HDTenEightyP/NetNews huggingface dataset:
From: rudolph@lucky.informatik.uni-dortmund.de (Guenther Rudolph)
Newsgroups: comp.ai.genetic
Subject: Re: Tabu Search !
Date: 21 Jan 1993 17:09:21 GMT
Organization: CS Department, Dortmund University, Germany
Distribution: world
NNTP-Posting-Host: lucky.informatik.uni-dortmund.de

Chao-Hsien Chu asked:

> Another GA related methodology called "Tabu Search" (mainly
> by Professor Glover at Univ. of Colorado).
>
>  1. Have anyone compared GA with Tabu search?
>
> (...)

Yes! I know that Stefan Voss has done a lot of work in this area.
He compares GAs, Simulated Annealing and Tabu Search methods. To
get more information and some references you should contact him
directly via VOSS@BWL.BWL.TH-DARMSTADT.DE .

Bye,

Guenter


------------------------------------------------------------------
Guenter Rudolph			University of Dortmund
				Computer Science Department, LS XI
Phone : +49-231-755-2637	P.O. Box 50 05 00
Fax   : +49-231-755-2450	D(W)-4600 Dortmund 50
Telex : 822465 unido d		Germany

e-mail: rudolph@ls11.informatik.uni-dortmund.de
	rudolph@unido.uucp
------------------------------------------------------------------
From: kzhan@sgi2 (Kaijun Zhan)
Newsgroups: comp.ai.genetic
Subject: how to subscribe this group?
Date: 25 Jan 1993 15:16:02 GMT
Organization: Department of Mathematics & Statistics, Univ. of Guelph
NNTP-Posting-Host: sgi2.mathstat.uoguelph.ca
X-Newsreader: TIN [version 1.1 PL8]

A friend of mine is interested in this group. but he has no access to the usenet, although he has email, ftp, telnet. now I am wondering if he could use email
to subscribe this group to get article posted here? if so, where and who should
be contacted? (listserver)

--
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~        
 KAIJUN S. ZHAN                          || (519)824-4120 Ext. 8325/6558        
 kzhan@sgi2.mathstat.uoguelph.ca         || (519)823-9238 (home)                
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 *********** please use email reply whenever possible ******************
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: dnb@pumphouse.denver.sgi.com (David Baumgartner)
Newsgroups: comp.ai.genetic
Subject: Lisp source code
Date: 25 Jan 93 20:17:42 GMT
Organization: Silicon Graphics, Inc.

Does anyone have a copy of the Lisp source code that John Koza
listed in his book, Genetic Programming?

Thanks,


-- 
-----
David Baumgartner
Northwest Region Technology Center      (303) 796-0022
Silicon Graphics Computer Systems
Internet: dnb@denver.sgi.com        UUCP: {ames,ucbvax,decwrl,sun}!sgi!dnb

- Reality is like quantum physics, it depends on what questions you ask. -
Newsgroups: comp.ai.genetic
From: al@gtx.uucp (Alan Filipski)
Subject: Re: Evolution vs Coevolution
Organization: GTX Corporation, Phoenix AZ
Date: Mon, 25 Jan 1993 21:09:20 GMT

In article <1993Jan24.103719@IASTATE.EDU> danwell@IASTATE.EDU (Daniel A Ashlock) writes:
>   A function optimizer has a global standard of fitness, the height of the
>function being optimized.  A GA playing prisoner's dillemma has a changing
>fitness function based on the particular mix of player's in the algorithm.
>I call these two sorts of genetic algorithms evolutionary and coevolutionary.

would it not be more accurate, in terms of existing biological usage,
to talk about "hard" vs. "soft" selection? "coevolution" usually refers
to the evolution of distinct, interacting populations, such as
host/parasite, not intraspecific competition as you imply in the above
example. it's not a good idea to change the meaning of existing terms.


  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 ( Alan Filipski, GTX Corp, 8836 N. 23rd Avenue, Phoenix, Arizona 85021, USA )
 ( INTERNET: al@gtx.com      UUCP: uunet!gtx!al         PHONE: (602)870-1696 )
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Newsgroups: comp.ai.genetic
From: pauld@cs.washington.edu (Paul Barton-Davis)
Subject: Re: Reproduction Methods
Organization: Precipitating Pendulums Postal Party Poopers
Date: Fri, 22 Jan 93 22:15:02 GMT

In article <C19x7D.DK3@cs.columbia.edu> evs@cs.columbia.edu (Eric Siegel) writes:
>pauld@cs.washington.edu (Paul Barton-Davis) says:
>
>> One really interesting feature that I have observed from my own work
>> with a tierra-like system is the effect of a reproductive burden on
>> the speed of evolution. ...
>
>> So, in normal GA-land, you simply use the "hand of god"
>> approach: you reach in, and *you* do the reproduction, eliminating any
>> selection pressure on this feature. ...
>
>> But wait ! Perhaps, just perhaps, this reproductive feature is just
>> what you want. Its not very difficult to make a tierra-like system
>> evolve its own form of crossover. All kinds of other wierd and
>> wonderful reproductive mechanisms evolve too. It seems possible to me
>> that when evolution is viewed as explorations of a vast state space,
>> particular reproductive methods might be incapable of ever searching
>> some particular areas of that space. Leaving the system free to devise
>> its own reproductive strategies opens up the possibility of exploring
>> areas of the state space that "programmer/user-selected" methods might
>> not. 
>
>Solution: A GA organism does not only represent a "vector" in the 
>solution space of the domain problem, but also has (seperately) 
>encoded a "vector" in _reproduction method space_ (e.g. Weights 
>of crosss-over, mutation; Number of cross-over points, etc.  A big 
>research area?)

Well, not really. This still leaves the programmer to devise an
appropriate set of reproductive methods. The beauty of tierra is that
(1) no fitness function for reproductive fitness is needed, because it
is implicit in the ranking of the genome within the population and (2)
any reproductive method anyone could ever dream up can potentially
evolve within the system, as well as few that no-one ever would.

>Yeah, I was just thinking about evolution a month ago, and said to my
>friend over lunch, "you ever realize that the manner by which evolution
>takes place evolves?"

My precise point. Programmers trying to second-guess what makes it
tick seems like an unnecessary constraint.

>This comparison between tierra-like systems and GAs reminds me: In the
>former, an effective upper-limit on time spent running an organism's 
>program is implicit in the organism's environment.  

Want to explain what you mean ? I don't see such an upper-limit, other
than the one imposed by the realities of the host system (ie. real
world physics).

-- paul

-- 
hybrid rather than pure; compromising rather than clean;    | Militant Agnostic
distorted rather than straightforward; ambiguous rather than| I Don't Know
articulated; both-and rather than either-or; the difficult  |   and You Don't
unity of inclusion rather than the easy unity of exclusion. |   Know Either
From: genetic+@pitt.edu (Dr. Dave)
Newsgroups: comp.ai.genetic
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Date: 21 Jan 93 16:16:59 GMT
Organization: Department of Industrial Engineering

Thus spake choup@ava.bcc.orst.edu (Ping Chou):
>
>        Isn't it true that selection/mating/mutation/chromosome/hybrid/
>genotype/phenotype/gene/... are taken from genetics?

No.  They are *inspired* by the biological processes of natural selection,
mutation, adaptation, speciation, etc.  They are meant to be analogous to
them, not rigidly isomorphic.

>        Don't you think you may be able to discover new algorithms from
>studying genetics?

Perhaps.  Cohoon, for example, has used such ecological notions as allopatric
speciation to motivate some of his work in parallel implementations of GA.
But GA is primarily concerned with solving problems, not mimicking nature.
If mimicking nature works best, so be it.  If it doesn't, none of us are 
going to lose sleep over the fact that our algorithm isn't "realistic".

>        BTW, I would appreciate any reasonable arguments because I'm trying
>to learn ai-genetic from the genetics side.

I'm not sure there *is* a genetics side, beyond the original motivation.
(I'm not sure there's an AI side, either; what's "intelligent" about a GA?)

David Tate

-- 
       David Tate        |"Something there is that doesn't love a wall,
 dtate@unix.cis.pitt.edu | That sends the screaming liner over it 
 Prof. of Story Problems | And spills the bleacher-dwellers from their seats."
 Member ORSA, TIMS, SABR |  	"Fenway Wall", Robert Frost          
Newsgroups: comp.ai.genetic
From: chang@pecan.cns.udel.edu (Liang-Wen Chang)
Subject: Re: GA versus Simulated Annealing
Organization: University of Delaware, Newark
Date: Wed, 27 Jan 1993 08:27:32 GMT

In article <2360@blue.cis.pitt.edu> genetic+@pitt.edu (Dr. Dave) writes:
>
>I say "should" above because, as far as I know, nobody has done a systematic
>study on this.  However, it seems to be not uncommon, at least when looking at
>combinatorial problems, to see cases where SA is faster, and running a lot of
>different SA runs will find you solutions just as good as the best GA runs, 
>but the *average* value of the GA solutions is better than the average value
>of the SA solutions, and the quality of the GA is less sensitive to parameter
>settings (pop size, mutation rate, encoding and operators, etc.) than the SA
>is to its parameters (cooling schedule, settle time at a given temp., etc.).
>
>I'd be happy to be corrected about any of the above, if anyone out there has
>different experience/intuition...
>
>Dave Tate

The GA is insensitive to encoding due to the way of crossover operation.
However, we also can say it IS sensitive to encoding once the encoding
method is chosen.  Because any encoding of numbers will have some
bits is significant than other bits, because such inhomogeneous of
bit encoding, the crossover will produce offsprings in the 'neighborhood'
of the parents.  The radius of the neighborhood is random and variable
depending on where the crossover points are cut.  I haven't figured
out the statistic nature of such variable radius w.r.t. particular
encoding.  The recent 'uniform crossover' method is more of multi-bit
mutation than crossover.  

Another thought is that GA is not quite working if the error surface
contains long nerrow stiff valleys.  Usually, GA will stuck, not
matter how you tune the parameters(those rates and pop size).  Once,
it stucks(converges prematurely), it can never get out by itself because
of the similarity among pops.

SA also has the problem of stucking in some local extremes.  However,
the process is dynamic due to the temperature, if let it run long enough,
it 'might' be able to jump out from the stagnation point.  The difficult
of SA is to determine the schedule and corresponding initial temperature
for a particular problem.  The error surface with long nerrow stiff valleys
, SA can perform  better than GA.  This is equivallent to say that 
mutation with certain distribution is preferable than crossover with
random cut to such error surfaces.  However, if the area of extremes
is not narrow but stiff, GA may work better than SA.

Any comments?  I would appreciate if any.

Boris
From: pja@neuron.cis.ohio-state.edu (Peter J Angeline)
Newsgroups: comp.ai.genetic
Subject: Re: GA versus Simulated Annealing
Date: 27 Jan 1993 14:46:14 -0500
Organization: Ohio State Computer Science
	<1993Jan27.082732.15090@udel.edu> <2624@blue.cis.pitt.edu>
Reply-To: pja@cis.ohio-state.edu
NNTP-Posting-Host: neuron.cis.ohio-state.edu
In-reply-to: genetic+@pitt.edu's message of 27 Jan 93 14:36:37 GMT


In article <2624@blue.cis.pitt.edu> genetic+@pitt.edu (David M. Tate) writes:

   chang@pecan.cns.udel.edu (Liang-Wen Chang) said:

   >I haven't figured
   >out the statistic nature of such variable radius w.r.t. particular
   >encoding.  The recent 'uniform crossover' method is more of multi-bit
   >mutation than crossover.  

   Not at all.  Mutation is disruptive, replacing alleles from the parents with
   alleles that do *not* occur in either parent.  Uniform crossover is perfectly
   conservative, recombining alleles from the parents.  Just because it doesn't
   use *contiguous* alleles from one parent doesn't make it "multi-bit
   mutation". There's not necessarily anything sacred about the particular
   sequence the genes are encoded in.

   As I see it, the role of breeding (be it crossover or some other operator) is
   to rearrange alleles you've got hanging around already, and the role of 
   mutation is to introduce (or re-introduce) alleles you'd *like* to have
   around, but don't.  Viewed this way, uniform crossover is clearly unlike
   mutation, and clearly like crossover.

I think you're pretty close here.  Its more a matter of _conservation_ of
genetic material in crossover.  Crossovers _preserve_ all of the genetic
material that came into the operator so that you can find all of the material
represented somewhere in the offspring after the operator.  However, mutation
does not preserve this criterion of conservation.  Heuristic: if you've got an
operator that doesn't conserve the genetic material, its not crossover but a
form of mutation, regardless of what else it does.  Notice that this doesn't go
the other direction, i.e. just because all genetic material is preserved
doesn't mean its crossover, it could be inversion.  But uniform crossover is
clearly NOT mutation.

-pete angeline

-------------------------------------------------------------------------------
Peter J. Angeline            ! Laboratory for AI Research (LAIR)
Graduate Research Assistant  ! THE Ohio State University, Columbus, Ohio 43210
ARPA: pja@cis.ohio-state.edu ! "Nature is more ingenious than we are."



-- 
-------------------------------------------------------------------------------
Peter J. Angeline            ! Laboratory for AI Research (LAIR)
Graduate Research Assistant  ! THE Ohio State University, Columbus, Ohio 43210
ARPA: pja@cis.ohio-state.edu ! "Nature is more ingenious than we are."
Newsgroups: comp.ai.genetic
From: Howard Oakley <Howard@quercus.demon.co.uk>
Subject: Re: Kozan Book Order INfo
X-Xxmessage-Id: <A785FD94EF010916@quercus.demon.co.uk>
X-Xxdate: Fri, 22 Jan 93 19:37:56 GMT
Organization: EHN & DIJ Oakley
X-Useragent: Nuntius v1.1.1d12
Date: Fri, 22 Jan 1993 19:29:26 GMT

Re: Kozan Book Order INfo
Perhaps I am less biased than James Rice:

I bought the book and video immediately before Christmas.  They are both
worth every dollar - the book is complete, and will remain the reference
work for some time to come.  You get a lot of book - and a lot of
research behind it - for your money.

I would recommend that you watch the video before reading the book.  I
have also shown it to a research audience, and it went down wonderfully. 
It enables you to grasp the fundamentals - with lovely and real
illustrations - very quickly before working on the 800 odd pages.  The
illustrations are superb and really do offer something that no printed
medium could ever match.

I support everything that James has said.

Howard.
Howard Oakley, EHN & DIJ Oakley,    * Howard@quercus.demon.co.uk
EHN & DIJ Oakley, Brooklands Lodge, * AppleLink UK0392
Park View Close, Wroxall, Ventnor,  * CompuServe 70734,120
Isle of Wight UK PO38 3EQ           * Tel +44 983 853605, fax 853253
Newsgroups: comp.ai.genetic
From: peetah@leland.Stanford.EDU (Peter Gage)
Subject: Constraint-handling in GA function optimization
Followup-To: peetah@leland.stanford.edu
Organization: DSG, Stanford University, CA 94305, USA
Date: Wed, 27 Jan 93 23:59:48 GMT

Recently there was a request for information on appropriate
penalty functions to use for constrained function optimization
with genetic algorithms. (Unfortunately I discarded the original
message.) I would like to extend the scope of this inquiry to 
include all methods for handling constraints. It seems that you 
lose information by shoving constraint violations and objective 
into a single scalar merit function, and I am interested in
methods which treat constraints separately.

I have noticed the mention of constraint-handling 'by repair',
but it's not clear to me exactly what this means. Is this 
method used to make a viable genome (e.g. check that every
city is represented in travelling salesman problem) so that 
fitness can be evaluated, or is it applied after fitness has
been evaluated so that future fitness can be improved?

Are there other methods for handling constraints 'out there'?

Please e-mail to me, and I will summarise for the group.

Peter Gage
From: charman@snoopy.inria.fr (Philou)
Newsgroups: comp.ai.genetic
Subject: GA for space planning problems ?
Date: 22 Jan 93 10:29:08 GMT
Organization: INRIA, Sophia-Antipolis (Fr)

-- 



Dear Netters,

I have been looking for the possibility of applying GA
to space planning problems. I wonder if any of you came 
across a paper about the subjects mentioned above. If yes 
I would be grateful if you can send me the name, authors, 
etc. either to my email address or to net.


Thanks in advance, Phil.


------------------------------------------------------------------
 Philippe CHARMAN

 INRIA Sophia Antipolis		  | voice (+33) 93.65.77.48        
 2004, route des Lucioles	  | fax   (+33) 93.65.77.65    	 
 F-06561 Valbonne Cedex (France)  | e-mail charman@sophia.inria.fr	
------------------------------------------------------------------
Newsgroups: comp.ai.genetic
From: long@next1.acme.ucf.edu (Richard Long)
Subject: Re: GA versus Simulated Annealing
Organization: University of Central Florida
Date: Thu, 21 Jan 1993 17:24:59 GMT

In article <1993Jan19.155159.16736@thunder.mcrcim.mcgill.edu>  
gblais@McRCIM.McGill.EDU (Gerard Blais) writes:
> 
> 
> I have been using a genetic algorithm to solve a minimization
> problem.  I performed the same minimization using the "Very Fast
> Simulated Reannealing" algorithm created by Lester Ingber and
> Bruce Rosen (I got it from a friend, but I believe it's available
> from the net).  In all cases I tried, the Annealing search was
> orders of magnitude faster for converging to the global minimum
> than the GA.  My friend observed the same result for a different
> minimization problem.
> 
> My question is:  Are there any cases that you know of where a
> genetic algorithm would actually perform better than simulated
> annealing?  Are there any cases where simulated annealing would
> fail but not genetic algorithms?

One can view simulated annealing and reannealing as a GA with a mutation  
rate set to non-monotonic decreasing and the crossover and inversion rates  
set to zero.  From the few cases that I've read about the successes of GA,  
crossover is particularly good for a problem that has a natural  
hierarchical structure to the optimal solution, since crossover exploits  
local fitness maxima in the construction of a global maximum.  If there is  
no hierarchical structure to the optimal solution, local fitness  
contributes very little to global fitness, and there is little advantage  
to keeping local good solutions as crossover does.  Inversion exploits  
symmetries in the problem. 
Some problems that may be naturally hierarchical and symmetric, such as  
image classification, may yield to GA methods more than simulated  
annealing or simulated reannealing.
--
Richard Long
Institute for Simulation and Training
University of Central Florida
12424 Research Parkway, Suite 300, Orlando, FL 32826
(407)658-5026, FAX: (407)658-5059
long@acme.ucf.edu
Newsgroups: comp.ai
From: jin@ece.scarolina.edu (Jin Kee)
Subject: Re: CMU LISP Tutor
Organization: USC  Department of Computer Science
Date: 24 Jan 93 20:04:01 GMT

christen@astro.ocis.temple.edu (Carl Christensen) writes:

>(This may be a FAQ but I can't find the FAQ list for comp.ai.)

>Does anyone know how to get a copy of CMU's famous LISP tutoring
>program?  I have tried 'archie' to no avail.  Replies via E-mail
>or posting.  Thanks.

>--

>Carl Christensen                      christen@astro.ocis.temple.edu
>Department of Computer Science
>Temple University
>Philadelphia, PA  19122   USA         (215) 735-6787

Please let me know also how to get a copy of CMU's LISP tutoring 
program. Thanks.

Jin Kee
Electrical and Computer Engineering Department
University of South Carolina
Columbia, SC 29208
(e-mail)  jin@ece.scarolina.edu
(voice)   (803) 777 - 0196
(fax)     (803) 777 - 8045

P.S. Does anybody know any ITS that teaches C program?
From: christen@astro.ocis.temple.edu (Carl Christensen)
Newsgroups: comp.ai
Subject: LISP for Windows 3.1 anywhere?
Date: 22 Jan 93 04:24:12 GMT
Organization: Temple University
X-Newsreader: TIN [version 1.1 PL8]

Hi,

  I'm looking to begin my MS project, and would like to know if
  anyone has run into a LISP interpreter that runs under
  and supports MS Windows 3.1 (and multi-media).

  The project will be a musical one so MIDI support (via
  Windows multi-media or whatever) is pretty important.
  Also, as I am a poor grad student an economical price would
  be nice.   ;)
  
  Thanks.

--

Carl Christensen                      christen@astro.ocis.temple.edu
Department of Computer Science
Temple University
Philadelphia, PA  19122   USA         (215) 735-6787
Newsgroups: comp.ai
From: brezil@laforia.ibp.fr (Patrick Brezillon)
Subject: Re: Journees d'etude sur les explications
Reply-To: jansen@syd.dit.csiro.au
Organization: LAFORIA - Universite Paris 6
Date: Thu, 28 Jan 1993 04:50:02 GMT

I know that everybody is not interested by local idioms and that English is the
current esperanto. 

However, my objective was threefolds:

(1) There are French persons in all parts of the world (even in Australia...)
and some non French native persons understand the French language (mainly those
living in France for a period and making an effort for learning French);

(2) Explanations is a subject that interests a number of French persons;

(3) The two chairpersons have pointed out a mutation in the approaches for
generating explanations that needs to be studied.


Thus, be patient, you will have later an English report on results of our
French meeting.

Amicalement,

Patrick

Xref: sparky comp.ai:4985 comp.ai.neural-nets:4966
From: mcramm@dct.ac.uk (McRamm)
Newsgroups: comp.ai,comp.ai.neural-nets
Subject: IEEE SMC '93 conference. E-mail address wanted.
Date: 24 Jan 93 15:09:40 GMT
Organization: Dundee Institute of Technology

Does anyone know of an E-mail address for the organiser of the special session
"Applications of Neural Networks to Control Problems" at the IEEE SMC'93
conference to be held in October.

I have a snail-mail adress but would like to be able to consult him/her via
e-mail.

Snail address is:
Prof. D. Lamy, ICAM Lille, Dept. Automatic Control, 6 Rue Auber, 59046 Lille,
France.

Any help will be greatly appreciated.
-- 
 Angus M. Marshall, MCRAMM@DCT.AC.UK,amarshal@mcs.dund.ac.uk, (+44 382) 308600
Then I was gone, and in my place - a cat with green eyes.
    )     <-- Cheshire Smiley?
Xref: sparky comp.ai:5052 comp.ai.edu:612 sci.edu:1494 misc.education:6149
From: skumar+@pitt.edu (Suresh Kumar)
Newsgroups: comp.ai,comp.ai.edu,sci.edu,misc.education,pitt.general
Subject: Introductory Computer Science (AI) books for 5th Graders
Date: 26 Jan 93 17:00:21 GMT
Followup-To: comp.ai
Organization: University of Pittsburgh
Originator: skumar@unixd2.cis.pitt.edu

Hi All,

Thanks very much for your time.  I'm looking for introductory computer
science books (including artificial intelligence) for high school students.
Could someone give me a couple of pointers please?

Suresh
-- 
------------------------------------------------------------------------
V Suresh Kumar                         Exchange Visitor - United Nations
520, Learning Research and Development Centre, 3939 O'Hara Street
University of Pittsburgh, Pittsburgh,  Pennsylvania, 15260, USA.
Newsgroups: comp.ai,rec.arts.books,misc.writing,rec.arts.int-fiction