
Genetic Algorithms Digest   Thursday, January 14, 1993   Volume 7 : Issue 1

 - Send submissions to GA-List@AIC.NRL.NAVY.MIL
 - Send administrative requests to GA-List-Request@AIC.NRL.NAVY.MIL
 - anonymous ftp archive: FTP.AIC.NRL.NAVY.MIL (Info in /pub/galist/FTP)

Today's Topics:
	- GA for neural net design
	- Weapon  Assignment using GAs
 	- Request for classifier code
	- Kalyanmoy Deb - change of address
	- GAs in Control
	- optimization of discrete event simulations
	- Looking for code for using GAs in scheduling problems
	- Symposium on Pattern Formation
	- CFP: IJCAI-93 Workshop, Machine Learning and Knowledge Acquisition
	- VFSR v6.30 now in Statlib
	- Educational Discount on GA software

----------------------------------------------------------------------
****************************************************************************

CALENDAR OF GA-RELATED ACTIVITIES: (with GA-List issue reference)

Symposium on Pattern Formation, Claremont CA (v7n1)             Feb 12-13, 93
ICNN93, IEEE Intl. Conf. on Neural Networks, Calif (v6n24)      Mar 28-01, 93
ECML-93, European Conf. on Machine Learning, Vienna (v6n26)	Apr 05-07, 93
Foundations of Evolutionary Computation WS, Vienna (v6n34)      Apr     8, 93
Intl. Conf. on Neural Networks and GAs, Innsbruck (v6n22)       Apr 13-16, 93
ECAL-93, 2nd European Conference on A-Life, Brussels (v6n31)    May 24-26, 93
ANN93, IEE Intl Conf on Artificial Neural Nets, Brighton        May 25-27, 93
ICGA-93, Fifth Intl. Conf. on GAs, Urbana-Champaign (v6n29)     Jul 17-22, 93
COLT93, ACM Conf on Computational Learning Theory, UCSC (v6n34) Jul 26-28, 93
Machine Learning & Knowledge Acq. Workshop (IJCAI), France (v7n1)  Aug 29, 93
ISEC-94 Int. Symp. on Evolutionary Computation, Orlando (v6n40) Jun 25-30, 94

(Send announcements of other activities to GA-List@aic.nrl.navy.mil)

****************************************************************************
------------------------------

From: KRAO%RPTVX0@gmr.com
Date: Tue, 1 Dec 1992 07:36 EST
Subject: GA for neural net design

   I have two GA programs for integer, and floating point optimization which
   i like to share in excahnge for a neural net trainer and evaluation
   program that we can develop together or otherwise. please call
   313-280-6664 or email.

   Thanks.Ken

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

From: Jim Van Zandt <jrv@mbunix.mitre.org>
Date: Tue, 01 Dec 92 13:35:08 EST
Subject: Weapon  Assignment using GAs

   In GA-List v6n39, Jean Berger asked for:

     Works on weapon assignment (or related resource allocation problem)
     using GAs ?  Did anyone know about past and current work on the
     weapon assignment problem using GA (combinatorial optimization and
     resource allocation problem) ?

   It happens I ran across this a couple of days ago:

	   William A. Metler, Fred L. Preston, & Jim Hofmann, "A Suite of
	   Weapon Assignment Algorithm for a SDI Mid-Course Battle Manager",
	   NRL Memorandum Report 6713, AD-A229 189, September 19, 1990.

   Metler and Preston are at AT&T Bell Laboratories.  Hoffman is at the
   Integrated Warfare Technology Branch, Information Technology Division,
   of the Naval Research Laboratory.  

   Their report discusses several integer programming formulations of
   weapon target assignment, and several algorithms for solving them
   (including GAs).
			    - Jim Van Zandt <jrv@mbunix.mitre.org>

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

From: powell@geneva.crd.ge.com (Powell)
Date: Wed, 23 Dec 92 09:42:19 EST
Subject: Request for classifier code

   I am co-teaching a course in GA and CLASSIFIER system using
   Goldberg's book and Hollands book. I have obtained from the
   GA ftp site some good code for the students to use with GA's.

   However, I have not come across any codes for classifier systems.
   Does anyone have any classifier system code or pointers to such
   code for the students to use this upcoming semester?

   Regards
   Dave

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

From: 0000 kalyanmoy deb <deb@iitk.ernet.in>
Date: Mon, 11 Jan 93 10:20:21 ist
Subject: Kalyanmoy Deb - change of address

   This is to inform you that I have left USA and joined 
   the Mechanical Engineering Depatment at Indian Institute
   of Technology, Kanpur on 28th December, 1992. My new
   e-mail address is deb@iitk.ernet.in and postal address is
   as follows:

   Kalyanmoy Deb
   CAD Project
   Department of Mechanical Engineering
   Indian Institute of Technology, Kanpur
   Pin 208 016, INDIA
   Fax Number: 091 512 250260 or 091 512 250007
   Phone Number: 091 512 250151 Ext. 2302 (Office)

   Even though we are thousands of miles away, by e-mail we
   are only three-to-four hours away. So, lets keep in touch.
   Thanks very much.
   Deb

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

From: carlos fonseca <fonseca@acse.sheffield.ac.uk>
Date: Tue, 8 Dec 92 15:24:02 GMT
Subject: GAs in Control

   I would liked to learn about what people are using GAs for in the Control
   Engineering field. References to published papers, or even draft copies of
   these sent to me by e-mail, would be particularly appreciated. I am to
   write an overview of GAs in Control as part of my doctoral work.

   Provided there is sufficient response, I also intend to submit a review to
   GA-List.

   Thanks for any help,
   Carlos

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

From: William Fulkerson <fulkersw@smtplink.de.deere.com>
Date: Tue, 08 Dec 92 11:33:27 CDT
Subject: optimization of discrete event simulations

   I want to produce a near optimum schedule for a foundry process of
   core-pour-mold operations.  Sub-optimization at the process level leads to
   poor performance.  A rule based approach is difficult to maintain.

   I propose to use GA's with an existing GPSS-H simulation as the fitness
   function.  GPSS-H control language can be used to make calls to external
   FORTRAN (only) programs to perfrom the selection-reproduction-mutation
   task.

   Questions:

   Has anyone had experince with this type of application?
   What is a good source of FORTRAN code for GA's?

   William F. (Bill) Fulkerson           
   Deere & Company Technical Center      
   3300 River Drive                     
   Moline IL 61265-1792
   USA              

   (309) 765-3797 voice   (309) 765-3882 secretary    (309) 765-3807 fax
   fulkersw@smtplink.de.deere.com Internet   4355311@mcimail.com MCI-mail

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

From: WU55@WILMA.WHARTON.UPENN.EDU
Date: Mon, 14 Dec 92 01:24 EDT
Subject: Looking for code for using GAs in scheduling problems

  Hi,

  Is there any place I  can get a program, preferably source code of any high
  level language, on applying GA to scheduling( either job shop or flow shop)
  problems?

  I will appreciate it if anyone could provide me some reference.

  Thanks in advance,
		  Don

  Don Wu, Dpet. of Decision Sciences, The Wharton School of the University of
  Pennsylvania, Philadelphia, PA 19104-6366
  email address: wu55@wharton.upenn.edu 

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

From: "Robert M. Keller" <keller@jarthur.Claremont.EDU>
Date: Tue, 22 Dec 92 15:16:44 PST
Subject: Symposium on Pattern Formation

			SYMPOSIUM on PATTERN FORMATION

			     February 12-13, 1993
			      Harvey Mudd College
			  Claremont, California 91711

  The   symposium   will   provide  a   focus   on  pattern   formation  from
  multidisciplinary  vantage points, particularly  on aspects  of interest to
  biologists,  computer scientists,  mathematicians, and physical scientists.
  It will examine current questions in pattern formation within each of these
  fields and also with cross-disciplinary perspectives.  The area  of pattern
  formation  includes formation  of  both  natural  and  artificial  cellular
  organisms,  formation  of patterns  on  and  within  these  organisms,  and
  space-time  growth patterns.  Of major concern is the formation of emergent
  patterns  through  the  actions  and  interactions  of many semi-autonomous
  units, none of which directs or has full knowledge of the overall process.

  Topics of interest include, but are not limited to:
       Cell growth		Fractals		Morphogenesis
       Cellular automata	Genetic algorithms	Osmotic growth
       Chaotic patterns		Genetic patterns	Percolation theory
       Emergent computation	L-systems		Reaction-diffusion
       Feature formation		

  Partial list of invited speakers and their topics

  Richard Belew, University of California, San Diego  
  Interposing a model of development between neural networks and 
    genetic algorithms

  Bruce Boghosian, Thinking Machines Inc.
  Cellular automata fluids

  Leah Edelstein-Keshet, University of British Columbia  
  Theories of pattern formation based on short and long-ranged interaction

  Stephanie Forrest, University of New Mexico     
  Emergent computation in the immune system

  Scott Fraser, Caltech
  Patterning of the developing brain

  John Gerhart, University of California, Berkeley 

  Rob Shaw, MacArthur Fellow              
  Transitions to turbulence in a reaction-diffusion system

  David Soll, University of Iowa                 
  Rhythmic behavior of cells in chemotactic waves during 
      dicytostelium aggregation

  Requests for participation 

  Requests for participation by  researchers, faculty, and students should be
  directed  to one  of the organizing  committee  listed  below.   Applicants
  should state briefly why they desire to participate and indicate the nature
  of their contribution, if any.  The  number of participants may be  limited
  due  to physical constraints.  Contributed  papers  are welcome  and  it is
  anticipated that  a proceedings will be  published. The final selection  of
  papers will be  made by the organizing  committee in consultation  with the
  advisory board.   A registration fee of $75 U.S. will be charged to  defray
  costs.  The fee will include two lunches and  one dinner  at the conference
  site.  Student participation is  encouraged  and it is  expected that  some
  scholarships will be available.

  Organizing committee

  T.J.  Mueller, Biology (chair)   mueller@hmcvax.claremont.edu, 909-621-8561
  Robert Keller, Computer Science  keller@jarthur.claremont.edu, 909-621-8483
  Robert Borrelli, Mathematics    borrelli@hmcvax.claremont.edu, 909-621-8023
  Stavros Busenberg, Mathematics busenberg@hmcvax.claremont.edu, 909-621-8023
  Harvey Mudd College        
  Claremont, CA 91711

  Symposium advisory board

  Leah Edelstein-Keshet, University of British Columbia
  Scott Fraser, Caltech
  David Goldberg, University of Illinois
  J.D. Murray, University of Washington
  Clifford Pickover, IBM Watson Research Center

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

From: tecuci@aic.gmu.edu (Gheorghe Tecuci)
Date: Tue, 29 Dec 92 13:31:23 EST
Subject: CFP: IJCAI-93 Workshop, Machine Learning and Knowledge Acquisition

                        CALL FOR PAPERS

                       IJCAI-93 WORKSHOP
           MACHINE LEARNING AND KNOWLEDGE ACQUISITION:
   Common Issues, Contrasting Methods, and Integrated Approaches

                29 August 1993, Chambery, France

  Machine learning and knowledge acquisition share the common goal of
  acquiring and organizing the knowledge of a knowledge-based system.
  However, each field has a different focus, and most research is still done
  in isolation from each other. The focus of knowledge acquisition has been
  to improve and partially automate the acquisition of knowledge from human
  experts. In contrast, machine learning focuses on mostly autonomous
  algorithms for acquiring or improving the organization of knowledge, often
  in simple prototype domains. Also, in knowledge acquisition, the acquired
  knowledge is directly validated by the expert that expresses it, while in
  machine learning, the acquired knowledge needs an experimental validation
  on data sets independent of those on which learning took place. As machine
  learning moves to more 'real' domains, and knowledge acquisition attempts
  to automate more of the acquisition process, the two fields increasingly
  find themselves investigating common issues with complementary methods.
  However, lack of common research methodologies, terminology, and
  underlying assumptions often hinder a close collaboration.  The purpose of
  this symposium is to bring together machine learning and knowledge
  acquisition researchers in order to facilitate cross-fertilization and
  collaboration, and to promote integrated approaches which could take
  advantage of the complementary nature of machine learning and knowledge
  acquisition.

  Topics of interest include, but are not limited to, the following:
  Case Studies
    Case studies of integrated ML/KA methods, with analysis of 
    successes/failures; integrated architectures for ML and KA; 
    interactive learning systems, automated knowledge acquisition 
    systems;
  Comparative Studies
    Comparative studies of KA and ML methods solving similar problems (e.g.,
    knowledge base refinement methods in KA versus theory revision methods in
    ML, constructive induction in ML versus knowledge elicitation in KA).
    Analysis of the complementarity of the KA and ML approaches to knowledge
    base construction (e.g. KA primarily addresses the problems of KB
    elicitation and refinement, while ML primarily addresses issues of KB
    refinement and optimization).
  Hard Problems
    Analysis of hard problems in KA or ML that could be simplified by
    employing techniques from the other area, as well as presentation of
    specific solutions (e.g. the problem of new terms in ML could be
    simplified by employing knowledge elicitation techniques developed in KA;
    the credit/blame assignment problem in ML could be simplified by employing
    knowledge refinement techniques developed in KA; KA of problem solving
    rules could be automated by using apprenticeship learning techniques);
  Knowledge Representation
    Knowledge representation issues in KA and ML (adequate representations for
    KA, adequate representations for ML, approaches to knowledge
    representation in integrated ML/KA systems like translation between
    representations, common representations, etc.);
  Key Issues
    Key issues in ML or KA (e.g. dynamic versus static knowledge 
    acquisition or learning, the role of explanations in KA and ML, 
    the validation of knowledge in KA and ML);
  Overviews
    Overviews of the state-of-the-art of ML, KA or of the 
    integration of ML and KA,
  Position Papers
    Position papers on methodology for integrated ML/KA systems or on
    improving the collaboration between the ML and KA communities.

  It is recommended that the papers make explicit the research methodology,
  the underlying assumptions, definitions of technical terms, important
  future issues, and potential points of collaboration. They should not
  exceed 15 pages. The organizers intend to publish a selection of the
  accepted papers as a book or the special issue of a journal. They
  encourage the authors to take this into account while preparing their
  papers.  The format of the workshop will be paper sessions with discussion
  at the end of each session, and a concluding panel on the integrated
  approaches, guidelines for successful collaboration, and concrete action
  items. The number of the participants to the workshop is limited to 40.
  Each workshop attendee must also register for the IJCAI conference and
  must pay an additional 300FF (about $60) fee for the workshop.  One
  student attending the workshop and being in charge of taking notes will be
  exempted from the additional 300 FF fee. Volunteers are invited.

  WORKSHOP Co-CHAIRS

  Smadar Kedar           Yves Kodratoff       Gheorghe Tecuci
  NASA Ames & Inst.for   CNRS & Universite    George Mason Univ.&
  Learning Sciences      de Paris-Sud         Romanian Academy
  (kedar@ils.nwu.edu)    (yk@lri.lri.fr)      (tecuci@aic.gmu.edu)

  PROGRAM COMMITTEE

  Ray Bareiss, Institute for the Learning Sciences
  Catherine Baudin, NASA Ames
  Guy Boy, European Inst. of Cognitive Sciences and Eng.
  Brian Gaines, University of Calgary
  Matjaz Gams, Jozef Stefan Institute
  Jean-Gabriel Ganascia, Univ. Pierre and Marie Curie
  Nathalie Mathe, European Space Agency and NASA Ames
  Ryszard Michalski, George Mason University
  Raymond Mooney, University of Texas at Austin
  Katharina Morik, Dortmund University
  Mark Musen, Stanford University
  Michael Pazzani, Univ. of California at Irvine
  Luc De Raedt, Catholic University of Leuven
  Alan Schultz, Naval Research Laboratory
  Mildred Shaw, University of Calgary
  Maarten van Someren, University of Amsterdam
  Walter Van de Velde, University of Brussels

  ADDRESS FOR CORRESPONDENCE

  Gheorghe Tecuci
  Artificial Intelligence Center, Computer Science Department
  George Mason University, 4400 University Dr., Fairfax, VA 22030
  email: mlka93@aic.gmu.edu, fax: (703)993-3729

  SUBMISSIONS

  Four copies of the papers (five to fifteen pages in length) should 
  arrive at the above address by March 31, 1993.
  Notification of acceptance or rejection will be sent by May 10.
  Final papers should arrive by June 10, 1993.

  Those who would like to attend without a presentation should send 
  a one to two-page description of relevant research interests and a 
  list of selected publications.

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

From: Lester Ingber <ingber@alumni.cco.caltech.edu>
Date: Mon, 7 Dec 1992 14:24:48 -0800
Subject: VFSR v6.30 now in Statlib

  Very Fast Simulated Reannealing (VFSR)

  vfsr v6.30 is now in Statlib (login as statlib to lib.stat.cmu.edu, file
  vfsr is in directory general).  If you already have vfsr v6.25 from Netlib
  (login as netlib to research.att.com, file vfsr.Z is in directory opt),
  this can be updated using a patch I'd be glad to send on request.

  v6.30 fixes a bug encountered for negative cost functions, and adds some
  printout to make your bug reports and comments easier to decifer.

  Lester

    ||  Prof. Lester Ingber               ingber@alumni.caltech.edu  ||
    ||  P.O. Box 857                                                 ||
    ||  McLean, VA 22101       703-848-1859 = [10ATT]0-700-L-INGBER  ||

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

From: emergent@aol.com
Date: Tue, 01 Dec 92 19:04:38 EST
Subject: Educational Discount on GA software

  Emergent Behavior is pleased to announce our new educational discount
  program.  We are making MicroGA available for a special discount to
  professors interested in teaching classes using GAs.  

  Here is some information about MicroGA.

  MicroGA is an object oriented system for solving problems using Genetic
  Algorithms.  It uses the C++ language to give it both power and
  flexibility.  In addition, it is surprisingly easy to use.

  Included with MicroGA are sample programs which show how genetic
  algorithms can be used for resource allocation and to optimize a traveling
  salesman problem.  A sample MacApp interface is included with the
  Macintosh version which implements: multiple documents, printing, and
  background processing.  The MS Windows version includes a similar program
  created using Borland's Object Windows Li brary.

  Also included with MicroGA is the Galapagosx code generation system.
  Thispowerful utility allows users to quickly create complete applications
  without writing any C++ code. It also allows programmers to quickly
  prototype their advanced applications.  All the user needs to do is fill
  in a dialog box defining the constraints and variables for the problem.
  Galapagos then writes the necessary source files.

  MicroGA is also built to be expandable.  It allows programmers to add new
  genetic representations, crossover schemes, and population types.  This
  makes it a great tool for research or education.

  MicroGA is currently available for Macintosh and IBM PC compatible
  computers.  The Macintosh version requires a Macintosh II or higher level
  machine.  It also requires MPW and a C++ compiler.  A 68881 math
  co-processor is recommended.  The PC compatible version requires MS
  Windows version 3.0 or higher and Turbo/Borland C++.  All source code is
  included.  Compiled applications can be made using MicroGA and sold
  without license fee.

  Includes:
  * Library with Source Code
  * Three Sample Programs with Source
  * Sample MacApp/OWL Interface with Source
  * Galapagos Code Generator
  * 95 page manual with tutorial and detailed member by member technical
  reference.
  * Special graphics objects for displaying results.

  [Ed's Note: Pricing information has been deleted.  If you are interested
  in this, please contact the sender for more information. -- Alan]

  Steve Wilson
  Emergent Behavior
  635 Wellsbury Way
  Palo Alto, CA 94306
  (415)494-6763
  fax: (415) 494-0570
  emergent@aol.com

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End of Genetic Algorithms Digest
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