HDTenEightyP/NetNews huggingface dataset:
From: jindril@eng.clemson.edu (jindrich liska)
Subject: Re: GAs for Compressor Sta. Optimization?
Reply-To: jindril@eng.clemson.edu
Organization: College of Engineering, Clemson Univ.
Date: Tue, 19 Jan 1993 00:44:51 GMT

In article 16225@uhura1.uucp, paul@uhura1.uucp (Paul O. Lamb) writes:
>
>I have an optimization problem I would like to get some ideas on.
>
>I am trying to develop a technique to optimize compressor station
>operation on natural gas transmission systems. Along most NG
>transmission systems there are a series of compressor stations to
>boost line pressure. The order in which they are brought on-line is
>typically done from a 'this worked last time, so lets do it again'
>approach. The whole idea is to provide enough pressure at delivery
>points to fulfill contractual agreements, but not to provide too much
>pressure, such that, you are wasting compressor fuel and increasing
>maintenance cost on the compressors to do so. It would be extremely
>useful to be able to optimize operating compressor stations. 
>
>How appropriate are GAs to doing this type optimization. 
>
>  
>

I don't know if you are aware of it but, there has been done very similar application of GA you are describing (if I understood it right). Now, it's considered to be a part of GA foundations. To make it short, here is a reference

Goldberg, Optimization of gas pipeline using GA, 1989, PhD thesis.

(substantial part of this work is presented in Goldberg's: GA in optimization, search and learning).

(I might be imprecises in the exact reference titles.)

Good luck,

jindrich


Newsgroups: comp.ai.genetic
From: Malcolm D Binns
Subject: Simple Question on basic knowledge	
Followup-To: comp.ai.genetic
Keywords: genetic algorithms
Reply-To: mdb1@eng.cam.ac.uk
Organization: Cambridge University Engineering Department
Date: Thu, 21 Jan 1993 14:17:10 GMT

Hello,

This is my first posting to cag, so you will have to excuse my ignorance
of basic terms. I am interested in applying various learning methods to
fuzzy logic systems and a very popular one seems to be genetic algorithms.

My intuition suggests that a genetic algorithm must be similar to a
directed random search. I realise it is not quite this simple, but I was 
wonderring what are the main differences. With all the talk on the net of
genes and mutations I assume that only part of the characterising 
information is altered with each iteration, but how in practice is this 
done and how do you combine genes.

Apologies for my ignorance, but I want to get an idea of what a genetic
algorithm is at a system level before trying to understand the detail.

Thanks for reading this,

Any answers to the net or e-mail to mdb1@eng.cam.ac.uk

Cheers,

Malcolm Binns.

Newsgroups: comp.ai.genetic
From: pauld@cs.washington.edu (Paul Barton-Davis)
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Organization: Precipitating Pendulums Postal Party Poopers
Date: Thu, 21 Jan 93 09:46:15 GMT

In article <1993Jan21.092927.4669@CSD-NewsHost.Stanford.EDU> jek@Xenon.Stanford.EDU (James E. Kittock) writes:
>In article <1jlmb2INNjp4@gaia.ucs.orst.edu> choup@ava.bcc.orst.edu (Ping Chou) writes:
>
>>        Do you really believe the complete genome of HIV is just a 4-state
>>bits encoded string?
>
>Well, in a sense, sure.  Why?  What do you know that we
>don't?  How about educate us rather than chastise us!

Here's a start: its highly probable that variations in the physical
structure of the DNA are intimately involved in regulating expression,
both in the sense of levels (via histone binding) and process control
(start, stop, splice points). This physical structure is
simultaneously redundant (similar sequences can give rise to the same
physical structure, within the limits of protein binding resolution
anyway) but is also cross-correlated (you need <pattern-1> here, and
<pattern2> there in order to get the effect).

Although these effects are not completely lost by using a 4 state
representation, they are considerably less obvious.

>>        Then please, give youself a chance, take a look at any modern
>>genetics text book, and be a cross-discipline expert.
>
>It is pretty arrogant to assume that people who work
>with genetic algorithms have no knowledge of genetics!
>Furthermore, different people have different interests,
>and you can't be sure what each person ought to study.
>For example, there is relatively little need for
>someone interested in modelling the evolution of
>populations to have a thorough understanding of, say,
>molecular biology even though it relates to genetics.

Perhaps you could explain why you believe this to be so. I can think
of several arguments against this.

>For those non-biologists interested in getting some
>background on cell physiology and/or genetics, I highly
>recommend "The Living Cell", by Christian de Duve from
>the Scientific American library series.  It is getting
>a bit "old" (1985?) but I assume the basic top-level
>picture it presents is still mostly "correct".  More
>importantly it is scientific without being pedantic and
>boring and yet it is not too "popular".

If you can handle Alberts et al. "Molecular Biology of the Cell",
you'll get more up to date info, nice pictures and lots of references.
Also, see Ben Lewin's "Genes" for a mind-boggling view of just how
complex biological genetics really is. You don't need to understand it
all - just get a feel for the actual complexities.

-- paul (ex-theoretical mol.biologist turned operating systems
	 hacker and a-life private explorer)
-- 
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
Newsgroups: comp.ai.genetic
From:  David Christopher Rogers <drogers@riacs.edu>
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Organization: RIACS, NASA Ames Research Center, Moffett Field, CA
Date: Thu, 21 Jan 1993 18:50:14 GMT

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.  Scientists
spend much painful work deriving a useful vocabulary for their area of
research, and have every right to resent careless redefinitions of their
terms from people that don't understand the standard definitions of those 
terms at all well.  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.

That said, I believe that both fields would benefit tremendously from
closer interaction with their biological namesakes. We should encourage
this interaction and listen carefully to the complexity of their world.
In the sort run, we are more likely to gain from that interaction than
they are.

David Rogers

Newsgroups: comp.ai.genetic
From: miket@hermes.tcs.com (Michael Turner nmscore Assoc.)
Subject: Re: Sharing
Keywords: Sharing optimization
Organization: Teknekron Communications Inc.
Date: Wed, 27 Jan 1993 02:44:20 GMT

In article <C1F1uu.HI8@world.std.com> jbk@world.std.com (Jeffrey B Kane) writes:
>
>I was wondering if anyone had any good references on sharing when working
>with the multimodal optimization problems  I've been using some of the
>concepts presented in Goldberg's book, but there is a big problem with a 
>combinatorial explosion in comparative operators (negating
>the speed improvements you get with GAs). My problem involoves non-binary
>string populations so I really need to find an efficient way of finding
>highly correlated strings.

Are you doing sequence comparison to determine what is "highly correlated"?
There is a lot of work in this area, much of it inspired by chromosome-
comparison.  The algorithms are still expensive (O(n*m) to compare
two sequences of length m and n; though I think there are some good
speed-up heuristics.)
---
Michael Turner
miket@tcs.com
Newsgroups: comp.ai.genetic
From: kkv@inel.gov (Jamie J Ferguson)
Subject: Re: How to use ga packages when fitness is computed by running a nn
Organization: Idaho National Engineering Laboratory, Idaho Falls, Idaho
Date: Thu, 21 Jan 93 22:16:52 GMT


I did something similar for my graduate thesis, but the neural
network and the genetic algorithm were combined into one program
(I wrote the GA by adding to a previously written NN program --
this was in C++).  I'm curious, though, as to what exactly you
are working on.

Jamie

========== long legal disclaimer follows, press n to skip ===========

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and shall not be used for advertising or product endorsement purposes.
From: wbdst+@pitt.edu (William B Dwinnell)
Newsgroups: comp.ai.genetic
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Date: 21 Jan 93 13:36:50 GMT
Organization: University of Pittsburgh


Yes, GAs have learned alot from genetics, but GAs are abstract, and do not
have to follow the things they imitate exactly. Yes, the field of GAs may
still have more to learn from genetics, but taking 7 courses towards
a medical degree hardly seems worthwhile.
For a look at another field, which has been around a bit longer, try
neural networks. Neural nets were meant to imitate nervous systems{ in
animals, but often when neural net technology diverges from actual
neural systems in concet,xxxx concept, they are most successful.
Newsgroups: comp.ai.genetic
From: erwin@trwacs.fp.trw.com (Harry Erwin)
Subject: Re: So, genetic algorithms have nothing to do with genetics?
Organization: TRW Systems Division, Fairfax VA
Date: Fri, 22 Jan 1993 20:04:58 GMT

I've worked both sides of this furrow (both genetics and GA) and I've
learned that GAs are related to genetics about the same way BP is related
to wet networks. In both cases, the AI community has created a simplified
and sometimes wierd analog to a naturally occurring phenomenum. JMS is
probably the right place to start, because the baroque complexity of
nature is likely to provide alternative ways of doing GAs that your
ordinary computer scientist is likely to have no clue about.

Cheers,
-- 
Harry Erwin
Internet: erwin@trwacs.fp.trw.com

Newsgroups: comp.ai.genetic
From: freier@mail.physics.utah.edu (rodney james freier)
Subject: Re: GAs for Compressor Sta. Optimization?
Organization: University of Utah - Physics Department
Date: Thu, 21 Jan 93 05:39:31 GMT

In article <1993Jan18.164929.16225@uhura1.uucp>, paul@uhura1.uucp (Paul O. Lamb) writes:
|> 
|> I have an optimization problem I would like to get some ideas on.
|> 
|> I am trying to develop a technique to optimize compressor station
|> operation on natural gas transmission systems. Along most NG
|> transmission systems there are a series of compressor stations to
|> boost line pressure. The order in which they are brought on-line is
|> typically done from a 'this worked last time, so lets do it again'
|> approach. The whole idea is to provide enough pressure at delivery
|> points to fulfill contractual agreements, but not to provide too much
|> pressure, such that, you are wasting compressor fuel and increasing
|> maintenance cost on the compressors to do so. It would be extremely
|> useful to be able to optimize operating compressor stations. 
|> 
|> How appropriate are GAs to doing this type optimization. 
|> 
  Incredibly.  If I remember right, there is a discussion in the literature
about a system that did just this, and it was compact enough to run in 64k.
It worked as well as or better than any human expert.

Rod
From: lmerkle@afit.af.mil (Laurence D. Merkle)
Subject: Re: GA versus Simulated Annealing
Organization: Air Force Institute of Technology
Date: Sun, 24 Jan 1993 23:06:30 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.
>

I'm interested in your results.  I don't (yet) have any to offer
along the same lines, but I have a few questions:

	What are your problems?  
	
	Are they well behaved, or do they have lots of local minima?  
	
	Are they large, or toy sized?

Thanks!

Larry
-- 
Laurence D. Merkle, CAPT, USAF    | The opinions expressed are
Student, Department of Electrical | my own, and do not necessarily
	 and Computer Engineering | reflect those of AFIT, the USAF, 
Air Force Institute of Technology | or the US Government
Newsgroups: comp.ai.genetic
From: danwell@IASTATE.EDU (Daniel A Ashlock)
Subject: Evolution vs Coevolution
Reply-To: danwell@IASTATE.EDU (Daniel A Ashlock)
Organization: Iowa State University
Date: Sun, 24 Jan 1993 16:37:19 GMT

   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.

   What I'd like is to see references to transformations of evolutionary 
problems into coevolutionary problems.  If possible summarize the effects.
For example:

   Hillis, Daniel "Co-evolving Parasites Inprove Simulated Evolution as an 
   Optimization Procedure" Proceedings of the Second Conference on Artificial
   Life.

Summary:
 
   Hillis observed better results when he modified a GA that located sorting
networks with relatively few comparitors to have an evolving population of test
sequences whoes fitness was proportional to the number of sorting networks they
fooled.  The fixed population of test sequences let to a 65 comparitor network
for a 16-element list.  The coevolutionary version found a 61 element network.
It should be noted that the "best" network contains 60 comparitors and was
found by a human being.

   Thanks in advance,

Dan
Danwell@IASTAET.EDU




Newsgroups: comp.ai.genetic
From: rvs1265@ritvax.isc.rit.edu
Subject: Re: Some minor suggestions
Reply-To: rvs1265@ritvax.isc.rit.edu
Organization: Rochester Institute of Technology
Date: Fri, 22 Jan 1993 01:20:41 GMT

My only reference to Mr.Rice's posting was in terms of concoction of new terms
but it was not intended to hurt anyone or as a disclaimer of sorts.
If the concerned people mis-interpreted my words, sorry about that.
In fact this News Group was supposed to be for the holy purpose of
sharing of knowledge and not to throw allegations.

Good Luck,
Sudhakar
Newsgroups: comp.ai.genetic
From: jbk@world.std.com (Jeffrey B Kane)
Subject: Sharing
Summary: Sharing in multimodal optimization problems
Keywords: Sharing optimization
Organization: The World Public Access UNIX, Brookline, MA
Date: Mon, 25 Jan 1993 15:32:53 GMT


I was wondering if anyone had any good references on sharing when working
with the multimodal optimization problems  I've been using some of the
concepts presented in Goldberg's book, but there is a big problem with a 
combinatorial explosion in comparative operators (negating
the speed improvements you get with GAs). My problem involoves non-binary
string populations so I really need to find an efficient way of finding
highly correlated strings.

      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.]
From: genetic+@pitt.edu (David M. Tate)
Newsgroups: comp.ai.genetic
Subject: Re: Penalty functions
Keywords: Penalty Optimization Minimization
Date: 28 Jan 93 16:23:13 GMT
Organization: Department of Industrial Engineering

hart@de01.denver.waii.com said:
>
>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.


Multiplier methods are fine for *continuous* optimization, but not readily
applicable for many combinatorial problems, unless a convenient continuous
relaxation exists.  Multiplier methods also require that you have an explicit
representation of the constraint set in a particular form.  For example, in
a Travelling Salesman problem the logically simply constraint "Each tour must
pass through each city exactly once" translates into an exponential number of
linear inequalities in the linear/integer programming formulation.

-- 
       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: David.Beasley@cm.cf.ac.uk (David Beasley)
Newsgroups: comp.ai.genetic
Subject: Re: Simple genetic example code
Summary: FTP site available
Date: 22 Jan 93 16:13:47 GMT
Organization: University of Wales College of Cardiff

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). 
>
>The appendix of Goldberg's book, _Genetic Algorithms in Search,
>Optimization, and Machine Learning_, has code for a "Simple Genetic
>Algorithm" and a "Simple Classifier System" in it. 
>I don't know whether this is available on-line somewhere--I
>imagine it is-

Yes it is (in C, even). 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.

The administrator also requests:      

      Etiquette: Please use your email address as your password.
      This will help us to track usage of the facility.  Also, please
      restrict your usage to off-peak hours.  Do NOT use between
      8:00am and 6:00pm EST Monday through Friday.


--

David Beasley                          (David.Beasley@cm.cf.ac.uk)    
Department of Computing Mathematics 
University of Wales College of Cardiff                          __o
PO Box 916                                                      \<,   
CARDIFF    CF2 4YN                         ___________________()/ ()___  
Newsgroups: comp.ai.genetic
From: jbk@world.std.com (Jeffrey B Kane)
Subject: Penalty functions
Summary: Examples of penalty functions for optimization
Keywords: Penalty Optimization Minimization
Organization: The World Public Access UNIX, Brookline, MA
Date: Mon, 25 Jan 1993 17:55:56 GMT


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.]
Newsgroups: comp.ai.genetic
From: evs@cs.columbia.edu (Eric Siegel)
Subject: Reproduction Methods
Organization: Columbia University Department of Computer Science
Date: Fri, 22 Jan 1993 21:04:24 GMT

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?)

This way, the evolutionary method which converges most quickly for the
domain at hand will be converged to!  And this way, you don't have to
strain so hard to decided how much of biology to simulate.

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?"

To go a step further, for systems that use GAs on-line (such as
schedulers?), use GA technology to evolve the evolutionary method
_off-line_. 

---------

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.  In the latter, 
the Halting Problem prevents us from using all-out models of 
computation.  But I haven't heard these things mentioned 
explicitely...  Haven't they been addressed?

---------

Has there been any work using evolutionary systems to teach a computer
English?  (I'm in Natural Language Processing.)  References appreciated.

Newsgroups: comp.ai.genetic
From: alliot@cenatls.cena.dgac.fr (Jean-Marc Alliot)
Subject: How to order ICGA proceedings?
Organization: Centre d'Etudes de la Navigation Aerienne, Toulouse, France
Date: Sun, 24 Jan 1993 22:10:50 GMT


I would like to order ICGA proceedings, but don't know the exact
references (ISBN, publisher). Can any kind soul mail the information
to me?

Thanks in advance.
	
	Jean-Marc
Newsgroups: comp.ai.genetic