
Genetic Algorithms Digest   Monday, May 26 1992   Volume 6 : Issue 17

 - 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 (see v6n5 for details)

Today's Topics:
	- Technical report available
	- Info on a new book
	- GA's and Protein folding refs?
	- Two papers from 91 Conference wanted
	- NN hyperplane animator
	- Genitor Code Package of Colorado State, D. Whitley
	- Handling restrictions
	- Need individuals to serve on dissertation committee
	- Benefits of crossover
	- Employment Announcement, GA related

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

 COGANN, Combinations of GAs and NNs, @ IJCNN-92 (v5n31)      Jun 6,     1992
 ARTIFICIAL LIFE III, Santa Fe, NM                            Jun 15-19, 1992
 Evolution as a computational process, Monterey (v6n9)        Jun 22-24, 1992
 ML-92, Machine Learning Conference, Aberdeen (v6n8)          Jul  1-3,  1992
 10th National Conference on AI, San Jose,                    Jul 12-17, 1992
 FOGA-92, Foundations of Genetic Algorithms, Colorado (v5n32) Jul 26-29, 1992
 COG SCI 92, Cognitive Science Conference, Indiana, (v5n39)   Jul 29-1,  1992
 ECAI 92, 10th European Conference on AI (v5n13)              Aug  3-7,  1992
 Parallel Problem Solving from Nature, Brussels, (v5n29)      Sep 28-30, 1992
 SAB92, From Animals to Animats, Honolulu (v6n6)              Dec  7-11, 1992

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

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From: dorigo@ICSI.Berkeley.EDU (Marco Dorigo)
Date: Mon, 27 Apr 92 15:11:13 PDT
Subject: Technical report available

   The following technical report is available and can be requested from:

   Marco Dorigo
   International Computer Science Institute
   1947 Center Street
   Suite 600
   Berkeley, California 94704-1105
   USA
   tel 	(510) 643-9153
   fax 	(510) 643-7684
   e-mail: dorigo@icsi.berkeley.edu.

   Title: Implicit Parallelism in Genetic Algorithms

   Authors:   A. Bertoni, M.Dorigo

   Report n : 92-012 - Politecnico di Milano - Italy

   Abstract: In this paper we revisit Holland's theorem on implicit
   parallelism. Holland demonstrated a O(n^3/c1) lower bound to the number of
   schemata usefully processed by the genetic algorithm in a population of n
   = c1 * 2^l binary strings, with c1 a small integer. We show that Holland's
   result is probabilistic in nature and we give similar results in the case
   of arbitrary c1.

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

From: zbyszek@unccvax.uncc.edu (Zbigniew Michalewicz)
Date: Wed, 29 Apr 92 15:37:35 EDT
Subject: Info on a new book

   A new book on genetic algorithms will appear shortly:

   Title of Book: "Genetic Algorithms + Data Structures = Evolution Programs"
   Author: Zbigniew Michalewicz
   Publisher: Springer-Verlag, (Artificial Intelligence Series)
   Publication date: June, 1992.
   ISBN#: 0-387-55387-8
   Other: Hardcover, 250pp., 48 figures, 29 tables.
   Price: approx. $41.00

   TABLE OF CONTENTS:

      Preface                                                 
      Table of Contents
      Introduction                                            
   Part I Genetic Algorithms
      1. GA: What Are They?                                      
      2. GA: How Do They Work?                                   
      3. GA: Why Do They Work?                                   
      4. GA: Selected Topics                                     
   Part II Numerical Optimization
      5. Binary or Float?                                        
      6. Fine Local Tuning                                       
      7. Handling Constraints                                    
      8. Evolution Strategies and Other Methods                  
   Part III Evolution Programs
      9. The Transportation Problem                                  
     10. The Traveling Salesman Problem                              
     11. Graph Problems, Scheduling, and Partitioning            
     12. Machine Learning                                         
     Conclusions                                             

   To order:

     * call 1-800-SPRINGE(R), i.e., 1-800-777-4643
       (in NJ call (201)348-4033)

     * FAX your request (201) 348-4505

   Thank you
   Zbigniew Michalewicz

   Mail: Department of Computer Science      E-mail: zbyszek@unccvax.uncc.edu
	 University of North Carolina        Phone:  (704) 547-4873          
	 Charlotte, NC 28223                 Fax:    (704) 547-2352          

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

From: peb@autodesk.com (Paul Baclace {Xanalogical User Interfaces})
Date: Thu, 30 Apr 92 14:40:21 PDT
Subject: GA's and Protein folding refs?

   I am looking for previous work on Protein Folding using GA's.  Previous
   work seems to use Neural Networks to recognize combinations and recall
   structure, but a GA could possibly speed up a series of single point
   optimizations, I think.  If you have done work in this are, please send me
   mail.

   Paul E. Baclace
   peb@autodesk.com

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

From: kcj@matilda.vut.edu.au (Kate Juliff)
Date: Sun, 3 May 92 15:07:57 EST
Subject: Two papers from 91 Conference wanted

   I would like to get hold of the two papers
   from the 1991 Conference. I believe they are anavailable in 
   Australia. Will some kind soul help me? The papers are by

	   Husbands & Mill 

   and
	   Muhlenbein, Schomisch & Born

   Thanks in advance,

   Kate Juliff
   kcj@matilda.vut.edu.au

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

From: booker@starbase.MITRE.ORG (Lashon Booker)
Date: Tue, 5 May 92 09:26:38 EDT
Subject: NN hyperplane animator

   This announcement may be of interest to the GA community.

   Lashon

      ***** Begin Included Message *****

   From: pratt@cs.rutgers.edu
   Date: Mon, 4 May 92 18:01:05 EDT
   Subject: Announcing the availability of a hyperplane animator 



                     -----------------------------------
				 Announcing 
			   the availability of an
		  X-based neural network hyperplane animator
                     -----------------------------------

			  Lori Pratt and Paul Hoeper
			    Computer Science Dept
			      Rutgers University


    Understanding neural network behavior is an important goal of many
    research efforts.  Although several projects have sought to translate
    neural network weights into symbolic representations, an alternative
    approach is to understand trained networks graphically.  Many
    researchers have used a display of hyperplanes defined by the weights
    in a single layer of a back-propagation neural network.  In contrast to
    some network visualization schemes, this approach shows both the
    training data and the network parameters that attempt to fit those
    data.  At NIPS 1990, Paul Munro presented a video which demonstrated
    the dynamics of hyperplanes as a network changes during learning.  This
    video was based on a program implemented for SGI workstations.

    At NIPS 1991, we presented an X-based hyperplane animator, similar
    in appearance to Paul Munro's, but with extensions to allow for
    interaction during training.  The user may speed up, slow down, or
    freeze animation, and set various other parameters.  Also, since it
    runs under X, this program should be more generally usable.

    This program is now being made available to the public domain.  The
    remainder of this message contains more details of the hyperplane
    animator and ftp information.

    ******

   1. What is the Hyperplane Animator?

   The Hyperplane Animator is a program that allows easy graphical display 
   of Back-Propagation training data and weights in a Back-Propagation neural
   network.

   Back-Propagation neural networks consist of processing nodes
   interconnected by adjustable, or ``weighted'' connections.  Neural network
   learning consists of adjusting weights in response to a set of training
   data.  The weights w1,w2,...wn on the connections into any one node can be
   viewed as the coefficients in the equation of an (n-1)-dimensional plane.
   Each non-input node in the neural net is thus associated with its own
   plane.  These hyperplanes are graphically portrayed by the hyperplane
   animator.  On the same graph it also shows the training data.

   2. Why use it?

   As learning progresses and the weights in a neural net alter, hyperplane
   positions move.  At the end of the training they are in positions that
   roughly divide training data into partitions, each of which contains only
   one class of data.  Observations of hyperplane movement can yield valuable
   insights into neural network learning.

   3. How to install the Animator.

   Although we've successfully compiled and run the hyperplane animator on
   several platforms, it is still not a stable program.  It also only
   implements some of the functionality that we eventually hope to include.
   In particular, it only animates hyperplanes representing input-to-hidden
   weights.  It does, however, allow the user to change some aspects of
   hyperplane display (color, line width, aspects of point labels, speed of
   movement, etc.), and allows the user to freeze hyperplane movement for
   examination at any point during training.

   How to install the hyperplane animator:

     1. copy the file animator.tar.Z to your machine via ftp as follows:

	ftp cs.rutgers.edu (128.6.25.2)
	Name: anonymous
	Password: (your ID)
	ftp> cd pub/hyperplane.animator
	ftp> binary
	ftp> get animator.tar.Z
	ftp> quit

     2. Uncompress animator.tar.Z

     3. Extract files from animator.tar with:
	tar -xvf animator.tar

     4. Read the README file there.  It includes instructions for running
	a number of demonstration networks that are included with this
	distribution.

   DISCLAIMER:
     This software is distributed as shareware, and comes with no warantees
   whatsoever for the software itself or systems that include it.  The
   authors deny responsibility for errors, misstatements, or omissions that
   may or may not lead to injuries or loss of property.  This code may not be
   sold for profit, but may be distributed and copied free of charge as long
   as the credits window, copyright statement in the ha.c program, and this
   notice remain intact.

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

From: Chris Whitley <chrisw@bach.ftcollinsco.NCR.COM>
Date: Thu, 7 May 92 15:35:41 MDT
Subject: Genitor Code Package of Colorado State, D. Whitley

   Hi!

   Anyone who has obtained a copy of the Genitor package from the
   genetic algorithm research group at Colorado State University
   (Darrell Whitley's crew), I would really appreciate your taking
   a moment to give me some feedback on your experience with it.

   I am the author of the code, and it is part of my thesis project.
   Any comments you give me will be anonymous, but may be included
   in my thesis.

   1. Why did you obtain Genitor code?  Was it merely curiosity,
   because you wanted to see how a genetic algorithm worked, because
   you were interested in the Genitor algorithm in particular,
   because you wanted to use it for a specific application, what?

   2. What, if any, application did you use the Genitor package for?

   3. How long did it take you to figure out how to use the Genitor
   package?  Was it a easy, difficult, painful?

   4. Did you read the documentation, or just look at the examples?

   5. Did you already have your own genetic algorithm code in house?
   If so, did Genitor replace any of your code?

   6. I'd be happy to hear anything you have to tell me about your
   experience, or to answer any questions you might have.

   Thanks in advance for answering!

   Chris Whitley
   Chris.Whitley@FtCollinsCO.NCR.COM
   (303)223-5100

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

From: Anthony Katz <ak3@doc.imperial.ac.uk>
Date: Fri, 8 May 92 12:37:37 BST
Subject: Handling restrictions

	   I am trying to use a GA solution for an optimisation problem. The
   problem is the restrictions of the model. To kill every illegal chromo
   after crossover would too rapidly diminish the population. (zero value ->
   kill ).  To modify the chromo minimally into a legal one, could be very
   complicated and take up a serious amouunt of time.

	   My experience has led me to beleive that there are two types of
   restrictions, fatal and non fatal. The non-fatal can be given a relatively
   lower value, and the fatal kill. Deciding which type of broken
   restriction` is which has proved difficult, bur it looks promosing.

	   Being a new-comer to this field, I would welcome advice from
   anyone on dealing with restrictions.

	   Thanks,
	   Anthony Katz                            ak3@doc.ic.ac.uk
	   Imperial College
	   London.

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

From: HOLMES@mscf.med.upenn.edu
Date: Wed, 22 Apr 92 17:38 EDT
Subject: Need individuals to serve on dissertation committee

   To whom it may concern:

      I am trying to locate university faculty in the Philadelphia area who
   are interested in GAs; I need at least one such individual to serve on a
   dissertation committee.  Is it possible to forward to me a list of
   participants in GA-List, especially those from Drexel University (if any)?
   Thanks for your help!

   John H. Holmes
   Clinical Epidemiology Unit
   University of Pennsylvania School of Medicine
   Philadelphia, PA 19104
   215-898-7838 (voice) 

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

From: Melanie Mitchell <melaniem@lyra.eecs.umich.edu>
Date:  Mon, 11 May 92 18:33:40 -0400
Subject: Benefits of crossover

   In reply to Mike Hobbs message on "Cross-over vs Mutation":

   There are a number of points to be made here.  First, crossover most
   certainly does not help on every problem.  One class of problems where it
   does not help was discussed in a paper by Stephanie Forrest and myself,
   "The performance of genetic algorithms on Walsh polynomials: Some
   anomalous results and their explanation", in the 1991 ICGA proceedings.
   One reason crossover did not work on the functions we were studying was
   that there was no information from lower order building blocks.  There are
   other reasons crossover can also fail to help as well.

   In general, it is an open (but very important) problem to characterize the
   class of problems on which crossover helps. The "building blocks
   hypothesis" says that crossover helps when short, low-order, fit schemas
   recombine to form even more highly fit higher-order schemas.  This is a
   start, but it is not specific enough.  There has been some work in the GA
   community on trying to understand in more detail when crossover will help.
   Some examples of some recent papers concerning this are:

   Eshelman et al., "Biases in the crossover landscape", in the 1989 ICGA
   proceedings.

   Manderick et al., "The genetic algorithm and the structure of the fitness 
   landscape", in the 1991 ICGA proceedings.

   Schaffer and Eshelman, "On crossover as an evolutionarily viable strategy",
   in the 1991 ICGA proceedings.

   Mitchell, Forrest, and Holland, "The royal road for genetic algorithms:  
   Fitness landscapes and GA performance", in Proceedings of the First 
   European Conference on Artificial Life (MIT Press, 1992).

   Forrest and Mitchell, "Towards a stronger building-blocks hypothesis: 
   Effects of relative building-block fitness on GA performance"
   (To appear in FOGA 2).

   Fogel and Atmar, "Comparing genetic operators with Gaussian mutations in
   simulated evolutionary processes using linear systems.  Biological
   Cybernetics 63, 111-114, 1990.  

   There are others as well, but this gives a start.  

   It may be that your application is one of the problems on which crossover
   won't help too much.  But on the other hand, you are using uniform
   crossover, which is highly destructive of schemas.  On certain problems
   single-point or two-point crossover will help and uniform crossover won't.
   There are a number of papers on this in the various ICGA proceedings,
   including the Schaffer and Eshelman paper listed above.  Also .06 per bit
   is a very high mutation rate, and thus mutation may be destroying any
   benefit crossover might give.

   Hope this is of some help.

   Melanie Mitchell
   AI Lab
   University of Michigan 

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

From: ds1@philabs.Philips.Com  (Dave Schaffer)
Date: Tue, 12 May 92 14:28:35 EDT
Subject: Employment Announcement, GA related

   INDUSTRIAL DYNAMICS RESEARCH

   Genetic Algorithms Project:

   The Industrial Dynamics Research Department of Philips Laboratories 
   is seeking highly motivated researchers to contribute to activities 
   in Genetic Algorithms (GAs). The project goals include applying GAs
   to real problems in design and manufacturing and conducting research
   to improve our understanding of GAs as successful adaptive processes.

   The ideal candidate has:

   - MS or PhD in Computer Science, Electrical Engineering, Operations
     Research or related fields.
   - Theoretical knowledge of and experience with genetic algorithms;
   - Skill at programming in C under the UNIX operating system;
   - A strong motivation to advance the state of the art in genetic 
     algorithms and apply this understanding to practical engineering 
     problems;
   - An ability to work interactively with a creative group of researchers 
     and experts from various application domains.

   Philips Laboratories is located on the east side of the Hudson River, 
   about an hour's drive north of New York City. We offer a competitive 
   benefits and salary package.
   Interested individuals should send their resumes to:

   Mary Beth Morley
   mba@philabs.philips.com
   (914) 945-6056
   Philips Laboratories
   345 Scarborough Road
   Briarcliff Manor, NY 10510

   Philips is an equal opportunity employer.

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End of Genetic Algorithms Digest
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