
Genetic Algorithms Digest   Friday, February 14 1992   Volume 6 : Issue 4

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

Today's Topics:
	- hybrid systems - request for information
	- papers available --- ann, genetic algorithms
	- Learning in Search - request for information
	- New TCGA report
	- Engineering Design Centre to study application of GAs
	- CFP: BIOCOMPUTATION WORKSHOP
	- GAs applied to adaptive control - request for information

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

 Canadian AI Conference, Vancouver, (CFP 1/7)                 May 11-15, 1992
 COGANN, Combinations of GAs and NNs, @ IJCNN-92 (v5n31)      Jun 6,     1992
 ARTIFICIAL LIFE III, Santa Fe, NM                            Jun 15-19, 1992
 10th National Conference on AI, San Jose, (CFP 1/15)         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

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

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From: S.Khebbal@cs.ucl.ac.uk
Date: Thu, 16 Jan 92 11:37:10 +0000
Subject: Want information on hybrid systems

   Hello,

     I am about to do a comprehensive survey of Intelligent Hybrid Systems :
     systems that hybridise/incorporate one or several of the following 
     techniques: 
       Expert Systems, Neural Networks, Genetic Algorithms, Fuzzy Logic, 
       Rule Induction. 

       (eg. ES<->NN, NN<->GA, ES<->FL, GA<->FL, RI<->ES etc ). 

     I feel that these Hybrid systems fall into three categories and can be
     described as :

     1. Independent communicating hybrids - ie separate independent 
     intelligent systems that interact via some communication mechanism. 

     2. Hybrid systems that have another technique to replace one of its
     existing functions. As an example, using a neural network in an expert 
     system to do its pattern matching tasks.

     3. Hybrid systems that merge two techniques into one. ie the use of
     connectionist networks to do symbol processing tasks.

     I would be grateful for any information on work in any of these
     categories of Hybrid Systems, and about their possible applications.
    

			Thank you in Advance -- Sukhdev Khebbal
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 #  MAIL ADDRESS :                      |    EMAIL ADDRESS :                 #
 #       Sukhdev Khebbal,               |  skhebbal@uk.ac.ucl.cs  (UK sites) #
 #       Room 224,                      |  skhebbal@cs.ucl.ac.uk  (US sites) #
 #       Computer Science Department,   |------------------------------------# 
 #       University College London,     |    TELEPHONE NUMBER :              #
 #       Gower Street,                  |                                    #
 #       London WC1E 6BT.               |    LONDON (071) 387 7050 Ext 3664  #
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

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From: myself <T00BOR%DHHDESY3.bitnet@CUNYVM.CUNY.EDU>
Date:    MON, 27 JAN 92 15:49:08 MEZ
Subject: papers available --- ann, genetic algorithms

   papers available, hardcopies only.

   GENERAL ASYMMETRIC NEURAL NETWORKS AND
   STRUCTURE DESIGN BY GENETIC ALGORITHMS

   Stefan Bornholdt
   Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 2000 Hamburg 52

   Dirk Graudenz
   Institut f\"ur Theoretische Physik, Lehrstuhl E, RWTH 5100 Aachen,
   Germany.

   A learning algorithm for neural networks based on genetic algorithms is
   proposed. The concept leads in a natural way to a model for the
   explanation of inherited behavior. Explicitly we study a simplified
   model for a brain with sensory and motor neurons. We use a general
   asymmetric network whose structure is solely determined by an
   evolutionary process. This system is simulated numerically.
   It turns out that the network obtained by the algorithm
   reaches a stable state after a small number of sweeps.
   Some results illustrating the learning capabilities are presented.

   [to appear in Neural Networks]

   preprints available from:
   Stefan Bornholdt, DESY-T, Notkestr. 85, 2000 Hamburg 52, Germany.
   Email: t00bor@dhhdesy3.bitnet (hardcopies only, all rights reserved)

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

From: parker@taurus.cs.nps.navy.mil (gary parker)
Date: Tue, 28 Jan 92 12:12:45 PST
Subject: Learning in Search - request for information

   Request information dealing with the research done/ongoing
   in the field of "Learning in Search".  Specifically - an
   unknown search space with areas of varying degrees of
   obstacle density.

   E-mail:  parker@taurus.cs.nps.navy.mil

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

From: <@risc.ua.edu,@ua1ix.ua.edu:rob@galab2.mh.ua.edu>
Date: Mon, 27 Jan 92 14:58:36 CST
Subject: New TCGA report

   The Clearinghouse for Genetic Algorithms at the University of Alabama
   is pleased to announce the availability of the following technical report:

   "Task Allocation for Efficient Parallel Processing Using a Parallel Genetic
   Algorithm" by Daniel Joseph Smith (A Master's Thesis).
   TCGA Report #91008

   This report is a Master's thesis, so TCGA requests $9.00 ($12.00
   overseas) to offset the costs of copying, spiral binding, and shipping.
   To order, contact:

   Robert Elliott Smith
       Department of Engineering of Mechanics
       Room 210 Hardaway Hall
       The University of Alabama
       Box 870278
       Tuscaloosa, Alabama 35487
   <<email>> @ua1ix.ua.edu:rob@galab2.mh.ua.edu 
   <<phone>> (205) 348-1618
   <<fax>> (205) 348-6419    

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From: Richard Harris <richard@charles-cross.poly-south-west.ac.uk>
Date: Thu, 30 Jan 92 12:23:54 GMT
Subject: Engineering Design Centre to study application of GAs

   For general information !

   The Plymouth Polytechnic Southwest Engineering Design Centre

   Plymouth Polytechnic Southwest has recently received a substantial award
   from the U.K. Science and Engineering Research Council for the
   establishment of the sixth SERC Engineering Design Centre. The initial aim
   of the centre is to carry out fundamental research into the application of
   the Genetic Algorithm and related adaptive search techniques to
   engineering design. In this manner the techniques will be rendered
   accessible in a useful and understandable form, to engineering designers
   across a wide range of disciplines.

   The Design Centre is currently working in collaboration with engineers
   from the civil, mechanical and electrical sectors of the industry.
   Collaborative projects include the application of the Genetic Algorithm's
   processing capabilities to the design of gas turbine engines and to the
   multi-variable problems associated with the design of concrete arch dams.
   Other projects include the optimal design of FIR digital filters.

   It is also intended to investigate the application of the algorithm and
   related techniques to the design of manufacturing processes and to the
   establishment of 'best practice' programmes and energy efficient systems.

   For further information concerning the activities of the Engineering
   Design Centre please contact Dr I.C.Parmee (Centre Manager) on,

   ian@cx.psw.ac.uk

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

From: rsjuds@snll-arpagw.llnl.gov (judson richard s)
Date: Fri, 7 Feb 92 16:00:35 -0800
Subject: CFP: BIOCOMPUTATION WORKSHOP

   CALL FOR PAPERS  CALL FOR PAPERS  CALL FOR PAPERS  CALL FOR PAPERS

		   BIOCOMPUTATION WORKSHOP
	   Evolution as a computational process
		   June 22-24, 1992
		   Monterey, CA

   This workshop brings together biologists, physicists and computer
   scientists with interests in the study of evolution. The premise
   of the workshop is that natural evolution is computational process
   of adaptation to an ever changing environment. Mathematical theory
   and computer modeling are therefore ideally suited to study evolution
   and conversely, evolution may be used as a model system to study the
   computational processes of optimization and emergent pattern formation.

   Fifteen invited speakers will provide general reviews and summaries of
   their recent research.  Although oral presentations will be limited to
   invited speakers, original research contributions are solicited for
   poster sessions in the following areas:

   natural evolution
   artificial life
   genetic algorthms and optimization

   Authors should submit a single page abstract clearly stating their
   results by May 15 1992 to the Meeting Coordinator at the address
   listed below. Please indicate which of the above categories best
   applies to your paper.

   There will be no parallel sessions, and the workshop will be structured
   to stimulate and facilitate the active involvement of all attendees.

   Registration fees are $100 for full-time Ph.D. students and $250 for all
   others. Fees include admission to a banquet, at the Monterey aquarium,
   to be held on Monday night. (There is a $50 discount for students presenting
   posters at the meeting.)

   To obtain registration materials, abstract forms and housing information,
   please contact the meeting coordinator.

   Organizing Committee: Joachim Buhmann (LLNL), Michael Colvin (SNL), 
   Richard Durbin (MRC), Frank Eeckman (LLNL), Richard Judson (SNL), 
   Nora Smiriga (LLNL)

   For information only please contact eeckman@mozart.llnl.gov.
   Electronic submissions only at jb@s1.gov

   Meeting Coordinator:
   Chris Ghinazzi
   P.O. Box 808, L-426
   Lawrence Livermore Laboratory
   Livermore, CA 94550

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From: andersen@s1.elec.uq.oz.au (Hans Andersen)
Date: Mon, 10 Feb 92 12:08:32 EST
Subject: GAs applied to adaptive control - request for information

   I am searching for information on GAs applied to ADAPTIVE CONTROL.  I
would appreciate any correspondence on this topic.

   Thanks.

   Hans Christian Andersen
   Department of Electrical Engineering
   University of Queensland
   Brisbane, Australia
   Email: andersen@s1.elec.uq.oz.au

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End of Genetic Algorithms Digest
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