
Genetic Algorithms Digest   Tuesday, February 8, 1994   Volume 8 : Issue 4

 - 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:
	- Thesis ftp available on Genetic Neural Networks
	- GAs and Knowledge Generalizability
	- Information request
	- Distributed GA
	- GAucsd port to Borland C/C++?
	- WCCI Tutorial Abstracts

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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
COMPLEX94 2nd Australian National Conference, Australia (v7n34) Sep 26-28, 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: Frederic Gruau <gruau@drfmc.ceng.cea.fr>
Date: 23 Jan 94 10:45:46-0100
Subject: Thesis ftp available on Genetic Neural Networks

 The laboratory for Parallel Computer Science (LIP) is pleased to inform that
 the following PhD Thesis is available by  anonymous FTP. The title is:

 NEURAL NETWORK SYNTHESIS USING CELLULAR ENCODING AND THE GENETIC ALGORITHM
			by Frederic GRUAU.
 
			-----ABSTRACT------
 
Artificial neural networks used to be considered only as  a machine that learns
 using small modifications of internal parameters. Now this is changing. Such 
learning method do not allow to generate big neural networks for solving real
 world problems. This thesis defends the following three points:

	 (1) The key word to go out of that dead-end is "modularity".
	 (2) The tool that can generate modular neural networks is cellular
 	     encoding.
	(3) The optimization  algorithm adapted to the search of cellular codes
            is the genetic algorithm.

The first point is now a common idea. A modular neural network means a neural
 network that is made of several sub-networks, arranged in a hierarchical way.
For example, the same sub-network  can be repeated. This thesis encompasses two
 parts.

The first part demonstrates the second point. Cellular encoding is presented as
 a  machine language for neural networks, with a theoretical basis (it is a
 parallel graph grammar that checks a number of properties) and a compiler of 
high level language. 

The second part of the thesis shows the third point.
Application of genetic algorithm to the synthesis of neural networks using
cellular encoding is a new technology. This technology can solve problems that 
were still unsolved with neural networks. It can automatically and dynamically
 decompose a problem into a hierarchy of sub-problems, and generate a neural
network solution to the problem. The structure of this network is a hierarchy 
of sub-networks that reflects the structure of the problem. The technology
 allows to experience new scientific domains like the interaction between
 learning and evolution, or the set up of learning algorithms that suit the GA.

The anonymous ftp is: 140.77.1.11 (lip.ens-lyon.fr)
the directory is: pub/Rapports/PhD
The file PhD94-01-E.ps.Z is an english version
The file PhD94-01-F.ps.Z is a french versin

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

From: PAKATH@UKCC.uky.edu
Date:         Wed, 29 Dec 93 11:19:37 EST
Subject:      GAs and Knowledge Generalizability

Dear Colleagues:

Could any of you guide me to available literature, both theoretical
and applied,  on knowledge generalization by GA-based systems? That is, I
am interested in work that studies the ability of GA-based systems to
fruitfully apply knowledge (e.g., population members, GA operator
probability levels) gathered in one or more prior problem solving
episodes to other "similar" episodes, subsequently, where the term
"fruitfully" refers to increased problem solving effectiveness and/or
efficiency? In the event that this generally viewed by the GA research communit
y as a topic not worthy of study, could anyone indicate why?

I thank you for your interest and trouble and shall post all
"interesting" responses that I receive.

[Ed's Note:  John Grefenstette and I have applied case-based methods
in initializing the population of GAs that are used to guide search
in changing environments.  We have obtained very good results by using
good members of populations that learned in previous similar cases
(as defined by environmental parameters) to seed the current population.
A paper on this topic appeared in ICGA-93.  Zhou applied case-based
methods to classifier systems, and Whitley, Mathias and Fitzhorn
have a paper on Delta Coding, where they iteratively use previous
best solutions as a basis for altering the representation used during
a restart.
References:
Ramsey, C.L. and Grefenstette, J.J. (1993). Case-Based Initialization
of Genetic Algorithms.  Proc of the 5th Intl Conf on Genetic Algorithms.

Whitley, D., Mathias, K. and Fitzhorn, P. (1991). Delta Coding: An
Iterative Search Strategy for Genetic Algorithms. Proc of the 4th
Intl Conf on Genetic Algorithms.

Zhou, H.H. (1990). CSM: A Computational Model of Cumulative Learning.
Machine Learning 5(4), 383-406.]

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

From: capo@imdes02.dees.unict.it
Date: Fri, 7 Jan 1994 19:20:49 +0100
Subject: Information request

I would like to know if it exists an optimization software tool, GAs based, able
to manipulate "about" one thousand variables.

Thanks in advance

Riccardo Caponetto
PhD Student at
Universita' di Catania, Italy
Email dees@dees.unict.it (please specify my name)  

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

From: jvr@micas.inescn.pt (Joao Vasco Ranito)
Date: Fri, 7 Jan 94 16:38:22 GMT
Subject: Distributed GA

Hi.

Does anyone have some papers on Distributed Genetic Algorithms available
by ftp? It is not easy, here in Portugal, to get Proceedings and so on, so
we are more or less confined to ftp. If someone could give me some pointers,
I would be very gratefull. Oh! And I will publish the list on the Digest, of
course...

My e-mail is:

jfn@micas.inescn.pt

Thanks in advance.

			Joao Neto

Joao Filipe Neto                  e-mail: jfn@micas.inescn.pt
INESC                              fax   : + 351-2-318692
Largo Mompilher, 22                tel   : + 351-2-2094015
4000 Porto
Portugal

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

From: Nici Schraudolph <schraudo@salk.edu>
Date: Sat, 15 Jan 94 19:21:19 PST
Subject: GAucsd port to Borland C/C++?

> From: osherson@idiap.ch (Daniel Osherson)
> 
> For various reasons, I'd like to port the system to
> Borland C/C++. Since the code you provide is in an older
> version of C, this appears to be rather difficult.
> 
> Does there happen to be a version of GAucsd tailor-written
> for Borland C/C++ ?

Has anybody out there done this port?    - Nici.

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

From: john@cs.ucsd.edu (John McInerney)
Date: Thu, 6 Jan 94 15:58:41 -0800
Subject: WCCI Tutorial Abstracts

*****************************************************************************
		WCCI TUTORIAL ABSTRACTS
*****************************************************************************
FRESH, NEW, CUTTING EDGE TUTORIALS on ...
          # NEURAL NETWORKS
          # FUZZY TECHNOLOGY
	  # EVOLUTIONARY PROGRAMMING
At the ...
          1994 IEEE World Congress on
           COMPUTATIONAL INTELLIGENCE

          Walt Disney World Dolphin Hotel     
                 Orlando, FLA 
             June 26 - July 2, 1994 

[Ed's Note: This message has been shortened due to space constraints.
The full message, containing the descriptions of each tutorial,
is available from the ftp server, ftp.aic.nrl.navy.mil in the
file /pub/galist/info/conferences/WCCI-tutorial-94.  -- Connie]

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

"Evolution Strategies:  A Thorough Introduction"
Professor Thomas Baeck
Computer Science Department, LS XI
University of Dortmund, Dortmund, Germany

In addition to Genetic Algorithms and Evolutionary Programming, the
Evolution 
Strategy (Evolutionsstrategie) by Rechenberg and  Schwefel forms the third 
major representative of Evolutionary Algorithms.  Since its development in 
the 1960s at the Technical University of Berlin (Germany) for solving 
experimental optimization problems, the computer algorithm has been 
successfully applied to numerous hard continuous parameter optimization 
problems (an application field where Evolution Strategies reveal their 
strengths in comparison to the more familiar Genetic Algorithms).

The tutorial presents a thorough introduction to Evolution Strategies, with

special emphasis on the history of evolution strategies, detailed
presentation 
and explanation of the algorithm, genetic operators and parameter settings,

self-adaptation of strategy parameters, theory of evolution strategies, 
selected application examples of evolution strategies, evolution strategies

for neural networks and fuzzy logic,  guidelines for practitioners, and 
comparison to genetic algorithms and evolutionary programming.

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

"An Introduction to Evolutionary Computation"
David B. Fogel, Ph.D.
1591 Calle De Cinco
La Jolla, CA

The impact of evolutionary thinking on biology cannot be underestimated.  
Indeed, many biologists have remarked that the study of life cannot be 
conducted reasonably in the absence of an evolutionary paradigm.  But 
evolutionary thought extends beyond an ordering principle of biology. 
Evolution is a process that can be simulated on a computer and used for 
solving difficult engineering problems and gaining insight into natural 
evolved systems.  This tutorial, aimed at researchers in neural networks
and 
fuzzy systems, will introduce methods of evolutionary computations These 
include genetic algorithms, evolution strategies and evolutionary
programming, 
as well as related techniques.  The fundamental philosophical foundations
of 
the methods will be discussed and applications will be described, including

synergistic efforts of combining evolutionary optimization with
connectionist 
and fuzzy systems.

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

"Genetic Algorithms and Their Applications"
Dr. Lawrence "David" Davis, President
Tica Associates
Cambridge, MA 02139

Genetic algorithms are techniques for optimization and machine learning
that 
have been applied to a wide range of real-world problems.  This tutorial 
consists of an overview of genetic algorithms, a discussion of techniques
for 
applying them, a survey of areas in which they have been applied, and
several  
application case studies.  Particularly stressed in the tutorial will be 
traditional and nontraditional genetic algorithms for numerical function 
optimization; the use of order-based genetic algorithms for combinatorial 
optimization; and techniques for hybridizing genetic algorithms with other 
optimization algorithms.

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

"Genetic Programming"
Dr. John R. Koza
Consulting Professor
Computer Science Department, Stanford University

Genetic programming extends the genetic algorithm to the domain of computer

programs and genetically breeds populations of computer programs to solve 
problems.  Genetic programming can solve problems of system identification,

optimal control, pattern recognition, equation solving, game playing, 
optimization, and planning.  Starting with hundreds or thousands of
randomly 
created programs, the population is progressively improved by applying 
Darwinian fitness proportionate reproduction and crossover (sexual 
recombination).

Many problem environments have regularities, symmetries, and homogeneities 
that can be exploited in solving the problem.  The recently developed
facility 
of automatic function definition enables genetic programming to dynamically

decompose a problem into simpler subproblems, solve the subproblems, and 
assemble original problem.  Experimental evidence suggests that automatic 
function definition reduces the computation effort needed to solve a
problem 
and produces a simpler and more understandable overall solution. 

Portions of videotapes on genetic programming will be shown.

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

"Genetics-Based Machine Learning in Rule-Based and Neural Systems"
Professor Robert E. Smith
Department of Engineering Science and Mechanics
The University of Alabama, Tuscaloosa, Alabama

This tutorial covers the application of genetic algorithms (GAs) in machine

learning.  Machine learning is introduced in the framework of control, with

an emphasis on reinforcement learning, where the system must learn through
a 
exploration.  A brief overview of GAs is also provided.  Given this
background, 
the tutorial discusses rule-based, neural, and fuzzy techniques that
utilize 
GAs.  A rule-based technique, the learning classifier system (LCS), is
shown 
to be analogous to a neural network.  The integration of fuzzy logic into
the 
LCS is also discussed.  Research issues related to GA-based learning are 
overviewed.  The application potential for genetics-based machine learning 
is discussed. 

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

"Genetic Algorithms:  Theoretical Foundations and Experimental Evaluation"
Professor Darrell Whitley, 
Computer Science Colorado State University, Fort Collins, CO 80523

The principle of hyperplane sampling will be examined,  as well as exact 
theoretical models of a canonical genetic algorithm.  Other topics include:
 
deception, remapping hyperspace, stochastic hill-climbing versus hyperplane

sampling and the case against gray coding for test functions.  Holland's 
schema theorem and the K-arm bandit analogy will be reviewed and critiqued.
 
Alternative forms of the genetic algorithm such as Genitor, CHC, Evolution 
Strategies and parallel genetic algorithms will be reviewed.  The practical

implications of the existing theory will be explored with respect to 
implementing and applying genetic algorithms to complex problems.  Examples

are given where simple theoretical insights result in improved search on 
problems of more than 500 variables.

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

"An Introduction to Fuzzy Logic"
Professor James Bezdek
Department of Computer Science
University of West Florida, Pensacola, Florida

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

"Fuzzy Sets in Constraint Satisfaction"
Didier Dubois
Institut de Recherche en Informatique de Toulouse
Universite Paul Sabatier,  Toulouse Cedex - France

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

"Fuzzy Logic in Computer Vision"
Professor James M. Keller
Electrical and Computer Engineering Department
University of Missouri-Columbia, Columbia, MO

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

"Fuzzy Logic Applications to Artificial Intelligence and Intelligent
 Control Systems"
Enrique H. Ruspini
Artificial Intelligence Center
SRI International
Menlo Park, CA

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

"Fuzzy Neurocomputations"
Witold Pedrycz
Dept. of Electrical and Computer Eng.
University of Manitoba, Winnipeg

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

"Fuzzy Data Analysis"
Prof. Dr. Dr.h.c.Hans-Jurgen Zimmermann
Professor of Operations Research
RWTH Aachen
Aachen, Germany

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

"Applications of Neural Networks to Virtual Reality"
Professor Thomas P. Caudell
Department of Electrical Engineering and Computer Engineering
University of New Mexico, Albuquerque, New Mexico

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

"Computational Studies Of Biological Neural Networks: Introduction And 
Applications To Vision And Sensory-Motor Control"
Paolo Gaudiano
Department of Cognitive and Neural Systems
Boston University, Boston, MA

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

"Hybrid Systems: Neural, Symbolic, and Fuzzy"
Lawrence O. Hall and Abraham Kandel
Computer Science and Engineering Department
University of South Florida, Tampa, Fl.

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

"Practical Applications of Neural Network Theory"
Dr. Robert Hecht-Nielsen
HNC, Inc
San Diego, CA

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

"Basics of Building Market Timing Systems: Making Money with Neural 
Networks"
Casimir C. Klimasauskas
NeuralWare, Inc.

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

"Learning Algorithms In Neural Networks"
Professor Jacek M. Zurada
Computer Science and Engineering
University of Louisville, Louisville, KY


For more information contact:
     IEEE ICNN
     IEEE World Congress on Computational Intelligence
     Meeting Management
     2603 Main Street, Suite # 690
     Irvine, California 92714
     1-800-321-MEET
     FAX 714 752 7444
     e-mail: 74710.2266@compuserve.com

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

