Genetic Algorithms Digest   Thursday, 10 November 1988    Volume 2 : Issue 23

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

Today's Topics:
	- Administrivia
	- GAs for control systems
	- AI genealogy
	- GAs for game tree search
	- Apportionment of credit

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

Date: Thu, 13 Oct 88 13:34:21 EDT
From: GA-List Moderator <GA-List-Request@AIC.NRL.NAVY.MIL>
Subject: Administrivia

Two items concerning the administration of GA-List:

1.  The list has grown quite large (over 300 addresses).
This presents no immediate problem -- in fact, the growth
is extremely welcomed -- but it does slow down the process of
sending issues out.  It would help if organizations that had
several members could form local bulletin boards to which I
could send a single message.  The larger organizations are
listed below.  If anyone wants to volunteer to organize local
bulletin boards, let me know the new address, and who I
should remove from my list.

	domain		members
	---------	--------
	bbn.com		15
	umich.edu	12
	cs.ucla.edu	10
	cmu.edu		 7
	arc.nasa.gov	 7

2. Many sites have adopted new names in the process of switching
to domain style addresses.  If it appears that GA-List is
not being sent to the current preferred address, please let
me know and I will update our list.

A comment:

Many of the postings consist of questions about GAs or
classifier systems to which the author hopes the community
will respond.  I notice that answers to these questions are
rarely offered, despite the presense of many active researchers
on the list.  Please take the time to share your experiences
with the rest of us, and keep the discussion bi-directional.
Thanks.

-- John

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

Date: 13 Oct 88 12:40 EST
From: POWELL DAVID J <POWELL@ge-crd.arpa>
Subject: GAs for control systems

I have been working on a program where the user can attach any program
(with run times from 1 second to 30 minutes), specify inputs allowed to
vary, outputs to be optimized on, parameter constraints, and rules of
thumb on which parameters to vary to influence the optimization
variables. My program will run the users program to an iterative
solution. To date, I have attached a DC-MOTOR program, a turbine design
program and a compressor program with mixed results. It is safe to
assume that these programs provide a non linear, discontinuous and
multi-dimensional domain.

The intent of allowing rules of thumb to be added is to quickly prune
the design domain. Typically, the designer has a limited deadline ( 2 -
14 days ) and the program cannot exhaust all possibilties. I will
follow the experts advice first before trying other types of
heuristics. This works very well if the user understands the design
domain as I get a lot of gain in optimization quickly. However, it has
some severe drawbacks:

1. It does not guarantee a global optimum.

2. It does not learn. It uses the simple rules of thumb of the user but
does not combine them into more complex or general rules to speed up
the search or to escape a corner that it has forced itself into.
Furthermore, the rules are usually based on an understanding of the
domain from previous experience with different constraints. Therefore,
the experience may or may not be valid for different portions of the
domain.

3. If the user has little knowledge then my current heuristics,hill
climbing and random search are very inefficient. Inefficiency on day 1
is tolerable if the program can become smarter so that following
application runs can be performed more quickly.

Recently, I have read about genetic algorithms and Classifier systems.
They both seem to have applicability to my problem. John Grefenstette
was very kind and sent me a copy of his GENESIS package which I am
currently experimenting with and trying to learn about your exciting
field of genetics.  Two initial questions that I am facing is:

	1. How to speed up GA if I have some rules of thumb without
	forcing the GA to a local optimum?

	2. Proper selection of 	population size and parameter settings.

I have also recently read about Classifier systems as described by
Holland in Machine Learning and his book on Induction. He seems to
address the solution to my problems with his classifier system. Before
spending a great deal of time to try an implement a classifier system
which may or may not prove successfull, I have asked the computer
science community (ARPANET) if there were any classifier systems
available for me to try out in order to learn and see if they are as
successfull as I hope them to be.  One recommendation from Arpanet was
to post a mail message on this bulletin board.

I realize that the description of my problem is very brief but I would
appreciate any suggestions as to possible directions or pitfalls that I
may experience before I delve too deeply into this area. If anyone has
a Classifier system or other system that addresses my problem or may
lead to better insights on a method of solution then I would really
appreciate it if you can send it to me.

Hopefully, there will be a good response so that I can learn quite a
bit and reply back to the genetic community with some better questions
and possibly some initial results.

My mail address is powell@crd.ge.com.

Thank You
Dave Powell	

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

Date: Thu, 20 Oct 88 14:15:34 PDT
From: rik%cs@ucsd.edu (Rik Belew)
Subject: AI genealogy

                             AI GENEALOGY
                     Building an AI family tree

Over the past several years we have been developing a collection of
bibliographic references to the literature of artificial intelligence
and cognitive science. We are also in the process of developing a
system, called BIBLIO, to make this information available to
researchers over Internet. My initial work was aimed at developing
INDEXING methods which would allow access to these citations by
appropriate keywords. More recently, we have explored the use of
inter-document CITATIONS, made by the author of one document to
previous articles, and TAXONOMIC CLASSIFICATIONS, developed by editors
and librarians to describe the entire literature.

We would now like to augment this database of bibliographic information
with "cultural" information, specifically a family tree of the
intellectual lineage of the authors. I propose to operationalize this
tree in terms of each author's THESIS ADVISOR and COMMITTEE MEMBERS,
and also the RESEARCH INSTITUTIONS where they work. It is our thesis
that this factual information, in conjuction with bibliographic
information about the AI literature, can be used to characterize
important intellectual developments within AI, and thereby provide
evidence about general processes of scientific discovery. A nice
practical consequence is that it will help to make information
retrievals from bibliographic databases, using BIBLIO, smarter.

I am sending a query out to several EMail lists to ask for your help
in this enterprise. If you have a Ph.D. and consider yourself a
researcher in AI, I would like you to send me information about where
you got your degree, who your advisor and committee members were, and
where you have worked since then.  Also, please forward this query to
any of your colleagues that may not see this mailing list. The
specific questions are contained in a brief questionnaire below, and
this is followed by an example. I would appreciate it if you could
"snip" this (soft copy) questionnaire, fill it in and send back to me
intact because this will make my parsing job easier.

Also, if you know some of these facts about your advisor (committee
members), and their advisors, etc., I would appreciate it if you could
send me that information as well. One of my goals is to trace the
genealogy of today's researchers back as far as possible, to (for
example) participants in the Dartmouth conference of 1956, as well as
connections to other disciplines. If you do have any of this
information, simply duplicate the questionnaire and fill in a separate
copy for each person.

Let me anticipate some concerns you may have. First, I apologize for
the Ph.D. bias. It is most certainly not meant to suggest that only
Ph.D.'s are involved in AI research. Rather, it is a simplification
designed to make the notion of "lineage" more precise. Also, be
advised that this is very much a not-for-profit operation. The results
of this query will be combined (into an "AI family tree") and made
publically available as part of our BIBLIO system.

If you have any questions, or suggestions, please let me know. Thank
you for your help.

Richard K. Belew
	Asst. Professor
	Computer Science & Engr. Dept. (C-014)
	Univ. Calif. - San Diego
	La Jolla, CA 92093
	619/534-2601
	619/534-5948  (messages)
	rik%cs@ucsd.edu

  --------------------------------------------------------------
			  AI Genealogy questionnaire
			Please complete and return to:
			        rik%cs@ucsd.edu


NAME:	

Ph.D. year:	

Ph.D. thesis title:

Department:

University:
Univ. location:	

Thesis advisor:	
Advisor's department:	

Committee member:	
Member's department:

Committee member:	
Member's department:

Committee member:	
Member's department:

Committee member:	
Member's department:

Committee member:	
Member's department:

Committee member:	
Member's department:

Research institution:	
Inst. location:
Dates:

Research institution:	
Inst. location:
Dates:

Research institution:	
Inst. location:
Dates:


 --------------------------------------------------------------
			  AI Genealogy questionnaire
                                  EXAMPLE

NAME:			Richard K. Belew	

Ph.D. year:		1986	

Ph.D. thesis title:	Adaptive information retrieval: machine learning 
			in associative networks

Department:		Computer & Communication Sciences (CCS)

University:		University of Michigan

Univ. location:		Ann Arbor, Michigan

Thesis advisor:		Stephen Kaplan	
Advisor's department:	Psychology	

Thesis advisor:		Paul D. Scott
Advisor's department:	CCS 	

Committee member:	Michael D. Gordon	
Member's department:	Mgmt. Info. Systems - Business School

Committee member:	John H. Holland	
Member's department:	CCS

Committee member:	Robert K. Lindsay	
Member's department:	Psychology

Research institution:	Univ. California - San Diego
			Computer Science & Engr. Dept.
Inst. location		La Jolla, CA
Dates:			9/1/86 - present			



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

From: pixar!cc@ucbvax.Berkeley.EDU (Charlie Conklin)
Received: by golden; 26 OCT 88 13:16:00 PDT
Subject: GAs for game tree search

 I am currently doing some experimentation with different methods of
game tree search, and am looking into genetic algorithms as methods
to pre-select nodes of the tree. I would like to join the gla mailing
list so that I main gain more information about GA's, and maybe come
across someone using them in an application similar to my own. My address
is below.

                Charlie Conklin ...!{ucbvax,sun}!pixar!cc

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

Date: Thu, 27 Oct 88 13:29:49 +0100
From: jvelasco@dit.upm.es (Juan Ramon Velasco Perez)
Subject: Apportionment of credit

I am a student in the Department of Telematic Engineering
at the Madrid University of Technology.
I am doing a research in Apportionment of Credit for Genetic Algorithms
applied to Machine Learning, and I would like to be added to the "GA-LIST".

I would be very please if you can send me some references about Apportionment
of Credit (or the name of people who work in this area).

With thanks in advance, I remain,

                        Yours sincerely,

                        Juan R. Velasco

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End of Genetic Algorithms Digest
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