
Genetic Algorithms Digest   Tuesday, May 10, 1994   Volume 8 : Issue 15

 - 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:
	- Applications of GA to SPP/SCP problems (Re: v8n8) - 2 messages
	- Response to Evolver 2.0 Query (Re: v7n28)
	- 2D encoding query
	- info request
	- request: ga, TSP, and large numbers of cities ?
	- Postdoc
	- classifier systems and branch prediction?
	- evolutionary Fuzzy-systems
	- Bin Packing test cases & benchmarks
	- Long bit strings
	- AI'94 Workshop on Evolutionary Computation

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

The IEEE Conference on Evolutionary Computation, Orlando(v7n26) Jun 26-30, 94
FOGA94 Foundations of GAs Wkshop, Estes Park, Colorado(v7n26)Jul 30-Aug 3, 94
SAB94 3rd Intl Conf on Sim of Adaptive Behavior, Brighton(v7n11) Aug 8-12, 94
ECAI-94, 11th European Conference on AI, Amsterdam (v7n23)       Aug 8-12, 94
ECAI-94 Wkshp on Applied Genetic & Other Evol Algs, Amsterdam(v8n5) Aug 9, 94
IEEE/Nagoya Univ WW Wkshp on Fuzzy Logic & NNs/GAs, Japan(v7n33) Aug 9-10, 94
ISRAM94 Special Session on Robotics & GAs, Maui, Hawaii (v7n22) Aug 14-17, 94
Evolution Artificielle 94, Toulouse, France (v8n10)             Sep 19-23, 94
COMPLEX94 2nd Australian National Conference, Australia (v7n34) Sep 26-28, 94
PPSN-94 Parallel Problem Solving from Nature, Israel (v7n32)     Oct 9-14, 94
AI'94 Workshop on Evol Comp, Armidale, NSW, Australia (v8n15)      Nov 22, 94
EP95 4th Ann Conf on Evolutionary Programming, San Diego,CA(v8n6) Mar 1-4, 95
ICANNGA95 Intl Conf on Artificial NNs and GAs, France (v8n10)   Apr 18-21, 95
ECAL95 3rd European Conf on Artificial Life, Granada, Spain(v8n5) Jun 4-6, 95

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

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From: nagar@server.uwindsor.ca (Nagar Amit)
Date: Fri, 8 Apr 94 11:52:38 -0400
Subj: Applications of GA to SPP/SCP problems (Re: v8n8)

In response to my query about the applications of GA to SPP/SCP problems,
I received the following responses which I would like to share with the
other readers. Thanks to everyone who responded.

-Amit Nagar
nagar@server.uwindsor.ca

   From: David Levine <levine@mcs.anl.gov>
   Date: Thu, 31 Mar 1994 18:17:54 -0600
   Subject: set/spp

   I'm hoping to finish my PhD thesis at the end of next month: "A Parallel
   Genetic Algorithm for the Set Partitioning Problem".  I have a short
   paper in ICGA #5.

   The following references all have something avout Set Covering in them.

   @inbook{LiHiRiPa,
      author = "G. Liepins and M. Hilliard and J. Richardson and M. Palmer",
      editor = "D. Brown and C. White",
      title = "{G}enetic {A}lgorithms {A}pplications to {S}et {C}overing and
   {T}raveling {S}alesman {P}roblems",
      year = "19XX",
      booktitle = "OR/AI: The Integration of Problem Solving Strategies",
      publisher= "XXXX",
      address = "XXXX",
      pages = "29--57"
      }

   @inproceedings{RiPaLiHi89,
     author = "J. Richardson and M. Palmer and G. Liepins and M. Hilliard",
     editor = "J. Schaffer",
     title = "Some {G}uidelines for {G}enetic {A}lgorithms with {P}enalty
   {F}unctions",
     booktitle = "Proceedings of the Third International Conference on Genetic
   Algorithms",
     year = "1989",
     publisher = "Morgan Kaufmann",
     Pages ="191--197"
     }

   @book{Da91,
     author =  "L. Davis",
     title = "Handbook of Genetic Algorithms",
     publisher = "Van Nostrand Reinhold",
     address = "New York",
     year = "1991"
     }

   Finally, I don't recall it was GAs, but there is/was a Univ of
   Rochester tech report about parallel heuristics for SCP.

   regards --dave

   David Levine  levine@mcs.anl.gov  (708)-252-6735   Fax: (708)-252-5986
   MCS 221 C-216    Argonne National Laboratory   Argonne, Illinois 60439


   From: potter@pollux.cs.uga.edu (Don Potter)
   Date: Fri, 1 Apr 1994 10:06:37 +0500
   Subject: GA refs

   Hi,
   You should take a look at the papers by
   Liepins and papers by Potter (that's me)
   for GA set covering info.  We actually address
   multiple fault diagnosis but it's really
   a set covering problem.  See the Handbook of GAs
   , proceedings of the IEA/AIE conferences, and
   recent IEEE SMC journal (sept/Oct 93 vol 23 no 5,
   paper by Miller et. al), and the Applied Intelligence
   journal (vol 2, 1992).

   Also, please post or setup ftp access to your
   reference list.

   Thanks,
   Don Potter
   Graduate Coordinator, AI Programs
   and
   Associate Professor of Computer Science
   University of Georgia
   Athens, GA  30602-7405

   potter@pollux.cs.uga.edu


   From: Tal Grossman <tal@goshawk.lanl.gov>
   Date: Fri, 1 Apr 1994 08:42:57 -0700
   Subject: set covering

   Amit,
   I am currently working on Neural computation approach to the set covering
   problem (and on variations of the greedy algorithm).
   There are VERY few works on NNs for that problem and
   I know of know GA for purpose. There are many applications of GAs to related
   problems such as scheduling, general assignment, graph partitioning etc.
   I think GA is not a good approach for this type of problems as
   there is very little structure that allows the transfer of usefull
   information from one generation to the next. I tried all sorts of GA
   approaches on related problems, such as MaxClique.
   However, if you have a clever idea of how overcome this problem (e.g. by
   adding local search to the basic GA scheme) you should certainly try it.
   A similar approach for the MaxClique was presented at the DIMACS challenge
   by Fleurent and Ferland (they have found that even such a hybrid is
   outperformed in most cases by tabu search).
   You may also be interested in general approaches to such strategies like:
   "Hybrid Genetic Algorithms for Bin-Packing and related Problems" -
   Colin Reeves (preprint, contact crreeves@uk.ac.cov), or
   "Towards Solving Subset Selection Problems with the Aid of the Genetic
   Algorithm" - Lucasius and Kateman, in Parallel Problem Solving from
   Nature II, Manner and Manderick (Eds), Elsevier 1992.
   In any case, I would be very interested in what you find, both in your
   literature search and in your experiments with the problem. With a friend at
   the Weizmann Inst. we intend to make an experimental study of different
   algs. for the set covering (greedy, NNs, and other algs based on linear
   programming).  It would be nice to add GAs to the party. We already
   have a set of benchmarks, and welcome any addition to our test suite
   - so if you have any such instances, I will also be interested.
   The situation so far is that the simple greedy heuristic (or simple
   variations of it) is doing very well on most of the problems... Other
   more sophisticated algorithms have very hard time trying to beat it.
   We keep on trying.
   Good Luck with your trials,
   yours - Tal Grossman


   From: rogerw@penguin.mcs.utulsa.edu (Roger Wainwright)
   Date: Fri, 1 Apr 94 11:52:11 cst
   Subject: GA set covering and Partition problems

   My students and have published several papers using GAs for solving
   the set covering and various partitioning problems.  We do not
   have them available in electronic form as yet, but I will be happy
   to mail it to you by surface mail.
   Our work involves set covering, graph bisection,
   triangulation of a point set, and k-way graph bisection

   Give me your mailing address, and I wil send the papers
   to you.

   Roger L. Wainwright
   Associate Professor and Chairman of Computer Science
   Department of Mathematical and Computer Sciences
   University of Tulsa
   600 South College Ave.
   Tulsa, OK   74104-3189
   Ph:  (918) 631-3143
   Fax: (918) 631-3077
   rogerw@penguin.mcs.utulsa.edu

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

From: Sandip Sen <sandip@kolkata.mcs.utulsa.edu>
Date: Fri, 1 Apr 1994 14:25:28 -0600
Subject: Applications of GA to SPP/SCP problems (Re: v8n8)

In v8n8 of GA Digest Amit Nagar (nagar@server.uwindsor.ca)
inquires about applications of GA to set covering/partition
problems.

In the following paper, I have compared the performance
of a simple GA and an automatic schedule simulated annealing
algorithm on a class of randomly generated set covering
problems:

Sandip Sen, "Minimal cost set covering using probabilistic methods,"
Proc. 1993 ACM/SIGAPP Symposium on Applied Computing,
pages 157-164, 1993.

In the same proceedings, you will find the following paper
which also uses the set covering problem for illustrating
the usefulness of infeasible solutions in GA search:

D. Ansa Sekharan and Roger L. Wainwright, "Manipulating Subpopulations of
Feasible and Infeasible Solutions in Genetic Algorithms," Proc. ACM/SIGAPP
Symposium on Applied Computing, pages 118-125, 1993.


Sandip Sen
Department of Mathematical & Computer Sciences,
University of Tulsa,
600 South College Avenue,
Tulsa, OK 74104-3189.

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

From: cscbp@knuth.mtsu.edu (Dr. Chris Pettey)
Date: Fri, 15 Apr 1994 11:26:56 -0500 (CDT)
Subject: Response to Evolver 2.0 Query

Several months ago I posted a query for users of Evolver.  I got two
responses--both negative--so my colleague decided against buying.  I
am including both responses here in case anyone is interested.

Chrisila Pettey (cscbp@knuth.mtsu.edu)


   Return-Path: <C1894@UMSLVMA.UMSL.EDU>
   From:         Don Kummer <C1894@UMSLVMA.UMSL.EDU>
   Subject:      Evolver 2.0

   Chris, In response to your GA-list query regarding Evolver 2.0, I have
       used the product in a classroom setting as an illustration of a
       non-linear solution technique. Beyond setting the crossover and
       mutation rate there's not much one can do to alter the system.
       Even the population generation (ie number) is proprietary. I've
       spoken to the developers on a couple of occasions.

       In my area - Finance -- it provided an adequate illustration.
       But for someone in mgmt. science or interested in research, it
       would not be a good investment.

                            Don Kummer
                            School of Business
                            University of Missouri - St. Louis
                            314-553-6270 voice

   Return-Path: <fulkersw@smtplink.de.deere.com>
   From: William Fulkerson <fulkersw@smtplink.de.deere.com>

          My Company is listed as a satisfied user of Evolver on their
          promotional literature.  However, I have yet to meet anyone
          that has made a successful application.  The range of people
          this includes is from VP of Finance, pension fund portfolio
          manager, ..., engineer.

          In my recent work, the Excel Solver add-in has been
          more than adequate and faster than Evolver.

          BILL

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

From: "Andrew Hall"  <andrewh@logcam.co.uk>
Date: Fri, 15 Apr 94 14:09:25 BST
Subject: 2D encoding query

Has anyone considered encodings which are in more than one dimension?  For
instance, a real number is often encoded as a binary word, which is
intrinsically a one dimensional encoding.  What about problems where an encoding
could be two dimensional, ie a grid of bits?  I wonder how a GA could be adapted
to cope with this, and whether there are problems that exist that are somehow
more amenable to some kind of 2D encoding.

Any ideas?

Andy Hall                                               andrewh@logcam.co.uk
Logica Cambridge Ltd                                 +44 223 66343 Ext. 4878
Betjeman House
104 Hills Road
Cambridge CB2 1LQ.

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

From: greenwoo@lab.cc.wmich.edu (GREENWOOD GARRISON)
Date: Wed, 6 Apr 94 21:46:44 EDT
Subject: info request

I am beginning to investigate the use of genetic algorithms or
evolutionary strategies to solve the preventative maintenance (PM)
scheduling problem.  This problem is a variation of the time
schedule problem which is NP-hard.  Briefly, the PM problem is

1.  There are N preventative maintenance tasks.  Associated with
each task is a list of the specific required skills (e.g., mechanic
or electrician).

2.  Tasks are not preemptable.

3.  Only workers with the appropriate skills can be assigned
to a task.

4.  A worker cannot be assigned to more than one task at a
time.  Also, once assigned, the worker cannot be reassigned
until the task is completed.

5.  A list of workers (and their skills) is given.

Objective:  Find an appropriate assignment of workers to tasks
such that all tasks can be completed in minimum time.

I am interested in finding out if someone has done any work
on this type of problem.  I am particularly interested in
knowing the chromosome definition and the genetic operators
that were used.

Thanks,
Garry

garry.greenwood@wmich.edu

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

From: thorsten@archsci.arch.su.EDU.AU
Date: Fri, 8 Apr 1994 17:00:16 +1000
Subject: request: ga, TSP, and large numbers of cities ?

In his book about genetic algorithms [1], Michalewicz dedicates a whole
chapter to different ga approaches to the travelling salesman problem.
All the papers he lists try to code every single point (or edge) into
the genotytpe. For a large number of points, Michalewicz mentions
applications with as many as 1.2 million cities, this clearly is not a
useful approach.

One way to deal with high numbers of cities is to use a divide-and-conquer
algorithm. Litke [2] presented such an algorithm, using local optimization
for clusters with up to 10 cities (or subclusters).

I would like to know if someone has developed approaches to tsp's with
large numbers of cities using combinations of ga's and divide-and-conquer
algorithms (or is currently doing so). And what algorithms are currently
used for applictions with high numbers of cities (i.e. what is
'state-of-the-art')?

Thanks in advance

Thorsten Schnier

References:
1: Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs
Springer Verlag Berlin Heidelberg 1992

2: Litke, J.D.: An Improved Solution to the travelling salesman problem with
thousands of nodes, Communications of the ACM, Vol.27, No.12, pp1227-1236, 1984


Thorsten Schnier
Key Centre of Design Computing
University of Sydney
NSW 2006
thorsten@archsci.arch.su.edu.au

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

From: Stewart Wilson <wilson@smith.rowland.org>
Date: Thu, 21 Apr 94 14:42:21 EDT
Subject: Postdoc

	I would be interested in communicating with anyone
	completing a PhD and interested in doing a postdoc
	combining classifier systems and genetic programming.

	Stewart Wilson

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

From: "Theodore C. Belding" <streak@engin.umich.edu>
Date: Thu, 21 Apr 1994 20:53:18 +0100
Subject: classifier systems and branch prediction?

Does anyone know of any work done on implementing branch prediction
(in computer architecture) using classifier systems?  How about
implementing classifier systems in hardware (e.g., multiplexers, etc.)?
Thanks in advance!

Ted Belding            Ted.Belding@umich.edu or streak@engin.umich.edu
415 Lawrence, Apt. 6, Ann Arbor, MI 48104 USA   Phone: +1 313 994 9573
"Modern computing is basically a glamour industry." - Bruce Sterling

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

From: vasseur@uni-muenster.de
Date: Tue, 26 Apr 1994 13:10:18 +0200 (MES)
Subject: evolutionary Fuzzy-systems

 As as a french student of economy at the Fachhochschule Muenster (Germany),
I' ve started to take my diploma.
 The subjet of my dissertation is "the evolving of fuzzy-systems with help
of genetic algorithms and evolution-strategys, the possible applications in
management and business, perspectives and limits.

While there exist no collected works in the german speaking area, I would be
thankfull, if anybody could give me some information about scientific works
or paper concerning my subject.
Obviously, I will look for more information by myself and I' ll place my
research to everybody disponal.

Thanks Pierre-Andre Vasseur

Email:vasseur@uni-muenster.de.

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

From: rhh@matilda.vut.edu.au (Robert Hinterding)
Date: Sun, 17 Apr 94 16:16:23 EST
Subject: Bin Packing test cases & benchmarks

	In my research in grouping GAs, I have come across a new way of
	solving the Bin Packing problem using GAs.  I would like to know
	of any test cases or benchmarks for this problem.  Also references
	for other GA methods to solve this problem would be welcome.  I know
	of the work done by Emanuel Falkenauer and Kate Juliff.

Robert Hinterding
VICTORIA UNIVERSITY OF TECHNOLOGY            Email: rhh@matilda.vut.edu.au
P.O. Box 14428, Melb Mail Centre             Fax:   +61 3 688 4050
AUSTRALIA 3000                               Phone: +61 3 688 4686

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

From: David Levine <levine@mcs.anl.gov>
Date: Tue, 19 Apr 1994 09:40:58 -0500
Subject: Long bit strings

I'm looking for references to the longest bit strings that people have applied
GAs to.  I've been working on set partitioning problems which have a natural
binary representation.  Real world problems (primarily from airline crew
scheduling) typically have hundreds to tens of thousands of columns (bits).

Thanks --dave levine

David Levine  levine@mcs.anl.gov  (708)-252-6735   Fax: (708)-252-5986
MCS 221 C-216    Argonne National Laboratory   Argonne, Illinois 60439

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

From: xin@cspyr0.cs.adfa.oz.au (Xin Yao)
Subject: AI'94 Workshop on Evolutionary Computation
Date: Fri, 8 Apr 1994 14:27:12 +1000 (EST)

		CALL FOR PAPERS AND PARTICIPATIONS

	    AI'94 WORKSHOP ON EVOLUTIONARY COMPUTATION
	    Armidale, NSW, Australia, 22 November 1994

SCOPE
AI'94 Workshop on Evolutionary Computation will be held as part of AI'94
(The Seventh Australian Joint Conference on Artificial Intelligence) on 21--25
November 1994 in Armidale, NSW, Australia. People from all areas of evolutionary
computation are encouraged to participate in and submit their papers to the
workshop. The first such workshop was held on 16 November 1993 in Melbourne,
Australia, as the AI'93 Workshop on Evolutionary Computation.

Evolutionary computation is the study of computational systems which use ideas
and get inspirations from natural evolution and adaptation. Topics of this
workshop include, but are not limited to:
	+ Classifier Systems and Other Evolutionary Learning Systems
	+ Evolutionary Artificial Neural Networks
	+ Hybrid Learning Systems
	+ Comparisons Between Different Learning Systems
	+ Evolutionary Optimisation
	+ Self-Organisation
	+ Collective Behaviour
	+ Complexity in Evolutionary Systems
	+ Artificial Life
	+ Evolutionary Approach to Autonomous Robots
	+ Theories of Evolutionary Computation
	+ Parallel Implementations
	+ Applications


PAPER SUBMISSION AND PUBLICATION
Authors are invited to submit original papers describing experimental and/or
theoretical results from all areas of evolutionary computation. Four hard
copies of the *full* paper with no more than 25 11pt single-spaced,
single-column pages should be submitted to the following address before
*8 August 1994*.

Dr X. Yao
Department of Computer Science
University College, The University of New South Wales
Australian Defence Force Academy, Canberra, ACT 2600, Australia
Email: xin@csadfa.cs.adfa.oz.au
Phone: +61 6 268 8819
Fax: +61 6 268 8581

Authors are strongly encouraged to prepared their manuscripts in LaTex. You
may obtain the style file from the publisher's server
svserv@vax.ntp.springer.de under directory /tex/latex. The file name is
llncs.zip. All accepted papers will be published by Springer-Verlag as a
volume in Lecture Notes in Artificial Intelligence. Authors of accepted papers
are expected to present their papers at the workshop. Notification to authors
will be sent out on *12 September 1994*. The revised final papers should be
submitted before *17 October 1994* in order to be included in the proceedings.

WORKSHOP ORGANISING COMMITTEE
A/Prof D. Abramson 	Griffith University
Dr E. Lewis 		University College, UNSW, ADFA
Dr B. Marksj\"{o} 	CSIRO DBCE, Melbourne
Dr H.B. Penfold 	University of Newcastle
Dr X. Yao (Chair)	University College, UNSW, ADFA

IMPORTANT DATES
8 August 1994 	 	Submission of full papers
12 September 1994  	Notification of acceptance/rejection to authors
17 October 1994 	Submission of revised final papers

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

