HDTenEightyP/NetNews huggingface dataset:
Newsgroups: comp.ai.genetic
Subject: Distributed Computing of GAs
From: vidya@tahi.isor.vuw.ac.nz (Srividya Venkataraman)
Date: Sat, 23 Jan 1993 23:14:45 GMT
Organization: Institute of Statistics and Operations Research, VUW, Wellington, NZ


Hi,
I'm about to start my Masters work on Distribted Computing of Genetic Algorithms.
I'm new to GAs and would appreciate any information of work- past and present, 
in this area. Any ideas/suggestions/references will be greatly appreciated.

Thanks,
Vidya.

vidya@isor.vuw.ac.nz
From: achut@nairobi.Eng.Sun.COM (Achut Reddy)
Newsgroups: comp.ai.genetic
Subject: Re: Gassy GA package
Date: 26 Jan 1993 22:56:38 GMT
Organization: Sun Microsystems Inc., Mountain View, CA
NNTP-Posting-Host: nairobi

In <C1Epr0.7IM@cck.coventry.ac.uk> csg068@cch.coventry.ac.uk (Decker) writes:
>In article <TERRY.93Jan20201750@nambe.santafe.edu> terry@nambe.santafe.edu (Terry Jones) writes:
>>
>>The README file posted the other day for the Gassy GA package gave an
>>outdated internet site - one that can no longer be reached.
>>
>Could some kind soul PLEASE mail it to me, as this WAS going to be a    
>fundemental part of my honours project. HEEEEEEEEEEEEEEEEEELP!

Or better yet, post an ftp location that *can* be reached?
Gassy looks interesting; I'd really like to play with it.

-achut

Newsgroups: comp.ai.genetic
From: dave@vfl.paramax.com (David Lee Matuszek)
Subject: Re: Kozan Book Order INfo
Organization: Paramax Systems
Distribution: usa
Date: Thu, 21 Jan 1993 21:34:29 GMT


In regard to
     Genetic Programming: On the Programming of Computers by Means   
     of Natural Selection, by John R. Koza.


In article <RICE.93Jan18180101@hpp-ss10-1.stanford.edu>,
  rice@sumex-aim.Stanford.EDU (James Rice) writes:

|> I'm biased, but I'd suggest reading the book and watching the movie.
|> 
|> Rice.


In what way are you biased?

I'm thinking seriously about spending $55 for the book (though I wish
someone would tell us a little more about it), but $35 for a "movie"
seems awfully steep.  What's the point of the movie?  What makes it
worth this much?

  --dave
From: genetic+@pitt.edu (David M. Tate)
Newsgroups: comp.ai.genetic
Subject: Re: GA versus Simulated Annealing
Date: 27 Jan 93 14:36:37 GMT
Organization: Department of Industrial Engineering

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

This is certainly true.

>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.  

Just a note:  not all encodings are bits, and not all breeding operators
are "crossover".  That's just one special case, and not always desirable.

>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.

Again, most of my work is with combinatorial optimization problems for which
bit-string encodings would be nightmarish.  So my perspective is perhaps a
trifle unusual.

-- 
       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
From: genetic+@pitt.edu (Dr. Dave)
Newsgroups: comp.ai.genetic
Subject: Re: Genetic Algorithms for Scheduling (Summary)
Date: 21 Jan 93 21:47:06 GMT
Organization: Department of Industrial Engineering

Thus spake wright@lims01.lerc.nasa.gov (Ted Wright):

>Thanks to Dave Schaffer and everyone else that responded:

	[extensive bibliography deleted]

Another important (IMHO) GA paper that gives a scheduling application
is the following poorly-titled article:

	"New search spaces for sequencing problems with applications
	 to job shop scheduling"
	R. Storer, D. Wu, and R. Vaccari (Lehigh University)
	_Management_Science_ volume 38 #10, October 1992

This paper was one of the first to introduce (independently) the idea of
encoding a solution as a data/heuristic pair, rather than explicitly as a
solution.  In particular, schedules in a job shop are encoded as fake sets
of processing times and due dates, to which a naive constructive heuristic
is then applied.  This saves the hassle of representing the fine structure
of the resulting schedule in the encoding.


-- 
       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
From: hart@de01.denver.waii.com (Doug Hart)
Newsgroups: comp.ai.genetic
Subject: Re: Penalty functions
Keywords: Penalty Optimization Minimization
Date: 26 Jan 93 18:45:50 GMT
Reply-To: hart@de01.denver.waii.com
Organization: Western Geophysical

In article <C1F8H9.8nw@world.std.com>, jbk@world.std.com (Jeffrey B Kane) writes:
|> 
|> I was wondering if anyone could point me to some example code and/or references
|> for different penalty functions to be used in optimization problems.  Although
|> a number of sources make somewhat vague references to these functions 
|> (Goldberg, Davis, and others in their books) I wanted some concrete examples 
|> that I could look at.
|> 
|>        Thanks,
|>              Jeffrey
|> 
|> 
|> ======================================
|> Jeffrey Kane, MD
|> Kane Biomedical Systems
|> Boston, MA
|> 
|> Internet    jbk@world.std.com
|> Compuserve  74206,640
|> AppleLink   D0738
|> 
|> [Don't take life too seriously... it's not like anyone gets out of it alive.]

Multiplier methods, for constrained optimization problems, generally
exceed the performance of penalty function methods.  The reference:
R.A. Tapia, "Diagonalized Multiplier Methods and Quasi-Newton Methods for
Constrainded Optimization", Journal of Optimization Theory and Applications,
Vol. 22., No.2, June 1977, p. 135-194., is where I got started with these
methods.  Two papers on Penalty Function methods are in the reference section
of this paper.  They should point you to some examples of penalty functions.

Doug Hart
hart@de01.denver.waii.com


Newsgroups: comp.ai.genetic
From: root@ibmpcug.co.uk (Superuser)
Subject: Advice wanted: PhD in GAs
Organization: The IBM PC User Group, UK.
Date: Thu, 28 Jan 1993 23:01:28 GMT
Keywords: PhD GA funding universities


I'm wondering if anyone can give me some advice on the following
tow areas:
1) are there any organisations out there that would be interested in
funding me in a PhD - I don't know if there would be any interest abroad
but I thaught it would be best to cast my net as wide as possible
2) universities which are intereseted in GA research.  This must probably
exclude USA (I'd not mind going to the US at all, it's just too late
to apply there now, I believe...).  Universities in the EC (excluding the
UK) may be a good place, because then I may be able to get funding from 
EC gerant giving bodies.

I'm particularly interested in applying GAs to various machine learning
problems - tho not so much NNs since that's been (as far as I know) 
one of the 'flavours of the month' with the application of GAs

This is just a tentative initial enquiry - and the first point is
slightly more important (except wrt the EC bit) since I know of
some universities here in the UK which would be appropriate, it's the
funding that's the problem.

-John Critchley

j_critchley@ibmpcug.co.uk

PS I hope this understandable - I've had a long day!
From: choup@ava.bcc.orst.edu (Ping Chou)
Newsgroups: comp.ai.genetic
Subject: So, genetic algorithms have nothing to do with genetics?
Date: 21 Jan 1993 08:19:46 GMT
Organization: Biological Computing Consortium, OSU
Distribution: world
NNTP-Posting-Host: ava.bcc.orst.edu


        Or the development of genetic algorithms has completed?

        Isn't it true that selection/mating/mutation/chromosome/hybrid/
genotype/phenotype/gene/... are taken from genetics?

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

        Do you really believe the complete genome of HIV is just a 4-state
bits encoded string?

        Then please, give youself a chance, take a look at any modern
genetics text book, and be a cross-discipline expert.

--Ping

        BTW, I would appreciate any reasonable arguments because I'm trying
to learn ai-genetic from the genetics side.
From: pja@neuron.cis.ohio-state.edu (Peter J Angeline)
Newsgroups: comp.ai.genetic
Subject: Re: Genetic algorithm question
Date: 23 Jan 1993 13:20:43 -0500
Organization: Ohio State Computer Science
Reply-To: pja@cis.ohio-state.edu
NNTP-Posting-Host: neuron.cis.ohio-state.edu
In-reply-to: liu@rodin.wustl.edu's message of Fri, 22 Jan 1993 21:38:51 GMT

In article <1993Jan22.213851.8114@wuecl.wustl.edu> liu@rodin.wustl.edu (Liu Ann Pheng) writes:

   Greetings!  I have a question for the net readers:

   What is the relationship (as well as main differences, if any) between
   genetic algorithm and evolutionary programming?  

   Thanks,
   Liu, Ann Pheng

Genetic algorithms (GA) and Evolutionary Programming (EP) (ala L. Fogel and D.
Fogel) are similar in that they both use analogies to evolution to do problem
solving search. They differ on about every other aspect.  The most import
difference is the LEVEL of evolution they choose to make the analogy.  In GAs,
the level is that of genetics, i.e. evolution is modeled as a process that
alters a genetic code which is translated into a phenotype that is evaluated.
Evolution in GA occurs via _genetic operators_, i.e. crossover, inversion,
point mutation.  In EP, the manipulation is done directly on the phenotype
rather than on a description that is converted into the phenotype.  EP uses a
mutation operator that takes the population member and alters it so that is
still reasonably similar.  This is a higher level model of evolution and
potentially less flexible but more exploitable.

This can be seen best by an example.  Consider how each would evolve neural
networks. In a standard GA, a fixed-length string would be used with an
interpretation function to convert it to a neural network for evaluation.
Manipulation is done on the string making how the network is represented in the
string, i.e. how the interpretation function converts the string, a real
concern for GAs. In EP, the network itself is the member of the population and
is manipulated directly to create offspring. Mutation would alter the weight
values directly by adding a small random amount to it.  Other manipulations
would alter connections in the network.  I have intentionaly chosen an example
that benefits EP here to show its advantage over GA since it is the lesser
known of the two, but there are many tasks where the flexibility of a GA's
operators makes it the proper choice. 

There are other differences which are mainly due to the traditional support
mechanisms used with these techniques in practice (e.g. a determinsitic
selection method for EP and a probabilistic one for GA), but the essential
difference is their level of analogy to evolution.

-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."
Newsgroups: comp.ai.genetic
From: thrift@ra.csc.ti.com (Phil Thrift)
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Organization: TI Central Research Laboratories, Dallas
Date: Thu, 21 Jan 1993 22:37:52 GMT

In <1993Jan21.185014.25414@msi.com>
David Christopher Rogers <drogers@riacs.edu> writes:

  When talking about either genetic algorithms or neural networks to people,
  I usually say that these fields were inspired by the physical models, but 
  are certainly not a serious attempt to model either genetics or nervous 
  systems.

  I believe that we algorithmicists need to be quite careful in how we 
  use the vocabulary of other fields, especially if the algorithm is 
  using that field as a neat analogy rather than as a model ...

  ... Science is fighting a terrible tower-of-babel effect
  as it is without needless redefinition, often for what seems to be just
  the ability to have snappy words for the titles of papers.


There is another twist to this that is more bothersome to me. It seems
that if a certain field becomes 'hot' (e.g. neural nets, GAs, fuzzy
etc.)  people may want to describe their algorithms in the new
terminology (why?). For example, some statistical classifier or
clustering algorithms that have been around before the current NN wave
have been reformulated in neural network terminology.  Sometimes I wonder
if some use the terminology in describing their algorithms because of
the true biological inspiration or because of political/economic
reasons.

If biological terminology helps in understanding or explaining an
algorithm, that is good. I think the biological analogy in the case of
GAs is generally useful in explaining this class of
optimization algorithms.  But sometimes it just makes things
confusing.

Philip Thrift

Newsgroups: comp.ai.genetic
From: poliquin@netcom.com (Tom Poliquin)
Subject: Re: Simple genetic example code
Organization: Netcom - Online Communication Services  (408 241-9760 guest) 
Date: Tue, 26 Jan 1993 10:03:40 GMT

David.Beasley@cm.cf.ac.uk (David Beasley) writes:
: In article <105900@netnews.upenn.edu> jrv@gradient.cis.upenn.edu (JR VanMechelen) writes:
: >In article <1993Jan21.013648.22113@osuunx.ucc.okstate.edu> gcouger@olesun.okstate.edu (Gordon Couger) writes:
: >>Could some one point me in the direction of some examples
: >>of genetic source code (C I hope). 
: >
: 
: Try looking in ftp.aic.nrl.navy.mil,
: using "anonymous" as your user name and your email address as your
: password.  Then cd to /pub/galist/source-code/ga-source
: There are lots of things in here.

.... is anyone planning to convert the classifier system code in
Goldberg's book into C ? It seems that only the SGA is in the archive.

Hopeful in advance.....

Tom Poliquin
San Jose
"All great truths begin as blasphemies" George Bernard Shaw
From: cscon143@uoft02.utoledo.edu
Newsgroups: comp.ai.genetic
Subject: Re: Simple genetic example code
Date: 28 Jan 93 14:59:03 EST
Organization: University of Toledo, Computer Services

In article <1993Jan26.100340.1875@netcom.com>, poliquin@netcom.com (Tom Poliquin) writes:
> David.Beasley@cm.cf.ac.uk (David Beasley) writes:
> : In article <105900@netnews.upenn.edu> jrv@gradient.cis.upenn.edu (JR VanMechelen) writes:
> : >In article <1993Jan21.013648.22113@osuunx.ucc.okstate.edu> gcouger@olesun.okstate.edu (Gordon Couger) writes:
> : >>Could some one point me in the direction of some examples
> : >>of genetic source code (C I hope). 
> : >
> : 
> : Try looking in ftp.aic.nrl.navy.mil,
> : using "anonymous" as your user name and your email address as your
> : password.  Then cd to /pub/galist/source-code/ga-source
> : There are lots of things in here.
> 
> .... is anyone planning to convert the classifier system code in
> Goldberg's book into C ? It seems that only the SGA is in the archive.
> 
> Hopeful in advance.....
> 
> Tom Poliquin
> San Jose
> "All great truths begin as blasphemies" George Bernard Shaw
> 
-- 

	I did it for a class a year or so ago.  I had it running
	on the VAX.  I can send you the code if you want.

					- Erik Mayer 
___
  Internet: cscon143@uoft02.utoledo.edu    
  Bitnet: cscon143@uoft02.BITNET
From: ob00@ns1.cc.lehigh.edu (OLCAY BOZ)
Newsgroups: comp.ai.genetic
Subject: Ga in Language Acquisition?
Date: 28 Jan 93 04:27:30 GMT
Organization: Lehigh University

Is there any work done on language acquistion, Natural languahe understanding
or natural language processing? Any references will be greatly appreciated.
-- 


         -----------------------------------------------------------------
         |  OLCAY BOZ                     |  OB00@NS1.CC.LEHIGH.EDU      |
         |  LEHIGH UNIVERSITY             |                              |
         |  COMPUTER SCIENCE DEPARTMENT   |                              |
         |  Ph. D. Student                |  phone : (215) 758-1886      |
Newsgroups: comp.ai.genetic
From: liu@rodin.wustl.edu (Liu Ann Pheng)
Subject: Genetic algorithm question
Organization: Center for Optimization and Semantic Control, Washington University
Date: Fri, 22 Jan 1993 21:38:51 GMT

Greetings!  I have a question for the net readers:

What is the relationship (as well as main differences, if any) between
genetic algorithm and evolutionary programming?  

Thanks,
Liu, Ann Pheng
Newsgroups: comp.ai.genetic
From: terry@nambe.santafe.edu (Terry Jones)
Subject: Gassy GA package
Date: Thu, 21 Jan 93 03:17:50 GMT
Organization: Santa Fe Institute
Distribution: comp


The README file posted the other day for the Gassy GA package gave an
outdated internet site - one that can no longer be reached.

In fact, the package is being rewritten and is not currently
available via ftp. Perhaps it will be ready in a year or so...

Sorry for any inconvenience etc.
Terry Jones.
From: madavis@nmsu.edu (Mark Davis)
Newsgroups: comp.ai.genetic
Subject: comp.ai.genetic
Date: 22 Jan 93 18:06:58 GMT
Article-I.D.: sparta.MADAVIS.93Jan22110658
Distribution: comp.ai.genetic
Organization: Computing Research Lab


recent work by N. Radcliffe, "Genetic Set Recombination and Its
Application to Neural Network Topology Optimization" in Neural
Computing and Apllication, 1993 1:67-90, suggests that desirable
properties of GAs include "proper assortment" and "respect for
formae membership".  both these definitions build upon Radcliffe's
extension of Holland's schema analysis, called formae analysis,
and require, respectively, that the offspring of two genomes in
a GA have properties which in some way combine the desirable
properties of the parents.

this is nothing new, perhaps.  but what is interesting is his
arrival at the Random, Respectful Recombination operation which
involves selecting randomly and uniformly from the so-called
similarity set of the two parents to produce the offspring.  this
operation bears a striking resemblance to the Gaussian modification
scheme proposed by Fogel and Porto starting in the mid-60s and
elucidated in Artificial Intelligence Through Simulated Evolution,
an out-of-print and rare book, as well as in recent work (see
Fogel, D. and Atmar, J.W. "Comparing Genetic Operators with
Gaussian Mutations in Simulated Evolutionary Processes Using 
Linear Systems,"  Biol. Cybernetics, 63, 111-114, 1990.)  In 
their work, the value of standard GA mutation operators has been
repeatedly called into question, both in terms of its robust
ability to find globally optimal solutions, and in its speed
of convergence.

the fundamental criticism levied by the so-called evolutionary
simulation-people against GAs as a methodology is precisely
the question of the accuracy of the simulation.  the assumption
that representations which code individual properties in a
linear genome and operations on those representations can
approach the kind of robust optimization seen in nature is
at question.  specifically, they have suggested that complex
behavior in natural systems is almost always the result of 
complex epistatic and pleiotrophic interactions of suites of
genes, rarely of individual genes in isolation.  GAs lack
the ability to effectively simulate this property, and 
ultimately require random mutation to avoid suboptimal
convergence and complex considerations of representation to
provide comparable convergence rates.

any comments???

mark

--
                          WORLD                                    WORLD VIEW
        
      ** *   **     **  ***  ** **** **** 
       **** * *    ** ***************** * 
       *****      *  * ** ************ *                               
        *               ************** *
       ****       ****** ** ** ******      
       *****     *******     *   ***     
        **          ***           *   **
        *           ***            ***  
        *            *            *****  *
                                   * *   *

                                          - mark -
From: genetic+@pitt.edu (Dr. Dave)
Newsgroups: comp.ai.genetic
Subject: Re: Distributed Computing of GAs
Date: 25 Jan 93 15:48:58 GMT
Organization: Department of Industrial Engineering

Thus spake vidya@tahi.isor.vuw.ac.nz (Srividya Venkataraman):
>
>I'm new to GAs and would appreciate any information of work- past and present, 
>in this area. Any ideas/suggestions/references will be greatly appreciated.

J.P. Cohoon, S.U. Hegde, W.N. Martin, and D.S. Richards:
	"Distributed genetic algorithms for the floorplan design problem"
	IEEE Transactions on CAD
	Volume 10, 1991 p. 483


-- 
       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
From: rickr+@CMU.EDU (Richard Dale Romero)
Newsgroups: comp.ai.genetic
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Date: 23 Jan 93 20:59:34 GMT
Article-I.D.: andrew.sfMXtKq00VQs8XFl9y
Organization: Applications Software, Carnegie Mellon, Pittsburgh, PA

Excerpts from netnews.comp.ai.genetic: 21-Jan-93 Re: So, genetic
algorithms .. Phil Thrift@ra.csc.ti.co (1567)

> For example, some statistical classifier or
> clustering algorithms that have been around before the current NN wave
> have been reformulated in neural network terminology.  Sometimes I wonder
> if some use the terminology in describing their algorithms because of
> the true biological inspiration or because of political/economic
> reasons.


I wouldn't be so hard on people--often times, its just the context of
how they learned it.

-rick
Newsgroups: comp.ai.genetic