
Genetic Algorithms Digest   Monday, January 27 1992   Volume 6 : Issue 2

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

Today's Topics:
	- Re: Ordering information for GA proceedings and books
	- Re: Mutation, bitclimbing and test suites
	- Re: "Evolutionary Programming"
	- GAs and very fast simulated re-annealing: A comparison
	- Looking for information on Goldberg paper

**********************************************************************

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)

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

From: Alan C. Schultz (GA-List Moderator)
Date: Wed, 15 Jan 92 14:32:59 +0200
Subject: Re: Ordering information for GA proceedings and books

	Well, here's one I missed.  Add this to the list of information
	on GA-related books and proceedings (v6n1):

	Davidor, Yuval. (1991) Genetic algorithms and robotics, World
	Scientific. ISBN 9810202172

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

From: David Davis <70461.1552@CompuServe.COM>
Date: 14 Jan 92 09:30:16 EST
Subject: Re: Mutation, bitclimbing and test suites

   I intend for this is my last posting on the discussion of mutation, bit
   climbing, and test suites.  I just wanted to make sure that my conclusions
   were clear.

   Larry Eshelman has made several important points in a recent GA-List, and
   I would like to underscore them.  There has been a tendency in the GA
   field to discount mutation-based approaches to evolution, and my last
   posting might lead a casual reader to continue to discount them.  But
   these approaches can be quite effective, as Larry has shown using an
   ingenious algorithm of his own design on problems without the fatal flaws
   of the F1-F7 test suite.

   Larry also points out that I seem to have unjustly condemned all the
   members of the test suite because of the flaws of a subset of them.  This
   is true.  But I still think that one should never use F1-F7 as a test
   suite, for another reason mentioned in my earlier posting: One should
   never use any single test suite of problems in evaluating a GA using
   binary (and, I think, Gray coded) representation.  The reason is that, in
   experiments I have run using binary, and in other types of runs in binary
   carried out with Gil Syswerda when I was at BBN on problems like F1 and
   F6, it seems that the amount of difficulty the GA has in solving a problem
   is critically dependent on the amount of offset the problem has from the
   primary frequencies of the representation.  In looking at a graph of GA
   solution time as one shifts the representation, the result is not a linear
   increase or decrease.  It appears instead to be fractal.  Thus it is not
   simple to characterize a "representative" version of a mathematical
   function optimization problem in binary because the difficulties jump
   around so much. This is why I recommend (and will use in my own future
   work on binary representation, if I ever do any) multiple runs of the GA
   over representations shifted randomly-determined amounts.  The result, in
   the absence of theory showing how to predict the area under the difficulty
   graph, will be an approximation to this area.  I also recommend that
   researchers who do this publish their offset parameters with their
   results, so that other researchers can use the same test suites on their
   own versions of the algorithm.

   One nice feature of using numerical representation is that one doesn't
   have to worry about the problem of offset amounts, since the operators and
   representation are not sensitive to the alignment of the problem
   representation to the problem itself.  Another is that in my experience
   solutions to hard problems come much quicker.

   With all this said, I would like to reiterate two points from my posting
   and from Larry's posting, so that there is no mistake about what our main
   conclusions are and no impression that we disagree about them: 1. There
   are deep and pernicious problems with the test suite problems we have been
   using for 15 years that have led us to conclude that binary representation
   is much more effective at numerical optimization than it really is.  2.
   Mutation-based approaches to numerical function optimization are effective
   over a wider range of problems than we GA researchers have given them
   credit for; Larry's work and the earlier work of David Ackley (and his
   posting to GA-List in 1991) help to point this out.

   Lawrence "David" Davis
   Tica Associates

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

From: Inman Harvey <inmanh@cogs.sussex.ac.uk>
Date: Wed, 15 Jan 92 14:43:23 GMT
Subject: Re: "Evolutionary Programming"

   In GA Digest, Monday, January 13 1992   Volume 6 : Issue 1
   Melanie Mitchell <melaniem@lyra.eecs.umich.edu> writes

   >Subject: "Evolutionary Programming"
   >
   > I picked this up from a news group.  I'd never heard of this society
   > before.  Strange that there's no mention here of GAs!
   > ...
   >>				ANNOUNCING
   >>	     First Annual Conference on Evolutionary Programming
   >> ...

   On seeing that the Conference General Chairman is David B. Fogel, I think
   I can speculate why there is no mention of GAs.

   One of the early evolutionary programming approaches was that of
   "Artificial Intelligence through Simulated Evolution" by Fogel, L.J.,
   Owens, A.J. and Walsh, M.J. published by John Wiley (1966). This basically
   used (a) selection of the best Finite-State machine in a population of two
   and (b) a single genetic operator on the offspring of mutation of the
   state transition table (no crossover). Hence this was significantly
   different from GAs' later emphasis on crossover as the major operator, and
   use of sizeable populations.

   According to Goldberg ("Genetic Algorithms in Search, Optimization and
   Machine Learning", p. 105)
	   "The rejection of this work by the artificial intelligence
   community, more than any other single factor, was responsible for the
   widespread skepticism faced by more schema-friendly genetic algorithms of
   the late 1960s and mid-1960s."

	   I don't know how fair this comment is. If accurate, some might
   argue that there are parallels in the rejection of Rosenblatt's and
   colleagues' neural network ideas at the time Minsky and Papert published
   "Perceptrons". Anyway, there have been a recent number of papers by D.B.
   Fogel and others in Biological Cybernetics advocating extensions of
   Evolutionary Programming (in one paper a co-author is L.J. Fogel, which
   makes me think they are related - father/son?) containing quotes such as

	   "...Goldberg ... and Grefenstette et.al. have addressed the
   Traveling Salesman problem through the use of the genetic algorithm
   proposed by Holland (1975). This algorithm is a derivative of the
   Evolutionary Programming concept offered by Fogel (1962,1964; Fogel et.
   al. 1966)" Ref. (1) below

   I enjoyed the use of lower-case for ga's and upper-case for EPs, and
   generally get the impression that there is a bit of a rift between them.
   If this is so it is unfortunate. The main distinction between the two
   approaches nowadays seems to be whether or not mutation alone is any
   better than random search.  There has been a fair amount of discussion on
   this list and elsewhere recently on the surprising power of what the GA
   community calls Naive Evolution (mutation only - which sounds similar to
   Evolutionary Programming to me).

	   Inman Harvey

   (disclaimer - some of the above is guesswork and I am happy to be corrected!)
   Refs:
   1) D.B. Fogel 'An Evolutionary Approach to the Traveling Salesman Problem'
      Biol. Cybern. 60, 139-144 (1988)
   2) D.B. Fogel & J.W. Atmar "Comparing Genetic Operators with Gaussian
      mutation in Simulated Evolutionary Processes using Linear Systems"
      Biol. Cybern. 63 (2) 111-114 (1990)
   3) D.B. Fogel, L.J. Fogel & U.W. Porto "Evolving Neural Networks"
      Biol. Cybern. 63 (6) 487-493 (1990)

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

From: ingber@umiacs.UMD.EDU (Lester Ingber)
Date: Tue, 14 Jan 1992 04:48:55 EST
Subject: GAs and very fast simulated re-annealing: A comparison

   Genetic algorithms and very fast simulated re-annealing: A comparison
   by Lester Ingber and Bruce Rosen

   For awhile, most-current drafts of
   this (p)reprint and related papers can be downloaded via anonymous ftp
   from ftp.umiacs.umd.edu [128.8.120.23] in the pub/ingber directory.
   If you have problems with this, let me know and I will be glad to
   prepare a uuencoded copy to email to you.  This preprint is saga.ps.Z

   local% ftp ftp.umiacs.umd.edu
   [local% ftp 128.8.120.23]
   Name (ftp.umiacs.umd.edu:yourloginname): anonymous
   Password (ftp.umiacs.umd.edu:anonymous): [type in yourloginname]
   ftp> cd pub/ingber
   ftp> binary
   ftp> get README.file
   ftp> get file.ps.Z
   ftp> quit
   local% uncompress file.ps.Z
   local% lpr [-P..] file.ps [to your PostScript laserprinter]

	    ------------------------------------------ 
	   |           Prof. Lester Ingber            |
	   |          ______________________          |
	   | Science Transfer Corporation             |
	   | P.O. Box 857                703-759-2769 |
	   | McLean, VA 22101   ingber@umiacs.umd.edu |
	    ------------------------------------------ 

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

From: Richard Harris <richard@charles-cross.poly-south-west.ac.uk>
Date: Thu, 9 Jan 92 10:59:48 GMT
Subject: Looking for information on Goldberg paper

   Hello all,

   Does anybody out there know if D.Goldberg has completed his paper on
   proving performance theorems for real number representations for genetic
   algorithms.  (As mentioned in the Handbook of Genetic Algorithms, Ed.
   L.Davis, Van Nostrand Reinhold 1991) If so can anyone give me the
   reference for this paper so that I may obtain it for myself.

   My e-mail address is richard@cx.psw.ac.uk

   Many thanks.
   Richard Harris

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