
Genetic Algorithms Digest   Tuesday, December 14, 1993   Volume 7 : Issue 33

 - 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:
	- new report
	- Paper Available
	- GAs and protein folding bibliography
	- John Holland's Royal Road
	- are negative results important? how to prove?
	- Evolutionary Economics
	- Sending CFP of WWW

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

EP94 3rd Ann Conf on Evolutionary Programming, San Diego (v7n7) Feb 24-25, 94
IEE94 Colloquium on Molecular Bioinformatics, London, UK (v7n21)   Feb 28, 94
SPIE, Neural & Stoch. Methods in Image & Sig Proc, Orlando(v7n18) Apr 5-8, 94
FLAIRS-94 Workshop on Artif Life and AI, Pensacola Beach, FL(v7n23) May 4, 94
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
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
PPSN-94 Parallel Problem Solving from Nature, Israel (v7n32)     Oct 9-14, 94

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

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From: E.Pesch@KE.RULIMBURG.NL
Date: Tue, 23 Nov 1993 00:08:43 +0100 (MET)
Subject: new report

The following paper describes genetic algorithms as a special case of 
cellular automata. Anyone who whishes to receive a copy of the paper
please contact

        Alf Kimms
        Lehrstuhl fuer Produktion und Logistik
        Institut fuer Betriebswirtschaftslehre
        Christian-Albrechts-Universitaet
        D-24118 Kiel
        Germany
        FAX ..49 431 880-2072
        Tel...49 431 880-1531

                           Abstract

Cellular automata were used to model and to simulate phenomena in the area of
physics, biology and medicine. In this paper it is shown how the idea of 
cellular automata can be applied to optimization problems as well. As an 
example a cellular automaton is used as a basis for solving multi-level lot 
sizing and scheduling problems to suboptimality. We will furthermore give an 
outline of a proof that any genetic algorithm can be interpreted as a 
cellular automaton.

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

From: eehbp@cc.newcastle.edu.au
Date: Fri, 03 Dec 1993 14:29:40 +0000
Subject: Paper Available

The following paper is available via anonymous ftp:

   Deriving Application-Specific Neural Nets Using a
       Massively Parallel Genetic Algorithm

H. B. Penfold}                    U. Kohlmorgen, and H. Schmeck
The University of Newcastle,      Universitat Karlsruhe
Australia                         Germany

Abstract

This paper describes  a genetic algorithm which has been
applied to the derivation of both the structure and the connective weights
for  simple computational networks similar to recurrent neural nets.  The
algorithm has been implemented on a massively parallel
Single-Instruction-Multiple-Data (SIMD) architecture computer.  It
incorporates  a breeding  model, and  a mutation model which applies to
both the genetic descriptor and the transcription from the gene to the
phenotype.

Advantages of the method include the general connectivity of the resultant
network with the potential for economy of structure, the ability to
consider
complex performance criteria (for example fault-tolerance), and the freedom
to choose any desired node transfer function -- not merely those
functions which are continuously differentiable.  The massively parallel
structure allows very large population sizes (32K) at each generation, and
convergence typically requires few (<20) generations.

Examples include a single, double and triple fault-tolerant 
solution to the exclusive or problem, and a 2-node solution for a 6-input
incomplete even parity problem.

The paper is available electronically from tesla.newcastle.edu.au

	ftp tesla.newcastle.edu.au
	login: anonymous
	password: <your email address>
	cd /pub/hbp
	binary
	get Parallel_GA_004.ps.Z
	quit

At your system:

	uncompress Parallel_GA_004.ps.Z
	lpr -P<printer-name> Parallel_GA_004.ps.Z

Bruce Penfold,   	       	      
Dept. Elect. & Computer Engineering, 
University of Newcastle, Australia   	
University Drive,
CALLAGHAN  NSW 2308  AUSTRALIA

EMAIL:   eehbp@cc.newcastle.edu.au
PHONE:   +61 (0)49 21 6086
FAX:     +61 (0)49 60 1712

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

From: mm@santafe.edu (Melanie Mitchell)
Date: Thu, 18 Nov 93 11:01:14 MST
Subject: GAs and protein folding bibliography

Thanks to all who responded to my query about GAs and protein folding.  Here is
a compiled list of citations I was sent on GAs and various aspects of protein
folding and related areas.  

Many people expressed interest in this list, so if anyone has any additional
citations to add, please post them to the list.  

Melanie Mitchell

================

Marcel J. J. Blommers, Carlos B. Lucasius, Gerrit Kateman and Robert
Kaptein, Conformational Analysis of a Dinucleotide Photodimer with the
Aid of the Genetic Algorithm, Biopolymers, Vol. 32, 45-52, (1992)

Bouzida,D.,S.Jumar, and R.H.Swendsen, "A Simulated Aneeling Appproach
for Probing Biomolecular Structures", 26th Hawaii International Conference on 
System Sciences, vol.I, IEEE, 1993

Cedeno, Walter.  DNA restriction fragment map assembly with genetic
algorithms.  In Genetic Algorithms at Stanford 1993.  Stanford, CA:
Stanford University Bookstore. 1993.

Clark, Jones, Willett, Glen and Kenny, Pharmacophoric Pattern Matching
In Files Of Three-Dimensional Chemical Structures: Comparison Of
Conformational-Searching Algorithms For Flexible Searching, Presented
at the 3rd Int. conference on Chemical Structures, Journal of Chemical 
Information and Computer Sciences, (1993, in press).

Thomas Dandekar and Patrick Argos, Potential of genetic algorithms in
protein folding and protein engineering simulations, Protein
Engineering 5(7), 637-645, (1992)

David Eisenberg, Determining Protein Folds by Inverted and
Evolutionary Protein Folding Algorithms", Proceedings of the North
Caroline Symposium on Molecular Modeling: Integration of Theory and
Experiment, Research Triangle Park, North Carolina,21-23, 1993.

Fickett, James W. and Cinkosky, Michael J.  A genetic algorithm for
assembling chromosome physical 
maps.  Unpublished draft 1993.

E. Fontain, Application of Genetic Algorithms in the Field of Constitutional
Similarity, J. Chem. Inf, Comput Sci, 32, 748-752, (1992)

M. S. Freidrichs and P. G. Wolynes, Genetic algorithms for model biomolecular
optimization problems.  Unpublished manuscript.  Noyes Lab., University of
Illinois, Urbana, Il, 61801. 

S. Handley, Automated learning of a detector for helices in protein
sequences via genetic programming.  In 
S. Forrest (Ed.), Proceedings of the Fifth International Conference
on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA. 1993. 

Jones, Brown, Clark, Willett and Glen.  Searching Databases of Two-Dimensional
and Three-Dimensional Chemical Structures Using Genetic Algorithms, in Forest,
S. ed., Proceedings of the Fifth International Conference on Genetic 
Algorithms, Morgan Kaufmann Publishers, San Mateo, CA (1993).

Richard S. Judson, Teaching Polymers to fold, The Journal of Physical
Chemistry, 96(25), 10102, (1992).

R.S.Judson, M.E. Colvin, J.C.Meza, A.Huffer, and D.Gutierrez.  Do
intelligent configuration search techniques outperform random search
for large molecules?  International Journal of Quantum Chemistry, 44,
277-290, (1992).

R.S. Judson, E. Jaeger, A. Treasurywala, Conformation searching methods
for small molecules II: A Genetic algorithm approach, J.Comp.Chem.
(1993, in press).

Akihiko Konagaya, A stochastic approach to genetic information
processing.  Institute for New Generation Computer Technology, ICOT
Technical Memorandom: TM-1263", May, 1993.

Konagaya,A. and H.Kondo,"Stochastic Motif Extraction using a Genetic
Algorithm with the MDL principle"., 26th Hawaii International Conference on 
System Sciences, vol.I, IEEE, 1993

S. LeGrand and K. Merz, The application of the Genetic Algorithm to the
minimization of potential energy functions, J.Global Opt., (1992).

Leopold,P.E. and E.I.Shakhnovich, "Protein Folding Kinetics in the
Dense Phase", 26th Hawaii International Conference on System Sciences, 
vol.I, IEEE, 1993

Wolfgang Linert, Peter Margl and Istvan Lukovits, Numerical Minimization
Procedures in molecular mechanics: structural modelling of the solvation of
beta-cyclodextrin, Computers & Chem. 16(1), 61-69, (1992).

C. B. Lucasius and G. Kateman, Application of genetic algorithms in 
chemometrics.  In J. D. Schaffer (Ed.), Proceedings of the third international
conference on genetic algorithms.  Morgan Kaufmann, San Mateo, CA. 1989. 

D.B. McGarrah and R.S. Judson, An analysis of the genetic algorithm
method of molecular conformation determination, J.Comp.Chem.
(1993, in press).

Platt,D.M. and T.I.Dix,"Construction of Restriction Maps Using a
Genetic Algorithm", 26th Hawaii International Conference on 
System Sciences, vol.I, IEEE, 1993

S. Schulze-Kremer, Genetische Algorithmen zur Vorhersage von
Proteintertiaerstrukturen, in Fortschritte der Simulation in Medizin,
Biologie und Oekologie, (D.P.F. Moeller, O. Richter Hrsg.), Technische
Universitaet Clausthal- Zellerfeld, Institut fuer Informatik, pp. 
217-238, 1992.
 
Steffen Schulze-Kremer, Genetic Algorithms in Biochemistry (GALB):
Learning Protein Folding Pathways.  In Joachim Stender (Ed.), Parallel
Genetic Algorithms.  IOS Press, Amsterdam. 1992.

S. Schulze-Kremer, Genetic algorithms for protein tertiary structure
prediction.  In R. Manner and B. Manderick (Eds.), 
Parallel Problem Solving from Nature 2, pp. 391-400. 
North Holland, Amsterdam, 1992.  

S. Schulze-Kremer, U. Tiedemann, Genetic Algorithms for Protein
Tertiary Structure Prediction, in Artificial Intelligence and Genome
Workshop 26, (J.-G. Ganascia Ed.), International Joint Conference on
Artificial Intelligence, Institut Blaise Pascal, Paris, pp. 119-141,
1993.

S. Schulze-Kremer, U. Tiedemann, Parameterizing Genetic Algorithms
for Protein Folding Simulation, to appear in the Proceedings of the 27th 
Hawaii International Conference on System Sciences, 1994. 

R.W. Smith, "Energy minimization in binary alloy models via genetic
algorithms" Comput.Phys.Comm. 71, 134-146, (1992).

Shaojian Sun, Reduced representation model of protein structure
prediction: Statistical potential and genetic algorithms, Protein
Science, 2, 762-785, (1993)

Joachim Stender and Tom Addis, Using the Genetic Algorithm to Adapt
Intelligent Systems.  In Symbols versus Neurons?.  IOS Press,
Amsterdam, 1990.

P. Tuffery and C. Etchebest and S. Hazout and R. Lavery, A New Approach
to the Rapid Determination of Protein Side Chain Conformations, Journal
of Biomolecular Structure & Dynamics, 8(6), 1267, (1991)

P. Tuffery, C. Etchebest, S. Hazout, and R. Lavery, A Critical Comparison of
Search Algorithms Applied to the Optimization of Protein Side-Chain
Conformations, J. Comp. Chem. 14, 790-798, (1993).

R. Unger and J. Moult,
A genetic algorithm for 3D Protein Folding Simulations,
In S. Forrest (Ed.), 
Proceedings of the Fifth International Conference on Genetic Algorithms,
581-588. Morgan Kaufmann, San Mateo, CA. 1993. 

Ron Unger and John Moult, Genetic Algorithms for Protein Folding
Simulations, Journal of Molecular Biology, 231, 75-81, (1993).

Unger,R. and J. Moult, "On the applicability of genetic algorithms to
protein folding", 26th Hawaii International Conference on System Sciences, 
vol.I, IEEE, 1993

Jukka Vanhala and Kimmo Kaski, Protein folding simulation by genetic
algorithms.  In Risto Nieminen and Olle Teleman (Eds.), 7th Nordic
Symposium on Computer Simulation, Espoo, Finland, 1993.

R. Wehrens, C. Lucasius, L. Buydens, and G. Kateman, Sequential Assignment
of 2D-NMR Spectra of Proteins Using Genetic Algorithms, J. Chem. Info.
Comput. Sci.  33, 245-251 (1993).

Y.L. Xiao and D.E. Williams, Genetic algorithm: a new approach to the
prediction of the structure of molecular clusters, Chem. Phys. Let. 
(1993, in press)

Y.L. Xiao and D.E. Williams, GAME: Genetic algorithm for minimization of
energy, an interactive program for three-dimensional intermolecular 
interactions, Comput. & Chem, (submitted)

D. C. Youvan and A. P. Arkin and M. M. Yang, Recursive ensemble
mutagenesis - A combinatorial optimization technique for protein
engineering.  In R. Manner and B. Manderick (Eds.), Parallel Problem
Solving from Nature 2.  North Holland, Amsterdam, 1992.

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

From: terry@santafe.edu
Date: Fri, 19 Nov 93 00:48:01 MST
Subject: John Holland's Royal Road

There hasn't been much response to John Holland's Royal Road problem.
Has anyone run a standard GA (or other variant) on it? Do you have any
results you could mail me? I am mainly interested in the number of
evaluations it takes you to achieve the various levels on average.

Apart from Joe Culberson's GIGA and Ron Hightower's "cheap shot", no-one
has responded to this, and neither of those responses used what we'd
typically call a GA.

Maybe people have tried it and not been able to match John's 
"regularly achieves level 3 in substantially less than 10,000 evaluations"?

If that's so, you might be encouraged by the fact that John is also
not using a traditional GA.

I'd like to get some feel for how other versions of GAs perform on this
problem. I'll happily post some numbers, but is anyone even interested?

There was certainly a lot of interest at ICGA after his talk...

Terry Jones (terry@santafe.edu).

P.S. If you want a C version of the fitness function, I have one that
	 Joe Culberson gave me which I expect he'd be happy to have passed on.
	 Joe, Ron and I spent a lot of time making sure that it was correct.

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

From: mlevin@husc.harvard.edu
Date: Thu, 11 Nov 93 15:20:04 -0500
Subject: are negative results important? how to prove?

     I'd like people's thoughts on the following matter. Are negative
results in GA research important? Does anyone care if a particular
problem is hard for GAs (in the sense that the top fitness never rises
above that of the random population's)? If they do, then given that
someone has found a problem for which the fitness never seems to get
any better, how does one go about convincing people that it isn't just
his program that is messed up? What about the fact that an enormous
number of parameters for a GA can be tweaked (you can't possibly try
them all)? I'd appreciate any thoughts on this matter. Please email to
mlevin@husc8.harvard.edu.

Mike Levin

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

From: "Andrew Hall"  <andrewh@logcam.co.uk>
Date: Fri, 26 Nov 93 13:57:21 GMT
Subject: Evolutionary Economics

I'm interested in the use of GAs to optimise aspects of finance.  For instance,
has anyone done work on evolutionary techniques in portfolio management?
Or in generating price prediction techniques?

Any help would be appreciated.

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

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

From: furu@bioele.nuee.nagoya-u.ac.jp (Takeshi Furuhashi)
Date: Mon, 8 Nov 93 16:21:14 JST
Subject: Sending CFP of WWW

CALL FOR PAPERS                                   TENTATIVE
1994 IEEE/Nagoya University
World Wisemen/women Workshop(WWW)

ON FUZZY LOGIC AND NEURAL NETWORKS/GENETIC ALGORITHMS
-Architecture and Applications for Knowledge Acquisition/Adaptation-

August 9 and 10, 1994
Nagoya University Symposion
Chikusa-ku, Nagoya, JAPAN

Sponsored by Nagoya University

Co-sponsored by
IEEE Industrial Electronics Society

Technically Co-sponsored by
IEEE Neural Network Council
IEEE Robotics and Automation Society
International Fuzzy Systems Association
Japan Society for Fuzzy Theory and Systems
North American Fuzzy Information Processing Society
Society of Instrument and Control Engineers
Robotics Society of Japan

There are growing interests in combination technologies of fuzzy logic
and neural networks, fuzzy logic and genetic algorithm for acquisition
of experts' knowledge, modeling of nonlinear systems, realizing
adaptive systems. The goal of the 1994 IEEE/Nagoya University WWW on
Fuzzy Logic and Neural Networks/Genetic Algorithm is to give its
attendees opportunities to exchange information and ideas on various
aspects of the Combination Technologies and to stimulate and inspire
pioneering works in this area. To keep the quality of these workshop
high, only a limited number of people are accepted as participants of
the workshops. The papers presented at the workshop will be edited and
published from the Oxford University Press.

TOPICS:
Combination of Fuzzy Logic and Neural Networks, Combination of Fuzzy
Logic and Genetic Algorithm, Learning and Adaptation, Knowledge
Acquisition, Modeling, Human Machine Interface

IMPORTANT DATES:
Submission of Abstracts of Papers : April 31, 1994
Acceptance Notification           : May 31, 1994
Final Manuscript                  : July 1, 1994

A partial or full assistance of travel expenses for speakers of
excellent papers will be provided by the WWW. The candidates should
apply as soon as possible, preferably by Jan. 30, '94

All correspondence and submission of papers should be sent to 
Takeshi Furuhashi, General Chair
Dept. of Information Electronics, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-01, JAPAN
TEL: +81-52-781-5111 ext.2792
FAX: +81-52-781-9263
E mail: furu@uchikawa.nuem.nagoya-u.ac.jp

IEEE/Nagoya University WWW:

IEEE/Nagoya University WWW(World Wisemen/women Workshop) is a series
of workshops sponsored by Nagoya University and co-sponsored by IEEE
Industrial Electronics Society. City of Naoya, located two hours away
from Tokyo, has many electro-mechanical industries in its surroundings
such as Mitsubishi, TOYOTA, and their allied companies. Nagoya is a
mecca of robotics industries, machine industries and aerospace
industries in Japan. The series of workshops will give its attendees
opportunities to exchange information on advanced sciences and
technologies and to visit industries and research institutes in this
area.

*This workshop will be held just after the 3rd International
Conference on Fuzzy Logic, Neural Nets and Soft Computing(IIZUKA'94)
from Aug. 1 to 7, '94.

WORKSHOP ORGANIZATION

Honorary Chair: Tetsuo Fujimoto
                (Dean, School of Engineering, Nagoya University)
General Chair:  Takeshi Furuhashi (Nagoya University)
Advisory Committee:
        Chair:  Toshio Fukuda (Nagoya University)
                Fumio Harashima (University of Tokyo)
                Yoshiki Uchikawa (Nagoya University)
                Takeshi Yamakawa (Kyushu Institute of Technology)
Steering Committee:
                H.Berenji (NASA Ames Research Center)
                W.Eppler (University of Karlsruhe)
                I.Hayashi (Hannan University)
                Y.Hayashi (Ibaraki University)
                H.Ichihashi (Osaka Prefectural University)
                A.Imura
                (Laboratory for International Fuzzy Engineering)
                M.Jordan (Massachusetts Institute of Technology)
                C.-C.Jou (National Chiao Tung Universtiy)
                E.Khan (National Semiconductor)
                R.Langari (Texas A & M University)
                H.Takagi (Matsushita Electric Industrial Co., Ltd.)
                K.Tanaka (Kanazawa University)
                M.Valenzuela-Rendon
                (Institute Tecnologico y de Estudios Superiores de Monterrey) 
                L.-X.Wang (University of California Berkeley)
                T.Yamaguchi (Utsunomiya University)
                J.Yen (Texas A & M Universtiy)

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End of Genetic Algorithms Digest
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