From khedkar@merak.crd.ge.com Tue Feb 1 21:46:55 EST 1994 Article: 2174 of comp.ai.genetic Xref: glinda.oz.cs.cmu.edu comp.ai.fuzzy:1765 comp.ai.neural-nets:14531 comp.ai.genetic:2174 Newsgroups: comp.ai.fuzzy,comp.ai.neural-nets,comp.ai.genetic Path: honeydew.srv.cs.cmu.edu!rochester!news.crd.ge.com!merak!khedkar From: khedkar@merak.crd.ge.com (Pratap Khedkar) Subject: WCCI 94: Symposium on IMITATING LIFE : Prelim. Program Message-ID: Sender: usenet@crdnns.crd.ge.com (USENET News System) Nntp-Posting-Host: merak.crd.ge.com Organization: GE Corp. Research & Development, Schenectady, NY Date: Sat, 29 Jan 1994 16:45:11 GMT IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE ORLANDO, FLORIDA, JUNE 26-JULY 2, 1994 Walt Disney Word Dolphin Hotel PRELIMINARY PROGRAM FOR THE SYMPOSIUM ****************************************** COMPUTATIONAL INTELLIGENCE: IMITATING LIFE ****************************************** JUNE 27 - JULY 1, 1994 (for further info contact the Meeting Management ph: 714-752-8205, fax 714-752-7444 e-mail: 70750.345@CompuServe.com) The Symposium addresses critical and emerging technologies and issues relating to biologically, psychologically, and linguistically motivated models that exhibit various facets of computational intelligence. The paradigms discussed include learning, reasoning, evolution, search, and optimization each of which often uses life imitating metaphors for guiding model building. Machine learning from data, neural and fuzzy information processing, approximate reasoning, and evolutionary computation, are examples of computational intelligence approaches addressed by Symposium speakers. The Symposium provides a unique forum for cross-fertilization between the areas of neural networks, fuzzy logic, and evolutionary computing. Symposium presentations are explicitly targeted towards the identification of challenges, issues, and potential solutions for problems arising in computational intelligence. The Symposium consists of 3 public lectures, 10 plenary talks and 30 mini-symposia presentations, covering Neural Networks (21), Fuzzy Logic (13) and Evolutionary Computation (9). Contributions include recent research that has implications for further progress, state-of-the-art reviews, and discussions of important applications in fields such as biology, signal and imaging processing, robotics and control. Presenters have been chosen from academia and industry and represent the leaders in their fields from throughout the world. The Symposium Proceedings "Computational Intelligence: Imitating Life," will be published and available at the Congress for each participant. Proceedings will later be distributed by the IEEE Press. ****************************************** Part I: SYMPOSIUM LECTURES M0N-FRI 10:20am - 12:40 pm ****************************************** Genetic Algorithms: A 25 Year Perspective, Kenneth DeJong, George Mason University Evolutionary Programming In Perspective, Lawrence Fogel, L. J. Fogel, Ph.D., Inc., La Jolla, California Genetic Algorithms for Optimization: Three Case Studies, Lawrence Davis, Tica Associates, Cambridge, Massachusetts Beyond AI Winter: The Double Helix of AI and Alife, Hiroaki Kitano, Sony Computer Science Laboratory, Inc., Japan How to Improve GA-Performance for Combinatorial Optimization Problems by Analyzing Their Fitness Landscape, Bernard Manderick, Erasmus University Rotterdam, The Netherlands Theory and Applications of the Breeder Genetic Algorithm, Heinz Muehlenbein, Germany Evolution Strategy, Ingo Rechenberg, Technische Universitaet Berlin, Germany Combinations of Genetic Algorithms with NNs or Fuzzy Systems, David Schaffer, Philips Laboratory, Briarcliff Manor, New York Similarity-Based Approximate Reasoning, Henri Prade, Universite Paul Sabatier, France Hybrid Approaches for Fuzzy Data Analysis and Configuration Using Genetic Algorithms and Evolutionary Methods, Hans J. Zimmermann, Aachen Institute of Technology, Germany Reasoning Under Uncertainty and Learning in Knowledge Based Systems: Imitating Human Problem Solving Behavior, Ramon Lopez de Mantaras, Girona, Spain Fuzzy Systems that Can Learn, Hamid Berenji, NASA Ames Research Center, Moffett Field, California Integration of Fuzzy Logic Within Hierarchically Structured Control Systems, Reza Langari, Texas A & M University Fuzzy Logic Controllers: An Industrial Reality, Piero P. Bonissone, General Electric, Schenectady, New York A Neo Fuzzy Neuron and its Application to System Identification and Expectation of Chaotic Behavior, Takeshi Yamakawa, Kyushu Institute of Technology, Fukuoka, Japan Qualitative Modelling Based on Numerical Data and Knowledge Data, and its Application to Control, Michio Sugeno, Tokyo Institute of Technology, Yokohama, Japan What Is Computational Intelligence, James C. Bezdek, University of West Florida Computational Intelligence in High Level Computer Vision: Determining Spatial Relationships, James Keller, University of Missouri-Columbia Fuzzy Modelling: Methodology, Algorithms, and Practice, Witold Pedrycz, University of Manitoba, Canada Learning as Adaptive Interpolation in Neural Fuzzy Systems, Pratap Khedkar, General Electric, Schenectady, New York Fuzzy-Neuro-GA Based Intelligent Robotics, Toshio Fukuda, Nagoya University, Japan Self-Generation of Neural Net Controller by Training In Natural Environment, Teruo Fujii, The University of Tokyo, Japan Learning Control Aspects In Terms of Neuro-Control, Tetsuro Yabuta, NTT, Japan Learning of Neural Controllers in Intelligent Control Systems, Sigeru Omatu, The University of Tokushima, Japan Visual Learning of Objects: Neural Models of Shape, Color, Motion and Space, Allen Waxman, MIT Lincoln Laboratory Unsupervised Learning for Feature Extraction, Erkki Oja, Helsinki University of Technology, Finland Neural Networks and Pattern Recognition, Anil K. Jain, Michigan State University Neural Representations of Space in Rats and Robots, David Touretzky, Carnegie Mellon University Computational Color Vision Model by Neural Networks, Shiro Usui, Toyohashi University of Technology, Japan Status of Auditory Modeling Research and its Relationship to Automatic Speech Recognition, Karen Payton, University of Massachusetts at Dartmouth Neural Network Theory - Early Payoffs and New Challenges, Robert Hecht-Nielsen, HNC, Inc., San Diego, California Neural Networks For Time Series, John Moody, Oregon Graduate Institute of Science and Technology, Beaverton, Oregon Biology-Inspired Pulse Processing Neural Nets With Adaptive Weights & Delays - Concept Sources From Neuroscience Vs. Applications in Industry and Medicine, Rolf Eckmiller, University of Bonn, Germany New Paradigms in Technology Transfer, Joseph R. Brown, Microelectronics and Computer Technology Corp., Austin, Texas Why Does TD-Gammon Learn So Well, Gerald Tesauro, IBM Thomas J. Watson Research Center, Yorktown, New York Neurobiological Computational Systems, Charles H. Anderson, Washington University Neural Computing Technology Transfer - A UK Government Programme, Robert A. Wiggins, Department of Trade and Industry, London, England Integrating Neural Networks For Real World Applications, Francoise Fogelman, SLIGOS - CSIA/LRN, France Biomedical Applications of Computational Intelligence, Russell Eberhart, Research Triangle Institute, Research Triangle Park, North Carolina Visual Preprocessing, George Sperling, University of California, Irvine ****************************************** Part II: PUBLIC LECTURES ****************************************** On the Evolution of Evolutionary Computation, Hans-Paul Schwefel, University of Dortmund, Germany No title available as of this date, Lofti Zadeh, University of California, Berkeley How Captain Amerika Uses Neural Networks To Fight Crime, Steven K. Rogers, Air Force Institute of Technology, Wright-Patterson AFB, Ohio ****************************************** Symposium Coordinator: Dr. Jacek M. Zurada Samuel T. Fife Alumni Professor of Electrical Engineering University of Louisville, Louisville, KY 40292 (502) 588-6314 (voice, till Dec.31,1993 *** (502) 852-6314 after Jan.1,1994 (502) 588-6807 (fax), till Dec.31,1993 *** (502) 852-6807 after Jan.1,1994 jmzura02@ulkyvx.louisville.edu ****************************************** Article 1975 of comp.ai.fuzzy: Xref: glinda.oz.cs.cmu.edu comp.ai.fuzzy:1975 Path: honeydew.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!emory!swrinde!sgiblab!cs.uoregon.edu!reuter.cse.ogi.edu!netnews.nwnet.net!news.u.washington.edu!marks From: marks@u.washington.edu (Robert Marks) Newsgroups: comp.ai.fuzzy Subject: WCCI & FUZZ-IEEE Registration Form Date: 11 Mar 1994 20:42:15 GMT Organization: University of Washington, Seattle Lines: 1006 Message-ID: <2lql37$lio@news.u.washington.edu> NNTP-Posting-Host: carson.u.washington.edu Please Post, Circulate and Forward ___________________________________________________ -------------------------- REGISTRATION INFORMATION -------------------------- IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE Orlando, FLA June 26-July 2, 1993 IEEE International Conference on Neural Networks Third IEEE International Conference on Fuzzy Systems The IEEE Conference on Evolutionary Computation Special Plenary Symposium "Computational Intelligence: Imitating Life" -> Over 1600 Refereed and Invited Presentations <- -> 43 Cutting Edge Plenary Presentations <- -> Eighteen Cutting Edge Tutorials on Newest Innovations <- -> Current Technology Exhibits <- *** * ** ** *** ** **** **** **** * * ** ***************** * ***** * * ** ************ * * X ************** * **** ****** ** ** ****** ***** ******* * *** ** *** * ** * *** *** * * ***** * * * * Sponsored by the IEEE Neural Networks Council Exhibits organized by SPIE ******************************************** FREE CD-ROMS OF ICNN'93 AND FUZZ-IEEE'93 FOR THE FIRST 1000 REGISTRANTS ******************************************** For additional information, please contact: WCCI'94 Conference Office Meeting Management 2603 Main Street, Suite 690 Irvine, CA 92714 Tel. (714) 752-8205 Fax (714) 752-7444 e-mail: 74710.2266@compuserve.com CONTENTS OF THIS POSTING: 1. General Conference Information 2. Conference Registration Form 3. Tutorial Registration 4. Methods of Payment 5. Hotel Registration Form 6. Spouse Activities 7. Tutorial Titles 8. Tutorial Abstracts 9. Exhibits Information ****************************************** 1. General Conference Information ****************************************** The 1994 IEEE World Congress on Computational Intelligence consists of three IEEE International Conferences: The Third IEEE International Conference on Fuzzy Systems, IEEE International Conference on Neural Networks, and The IEEE Conference on Evolutionary Computation. Over 1600 refereed and invited papers will be presented in these Conferences as well as to a special five day Symposium entitled "Computational Intelligence: Imitating Life." This Symposium will be held Monday, June 27 through Friday, July 1, 10:20AM to 12:20PM. The Congress Inaugural will be held Tuesday, June 28, 6:30PM to 7:15PM. Special Plenary Symposium COMPUTATIONAL INTELLIGENCE: IMITATING LIFE June 27 - July 1, 1994 For the first time in one meeting, the main threads of the topics in computational intelligence are woven into a single cohesive fabric. The Symposium addresses the exciting emerging technologies and issues relating to biologically, psychologically and linguistically motivated models that exhibit various facets of computational intelligence. Machine learning from data, neuro-fuzzy information processing, approximate reasoning, vision/qualitory modeling and evolutionary computation, are examples of computational intelligence approaches addressed by Symposium speakers. The Symposium provides a unique forum for cross- fertilization between the areas of neural networks, fuzzy logic, and evolutionary computation. The Symposium consists of three public lectures, 10 plenary talks and 30 mini-symposia presentations; covering neural networks (21), fuzzy logic (13), and evolutionary computation (9). Contributions include research that has implications for further progress in the field, state-of- the-art reviews, followed by discussions of important applications in fields such as robotics and control, image processing, vision, and biology. The presentations will be highly focused but still tutorial. Symposium speakers represent the top internationl researchers and practioners of this cutting edge technology. "Computational Intelligence: Imitating Life" companion volume to this Symposium will be complimentary to each registered participant of the Congress. For more information, contact Symposium Chair: Dr. Jacek Zurada University of Louisville Louisville, KY 40292, USA PHONE: (502)852-6314, FAX: (502)852-6807 EMAIL: jmzura02@ulkyvx.louisville.edu ****************************************** 2. CONFERENCE REGISTRATION FORM ****************************************** Indicate Conference Selection (you may attend sessions from all three conferences) ( ) FUZZY ( ) NEURAL NETWORKS ( ) EVOLUTIONARY COMPUTATION NAME Last Name ________________________________ First Name ________________________________ Middle Initial ____________________________ _____________________ IEEE Membership Number (needed to qualify for IEEE Member discount) MAILING ADDRESS City _________________________________________ State and ZIP (USA only)______________________ Country ______________________________________ Phone ________________________________________ FAX __________________________________________ e-mail _______________________________________ Information to appear on Badge: Title (Circle One) Ms Dr Prof no title other____________________ Full Name ______________________________________ Affiliation______________________________________ City/State/Country ______________________________ CONFERENCE REGISTRATION FEES: before April 15, 1994 after April 15, 1994 IEEE Members $350.00 $425.00 Non-Members $420.00 $495.00 Students* $ 90.00 $150.00 * A letter from the Department Head to verify full-time student status at the time of registration is required. At the conference, all students must present a current student ID with picture. Student registration does not include social functions. Full Conference Registration permits attendance at Congress functions, the Symposium, technical sessions of all three conferences, and the individual reception of the conference selected. Your registration fee includes the Proceedings (for the conference selected: FUZZ-IEEE'94, ICNN '94 or ICEC '94) and the Symposium Proceedings. A complete set of Proceedings for all three conferences is available for an additional $105. *Please note that Proceedings will be available after the conference from IEEE, although the price for each will increase at that time. 3. TUTORIAL REGISTRATION Before April 15, 1994 After April 15, 1994 Regular Student Regular Student One Tutorial $225 $125 $300 $150 Two Tutorials $350 $200 $450 $225 Three Tutorials $475 $275 $600 $300 Four Tutorials $650 $350 $750 $375 Each additional $125 $ 75 $150 $75 Tutorial Selection (Circle desired tutorials) 1A 1B 2 3A 3B 4A 4B 5A 5B 6 7A 7B 8 9A 9B 10 11 12 13 14 15 16 17 18A 18B Alternate Tutorial(s) 1A 1B 2 3A 3B 4A 4B 5A 5B 6 7A 7B 8 9A 9B 10 11 12 13 14 15 16 17 18A 18B Payment(s): Registration Fees U.S. $____________ Tutorial Fees U.S. $____________ All Three Proceedings ($105) U.S. $____________ Grand Total U.S. $____________ ****************************************** 4. Methods of Payment: ****************************************** $ CHECK. All check payments made outside of the USA must be made on a USA bank in US dollars. Please make check payable to WCCI '94 $ CREDIT CARDS. Only VISA, MC and Amex accepted. Payment may be through e-mail. Registrations submitted by fax or surface mail must include an authorized signature. ( ) Visa ( ) M/C ( ) Amex Name on Credit Card ______________________________________ Credit Card Number _______________________________________ Exp. Date ________________________________________________ Authorized Signature _____________________________________ All Conference (other than hotel) Registration material is to be sent to WCCI '94 Conference Office Meeting Management 2603 Main Street, Suite 690 Irvine, CA 92714 USA Tel. (714) 752-8205 Fax (714) 752-7444 e-mail: 74710.2266@COMPUSERVE.COM 5. HOTEL RESERVATION FORM Reservation Payment may be made by Check or Credit Card. Mail this form and payments to: Walt Disney Sheraton World Dolphin Attention: Reservations Department 1500 EPCOT Resort Blvd. Lake Buena Vista, FL 32830 Please make checks payable to: Walt Disney World Dolphin Check Desired Accommodations: Single $145 _____ Double $145 _____ Non-Smoking _____ If requested bedding is not available, alternate bedding will be assigned NOTE: The standard rates during this time period start at $255.00 per night, the conference rates offer a substantial savings. Arrival Date: __________ Departure Date: __________ Check-in Time: 3:00 pm Check-out Time 11:00 am Name ______________________________________________________ Mailing Address ____________________________________________ City/State/Country/ZIP _____________________________________ ___________________________________________________ ___________________________________________________ Phone ___________________________________ FAX ___________________________________ Sharing Room With (if applicable) __________________________ Sheraton Club International Number (if applicable)__________________________ Deadline Date: May 27, 1994 Please reserve before May 27, 1994, after this date, rooms are subject to availability. Please circle credit card type: Visa M/C Amex DC CB ER DI JCB Credit Card Number _____________________________________ Exp. Date ______________________________________________ Name on Credit Card ____________________________________ Authorized Signature ___________________________________ All reservations require a one night's deposit. Failure to cancel your reservation 5 days prior to arrival will result in forfeit of deposit. Group rates can only be confirmed by using this reservation form or calling the hotel directly. For any questions or further information regarding your request , please call our Reservations Office (toll free from the U.S.) at (800) 227- 1500, Fax # (407) 934-4710, or contact the hotel directly at (407) 934-4000. To avoid duplication, please do not mail this form if you make your reservation by telephone or telefax. ****************************************** 6. Spouse Activities ****************************************** * Tee times on three nearby WALT DISNEY WORLD Championshiop Golf Courses. * The Walt Disney World Dolphin connects by waterways and walkways to EPCOT Center and the Disney-MGM Studios Theme Park. Convenient complimentary Disney-operated transportation ties directly to the MAGIC KINGDOM Park, Pleasure Island, Typhoon Lagoon, the Disney Village Marketplace, 3 nearby championship golf courses and other areas of the Vacation Kingdom. The Dolphin offers the following hotel amenities. * Two acre swimming area with three pools, including a themed grotto area with slide, waterfalls and whirlpool area, lap pool plus lakeside white sand beach with special activities and watercraft rental. * Camp Dolphin, offering a wide range of youth activities * Eight night-lit, hard tennis courts * One-on-One personal fitness training under the guidance of "Body by Jake", with sauna, whirlpool, weight room, and exercise equipment ****************************************** 7. TUTORIAL TITLES ****************************************** For the first time, the World Congress joins three IEEE conferences on neural networks, fuzzy systems, and evolutionary computation in a single comprehensive forum. The forum presents two days of tutorials designed to provide information and help attendees keep pace with developments in paradigms that are guiding the development of models for computational intelligence. WCCI tutorials will be held on Sunday, June 26, 1994 and Wednesday, June 29, 1994. The WCCI Organizing Committee reserves the right to cancel tutorials and refund payment should registration not meet the minimum number of persons per course. SUNDAY JUNE 26, 1994 #1A Evolution Strategies: A Thorough Introduction Professor Thomas Beack #2 Genetic Algorithms and Their Applications Dr. Lawrence "David" Davis #3A An Introduction to Evolutionary Computation Dr. David B. Fogel #4A Genetic Programming Dr. John R. Koza #5A Genetics-Based Machine Learning in Rule-Based and Neural Systems Professor Robert E. Smith #7A An Introduction to Fuzzy Logic Professor James Bezdek #9A Fuzzy Logic Applications to Artificial Intelligence and Intelligent Control Systems Dr. Enrique H. Ruspini #10 Fuzzy Logic in Computer Vision Professor James M. Keller #11 Fuzzy Neurocomputations Professor Witold Pedrycz #12 Fuzzy Data Analysis Professor Dr. Dr.h.c. Hans-Jurgen Zimmermann #18A Learning Algorithms In Neural Networks Professor Jacek M. Zurada WEDNESDAY JUNE 29, 1994 #1B Evolution Strategies: A Thorough Introduction Professor Thomas Beack #3B An Introduction to Evolutionary Computation Dr. David B. Fogel #4B Genetic Programming Dr. John R. Koza #5B Genetics-Based Machine Learning in Rule-Based and Neural Systems Professor Robert E. Smith #6 Genetic Algorithms: Theoretical Foundations and Experimental Evaluation Professor Darrell Whitley #7B An Introduction to Fuzzy Logic Professor James Bezdek #8 Fuzzy Sets in Constraint Satisfaction Dr. Didier Dubois #9B Fuzzy Logic Applications to Artificial Intelligence and Intelligent Control Systems Dr. Enrique Ruspini #13 Applications of Neural Networks to Virtual Reality Professor Thomas P. Caudell #14 Hybrid Systems: Neural, Symbolic, and Fuzzy Professor Lawrence O. Hall and Professor Abraham Kandel #15 Basics of Building Market Timing Systems: Making Money with Neural Networks Casimir C. Klimasauskas #16 Practical Applications of Neural Network Theory Dr. Robert Hecht-Nielsen #17 Computational Studies of Biological Neural Networks: Introduction and Applications to Vision and Sensory-Motor Control Professor Paolo Gaudiano #18B Learning Algorithms in Neural Networks Professor Jacek M. Zurada ****************************************** 8. TUTORIAL ABSTRACTS ****************************************** #1 Evolution Strategies: A Thorough Introduction Professor Thomas Beack 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 1960's 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 following topics: 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. #2 Genetic Algorithms and Their Applications Dr. Lawrence "David" Davis Tica Associates Cambridge, MA 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. #3 An Introduction to Evolutionary Computation Dr. David B. Fogel Natural Selection, Inc. 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, and beginners in the field of evolutionary computation, will introduce methods of evolutionary computation. 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. #4 Genetic Programming Dr. John R. Koza Consulting Professor Computer Science Department, Stanford University, Palo Alto CA 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. #5 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, AL 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. #6 Genetic Algorithms: Theoretical Foundations and Experimental Evaluation Professor Darrell Whitley Computer Science Department Colorado State University, Fort Collins, CO 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. #7 An Introduction to Fuzzy Logic Professor James Bezdek Department of Computer Science University of West Florida, Pensacola, FL This tutorial begins by developing the basis for fuzzy models. The first hour starts with a discussion of uncertainty in models and its importance for system design. Membership functions and fuzzy set operations are defined. We pose and answer some basic questions about fuzzy models - e.g., where do they come from? how are they evaluated? how do they compare with probability models? The second hour presents two applications vignettes. The first considers stabilization of the simple inverted pendulum. We compare the classical (linear feedback) and fuzzy control approaches, and discuss design issues such as tuning and stability. The second application area is segmentation of image data. Several approaches based on fuzzy and neural models are presented and compared. #8 Fuzzy Sets in Constraint Satisfaction Dr. Didier Dubois Institut de Recherche en Informatique de Toulouse Universite Paul Sabatier, Toulouse Cedex - France Constraint-directed search is a very general and powerful methodology for problem solving, which is particularly adapted to finite domains involving high combinatorial complexity. The aim of this tutorial is to show that fuzzy set theory and constraint satisfaction can be easily and usefully put together. The tutorial will describe the approach pioneered by Bellman and Zadeh for the modeling of fuzzy constraints, and point out the difference between a fuzzy constraint and an objective function, address how to imbed flexible constraint satisfaction in Zadeh's calculus of fuzzy relations, whose aim is to propagate preference in constraint networks, and review in detail the applications of fuzzy constraint satisfaction in the field of production research, and especially job-shop scheduling. The fuzzy methodology will be compared to knowledge-based job-shop scheduling techniques that come from Artificial Intelligence. #9 Fuzzy Logic Applications to Artificial Intelligence and Intelligent Control Systems Dr. Enrique H. Ruspini Artificial Intelligence Center SRI International, Menlo Park, CA We present first fuzzy logic as a methodology concerned with the representation and analysis of vague and uncertain aspects of reality. Using a unified model of approximate-reasoning methods, we discuss the nature of fuzzy-logic methods and compare them with other uncertainty-modeling techniques such as probabilistic reasoning. Using this model, we also show that fuzzy logic is a sound deductive technique relying on the notions of utility and preference. Based on such a characterization, we present an emerging set of procedures for the development and analysis of fuzzy models. Problems such as the derivation of possibility distributions, their interpretation, the representation of vague knowledge, the integration of multiple conflicting objectives, and the explanation of planning and control choices are handled in this framework by means of sound procedures rooted on logical concepts and principles. We illustrate the nature of these techniques by means of examples of their application to the development of intelligent devices and systems. In particular, we focus on the architecture and operation of the motion controller for SRI's Autonomous Mobile Robot, Flakey. #10 Fuzzy Logic in Computer Vision Professor James M. Keller Electrical and Computer Engineering Department University of Missouri-Columbia, Columbia, MO Computer vision is the study of theories and algorithms for automating the process of visual perception. This involves tasks such as noise removal, smoothing, and sharpening of contrast; segmentation of images to isolate objects and regions and description and recognition of the segmented regions; and finally interpretation of the scene. The purpose of this tutorial is to give an overview of the fuzzy set theoretic approach to computer vision. The applications of fuzzy set theory in computer vision in the areas of image modeling, preprocessing, segmentation, boundary detection, object/region recognition, and reasoning will be discussed. Techniques presented are demonstrated on real imaging problems. #11 Fuzzy Neurocomputations Professor Witold Pedrycz Dept. of Electrical and Computer Eng. University of Manitoba, Winnipeg Fuzzy neurocomputations as realizing the paradigm of distributed computations integrate essential learning capabilities of neural networks with the schemes of explicit knowledge representation stemming from the mechanisms of fuzzy sets. This tutorial will address the issues of constructing, testing, and utilizing fuzzy neural networks. The cornerstone of fuzzy neural networks is that their processing elements (neurons) are constructed with the aid of logical operations available in the theory of fuzzy sets. Each neuron, as completing logical operations on the input stimuli, conveys its own clearly visible semantics. The two classes of neurons will be studied. The first category of the neurons embraces aggregation units, while the other one includes referential operations. The studies of the learning algorithms applied to the network will include both the modified gradient-like optimization methods as well as schemes of genetic optimization. Those latter can be stratified as they pertain equally well to the structure of the network, types of the neurons, and the character of the individual connections. Various applications of the networks will be also outlined including the utilization of the networks in designing fuzzy controllers. #12 Fuzzy Data Analysis Professor Dr. Dr.h.c.Hans-Jurgen Zimmermann Professor of Operations Research RWTH Aachen, Aachen, Germany This tutorial begins with definitions of basic terminology. Following this,we discuss methods and techniques for fuzzy data analysis. Tools discussedwill include algorithms and software for fuzzy clustering, decision models that use fuzzy inferencing techniques and approaches based on combinationsof neural networks and fuzzy models. The tutorial will illustrate thesetechniques by discussing applications that include quality control, imagesegmentation, fault diagnosis and petrochemical design. #13 Applications of Neural Networks to Virtual Reality Professor Thomas P. Caudell Dept. of Electrical Engineering and Computer Engineering University of New Mexico, Albuquerque, NM The objective of this tutorial is to first introduce the topic of virtual reality and then to show where neural networks are contributing to this technology. Virtual Reality (VR) is a form of advanced human-computer interface technology that embodies a sense of immersion, interactivity, navigation, and exploration of computer generated virtual worlds. A relative of VR is Augmented Reality (AR), where the user remains immersed in the real world with only small amounts of data being presented. VR typically involves opaque head-mounted displays that show only computer generated graphics. AR uses see-through head-mounted displays that show mostly the real world with small amounts of computer generated graphics overlaid on real world objects. There are many technological challenges left to solve before VR and AR are practical. Neural networks offer solutions to some of these challenges, This tutorial will introduce VR and AR technologies and applications, introduce the classes of neural networks to be discussed, and illustrate the application of neural networks to this field with examples. #14 Hybrid Systems: Neural, Symbolic, and Fuzzy Professor Lawrence O. Hall and Professor Abraham Kandel Computer Science and Engineering Department University of South Florida, Tampa, FL Neural networks and expert systems are complementary approaches to knowledge representation and decision making. This tutorial concentrates on hybrid systems which incorporate neural networks to tune expert system knowledge or will work in concert with an expert system to solve a problem. The basic concepts underlying hybrid systems are clearly outlined. The tutorial examines the question of how to incorporate knowledge into a neural network and whether symbolic information can be extracted from a trained neural network. The tutorial examines the use of fuzzy logic in the neural network expert system mix. This includes hybrid neuro fuzzy systems. Examples will be given that show hybrid systems, properly designed, provide systems more powerful than any of the components used in a stand-alone fashion. #15 Basics of Building Market Timing Systems: Making Money with Neural Networks Casimir C. Klimasauskas NeuralWare, Inc. Pittsburgh, PA This tutorial will cover the basic principles for building successful financial market timing systems. Are the markets predictable? This is the foundation on which this talk is built. Identifying which markets and when they are predictable is the first step toward developing a successful system. In general, the objective of developing a neural network trading system is to make money. Building a system which meets the objectives is the next step and primary focus of this tutorial. The technological measures on which most neural network technology is built often fail to maximize system objectives. Various approaches to addressing these issues will be discussed. This includes what to predict, how to modify standard neural paradigms to enhance ultimate performance, selection of train, test and verification sets, and data pre-processing. An example of a system developed on recent data will be used to illustrate the various issues in the talk. #16 Practical Applications of Neural Network Theory Dr. Robert Hecht-Nielsen HNC, Inc San Diego, CA Neural network theory has advanced significantly over the past five years. In this tutorial, theoretical advances in the areas of universal approximation, learning and convergence, curse of dimensionality exorcism, and error problem-solving will then be described, with an emphasis on how our practical efforts can be guided by this theoretical knowledge. Special emphasis will be given to the topic of which types of problems neural networks are good at solving and how to select the proper neural network architecture for a problem. The tutorial is aimed at those with at least basic familiarity with neural network architectures and applications. No knowledge of theory is presumed and no mathematics beyond elementary calculus andlinear algebra will be used. #17 Computational Studies of Biological Neural Networks: Introduction and Applications to Vision and Sensory-Motor Control Professor Paolo Gaudiano Department of Cognitive and Neural Systems Boston University, Boston, MA This tutorial introduces an interdisciplinary approach to the study of computational neural models for uncovering the functional designs that underlie human and animal learning and performance. Through a combination of psychological, physiological, mathematical and computational notions, the presentation will show how simple networks of neurons can develop useful functional properties in response to a rapidly changing and unpredictable environment. Next, the presentation will illustrate how these fundamental neural network modules can be embedded into more elaborate networks that exhibit complex adaptive behavior,. It will then be shown that the same fundamental modules serve as building blocks for other neural network models that can explain biological function and at the same time provide novel technologies for practical applications. The presentation will focus on two examples: one model of low level vision explains how the vertebrate retina rapidly adjusts its sensitivity over an enormous range of illumination, a useful property for artificial vision systems; the other model describes adaptive sensory-motor control in humans and animals, and has been applied successfully to visually-guided navigation of mobile robots. #18 Learning Algorithms In Neural Networks Professor Jacek M. Zurada Computer Science and Engineering University of Louisville, Louisville, KY Learning is a fundamental property of networks acquiring computational intelligence. Learning can be understood as a change in behavior brought about by experience. In neural networks learning takes the form of approximation of relationships from data, or the form of encoding desired equilibria. This tutorial reviews basic concepts of supervised and unsupervised learning of most important neural network architectures. The tutorial stresses the visualization of learning in both pattern and weight space. It demonstrates links between various methods of network adaptation schemes. The material presented is addressed to persons interested in pursuing independent research/study/NN modeling who are also seeking understanding of concepts underlying computational properties of neural networks. ************** 9. EXHIBIT INFORMATION ************* ------------------- For Exhibit Kit and further information call Babs Kobersteen, SPIE Exhibit Coordinator, at 206/676-3290, email wcci@mom.spie.org, mail: WCCI c/o SPIE Exhibits, PO Box 10, Bellingham, WA 98227-0010 Available exhibit spaces include: OPEN-SPACE TABLES ($750.00) are 2' x 6' x 30" high, or the equivalent floorspace includes draped table, two chairs, carpet, and company sign. The maximum height of the display from table surface is 4'. Total depth and width allowed is 5'x6' respectively. If your display exceeds these limits, please choose a booth area. No utilities included. BOOTH AREAS ($1400.00) include 10' x 10' display will be set with pipe and drape, carpeting, and company sign. Electrical, telephone, utilities, and other furnishings are not included and must be ordered separately. UNIVERSITY TABLES ($200.00) - FOR UNIVERSITIES ONLY An open space table provided to universities to display information about departments involved in comutational intelligence. Tables may also be used to display research. Electrical, telephone, utilities, and other furnishings are not included and must be ordered separately.