been pondering on, anyone
care to give some answers?

1) If I have a system that depends on (say) 5 parameters that I vary
using genetic algorithms until I have (after many trials) got a
good gene pool, what are the best tactics if I then decide that
6 parameters might more closely fit the problem. Should I start from
a random pool, or could I add random 6th DNA strands to my current
gene pool.
e.g.
	I have some pseudo-random thing that I think
can be modelled by some relation
	X = a1*y+a2*y+a3*y+a4*y+a5*y
now I decide perhaps a better fit is
	X = a1*y+a2*y+a3*y+a4*y+a5*y+a6*y
If I start from scratch, I throw away the 1000's of trials done
for the first case. Now if I modify that gene pool to include a random
additional a6, does this
	a) save me time.
	b) have no impact, I might as well have started again.
	c) take much longer as the gene pool has to be effectively
	unfocus on the old situation and then focus on the
	new model.

	I suspect this depends on how closely the 6th parameter
interacts with the others, but I can't decide.

[ My experience suggests c) is most likely the case with traditional
  GA implementations in which loss of variability is hard to undo.
		     - moderator ]

2) Is there any good rule of thumb for the size of the gene pool?
I guess its obvious that the bigger the pool, the more trials
needed to get rid of poor genes. Also a small gene pool is not
going to provide much variety for crossing. Would anyone care
to take a guess at a ratio of number of trials to gene pool
size? E.g. I'm going to do 3000 trials, what size should my
gene pool be, or conversely a gene pool of 35, how many
trials are likely to be needed before I get a reasonably
good/perfect gene pool. I guess this depends also depends on the
the values of the DNA but these values could be normalised.

[ DeJong's thesis and Grefenstette's paper in IEE SMC Jan86 contain
  experimental results and discussions of these issues. Briefly, there
  seems to be a lower bound on the population size around 35-50.  Below
  that performance degrades due to too little memory (genetic drift).
  On the other hand, some of Holland's theorems suggest that the information
  capacity (hyperplane information) of a population increases exponentially
  with population size.  Experimentally this shows up in that little
  advantage is seen in increasing population size beyond 100-200.

  The number of trials is related more to the complexity of the space
  being searched and the length of an individual (number of genes).
  Generally speaking, with population sizes of 50-100, 2000-3000 trials
  are a minimum requirement.
  		- moderator ]
  
3) If there are two solutions to an answer, can this technique
discover either or both answers? My thoughts on the possibilities
here are
	a) The gene pool will sway around randomly until it
	approaches one of the solutions, and then latch onto that.
	b) The gene pool will be tugged in alternate directions
	going into a sort of flip-flop state going from one solution
	to the other.
	c) The gene pool will sway from side to side never getting
	to either of the solutions.
	d) the gene pool will be divided on the issue and there
	will the 50% of the genes on each answer.

What does the panel think?

[ Any of the above, none of the above, ...  It depends, unfortunately, on
  the representation chosen.  Hence all the discussion on representation
  issues.
		- moderator ]

Julian.

------------------------------

Date: Fri, 27 Jun 86 09:11:28 edt
From: Lashon Booker <booker>
To: GA-List
Subject: Clearinghouse
Status: R

I'm passing along this letter Dave Goldberg sent o all
GA conference attendees, just in case some GA-List readers
haven't seen it.  Enclosed with the letter were copies of
an extensive bibliography and a recent technical report.

Lashon

-----------------------------------------------------------

Dear Colleague:

     As an attendee at last year's conference on genetic algorithms, I
thought you might be interested in the two enclosed publications.  One is
a bibliography of genetic algorithm literature, and the other is some
musings on schemata in binary-coded populations.
     Because genetic algorithm publishing is such a hit and miss affair,
I have established The Clearinghouse for Genetic Algorithms (TCGA -
double entendre intended) here at Alabama as a publisher of last resort.
I also have most of the publications cited in the bibliography in our
TCGA files and can help you get copies if you are unable to obtain them
through normal channels.
     If there is interest in the Clearinghouse, we can formalize both the
publishing and information dissemination functions of TCGA.  I want to
emphasize that these efforts are intended to complement, not compete
with, the electronic information exchange established at NRL by Lashon
Booker and Ken DeJong.  As we all know, sometimes there is no substitute
for the printed word, and TCGA will focus its efforts there.
     Please let me know what you think about these publications and TCGA.

                                          Sincerely yours,

                                          David E. Goldberg
                                          Assistant Professor
                                          Dept. of Engineering Mechanics
                                          College of Engineering
                                          The University of Alabama
                                          University, Alabama  35486
                                          205/348-7241


------------------------------

Date: Fri, 27 Jun 86 10:18:40 edt
From: Kenneth Dejong <dejong>
To: ga-list
Subject: Machine Learning Group
Status: R

NCARAI, the Navy AI Center, is gearing up for a substantial effort
in machine learning.  In addition to Lashon and myself, we are delighted
that John Grefenstette has accepted a position starting this fall.
Several graduate students from local universities are also involved.
More details will follow on the direction and scope of our activities.

	Ken

------------------------------

Date: Fri, 27 Jun 86 09:27:32 edt
From: Lashon Booker <booker>
To: GA-List
Subject: News from JHH
Status: R

This is an edited version of two mail messages I
recently received from John Holland.  There are
some news items here of interest to GA-List.

Lashon

------------------------------------------------------------------------

Lashon,
     Good to hear from you!  I've been out of the country for a month (in
Norway at the invitation of the Norwegian Research Council) so I'm behind
but I did get a glimpse at the most recent GA-List and intend to
contribute.
     As far as I know there is no consistent compilation of errors in ANAS,
though my copy is filled with sheets of paper noting down this and that --
the most serious thing I know of is the error in the proof of exponential
allocation for the two-armed bandit problem (Dan Frantz found it) which in-
volves the use of one limit that is a function of another.  We both believe
the theorem is still correct but no one has taken the time to fix it.
     Did I tell you that I've been elected Ulam Scholar at Los Alamos for a
year (from an endowment set up for this purpose)?  It gives me a year to
pursue basic research in whatever direction I please, and I'll be in
residence there for about 3/4 of the time.  Also, did you see the June
issue of SCIENCE 86 -- it has a pretty extensive exposition (at a popular
level) of classifier systems and their relation to some of what I'll be
doing at Los Alamos.
     Our book is due out from MIT press sometime next month.  All in all
we're reasonably satisfied with the final version (which, as you can see,
went to press in a great hurry).
     I'm really glad to hear John is going to be with you guys; and with
another addition you should be THE hotbed of work in this area.  It seems
that several parts of the defense establishment have picked up on some of
this stuff -- I'm currently consulting for Honeywell on an Air Force 6.1
RFP that mentions this work by name.  Since I don't much like to do that
stuff I put an impossibly high consulting rate, but they accepted anyhow!
     Haven't heard anything from Dave Davis!  Stewart Wilson gave a presen-
tation at the "pushing out the door" ceremony for the Connection Machine
(with Wolfram going out the door muttering "maybe there is some science
that can be done there [machine learning] after all").  Since Davis was
there maybe Wilson has more recent info.
     I seem to remember something from you a while back about emerging
hierarchies and system coherence.  If it still relevant to you, I think
this is the most important single issue to be understood both analytically
and with appropriate simulation.  I have some new analytic results (some
of them obtained in Norway), but nothing particularly new w.r.t. simula-
tion (it's almost impossible to find large enough hunks of time to run it
productively during the academic year).  Stewart Wilson has some really
nice stuff about the efficacy of crossover on boolean problems.
     I hear through Rick Riolo who just came back from a couple of days at
Thinking Machines that they now have two of their own people (with Rick as
consultant) working on implementation of classifier systems!  I also hear
from him that at BBN they were doing some connectionist experiments with
settings given to them from CMU and then decided to use the genetic algor-
ithm to explore the parameter space with the result that they came up with
MUCH better settings than those recommended!
     I'll try to get back to you later with more.  If you want to forward
this to the GA-List, it's okay with me.  Let me hear more about what
you guys are up to.
     John

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End of Genetic Algorithms Digest
********************
