From owner-gann-list Thu May 2 05:40:19 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id FAA20094 for gann-list-outgoing; Thu, 2 May 1996 05:08:32 -0500 (CDT) From: ludwig@ibm18.uni-paderborn.de (Lars Alex. Ludwig) Message-Id: <9605021005.AA15086@ibm18.uni-paderborn.de> Subject: GANN: Call for Papers: Fuzzy-Neuro Systems '97 To: gann-list@cs.iastate.edu Date: Thu, 2 May 1996 12:05:54 +0100 (DFT) X-Mailer: ELM [version 2.4 PL20] Content-Type: text Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: ludwig@ibm18.uni-paderborn.de (Lars Alex. Ludwig) =============================================================================== FFFFF N N SSSSS || 99999 77777 Fuzzy-Neuro-Systeme '97 F NN N S 9 9 7 - Computational Intelligence - FFF N N N SSSSS 99999 7 4. Internationaler Workshop in Soest F N NN S 9 7 http://www.uni-paderborn.de/~fns97/ F N N SSSSS 99999 7 E-Mail: fns97@uni-paderborn.de =============================================================================== CALL FOR PAPERS Fuzzy-Neuro Systems '97 - Computational Intelligence - 4th International Workshop 12 to 14 March 1994 University of Paderborn, Germany Fuzzy-Neuro Systems '97 is the fourth event of a well established series of workshops with international participation. Its aim is to give an overview of the state of the art in research and development of fuzzy systems and artificial neural networks. Another aim is to highlight applications of these methods and to forge innovative links between theory and application by means of creative discussions. Fuzzy-Neuro Systems '97 will add evolutionary algorithms to the areas of fuzzy logic and neural networks in order to show current trends in soft computing and computational intelligence comprehensively. Interested parties are especially encouraged to hand in contributions of hybrid systems that combine advantages of various methods efficiently. Organizer of this workshop is Research Committee 1.2 "Inference Systems" (Fachausschuss 1.2 "Inferenzsysteme") of German Society of Computer Science (Gesellschaft fuer Informatik e. V. (GI)) supported by Research Center "Sensors/Actuators" (Forschungsschwerpunkt "Sensorik/Aktorik") of Northrhine- Westfalia (Nordrhein-Westfalen) at University of Paderborn, Department Soest. Preceding workshops were held in Braunschweig (1993), Munich (1994) and Darmstadt (1995). Invited plenary speakers were Prof. D. Dubois (Toulouse), Prof. J. A. Feldman (Berkeley), Dr. H. Hellendoorn (Munich), Prof. L. Koczy (Tokyo, Budapest), Prof. R. Kruse (Braunschweig), Prof. E. H. Mamdani (London), Prof. L. A. Zadeh (Berkeley) and Prof. H.-J. Zimmermann (Aachen). Invited plenary speakers for this workshop will be Prof. J. Bezdek (Pensacola), Prof. E. P. Klement (Linz), Prof. T. Kohonen (Helsinki), Prof. W. Pedrycz (Manitoba) and Prof. H.-P. Schwefel (Dortmund). Conference languages will be German and English. Further information is available in World Wide Web under http://www.uni-paderborn.de/~fns97/ Scientific Topics ----------------- - theory and principles of multivariate and fuzzy logic - representation modes of fuzzy knowledge - approximate reasoning - fuzzy control: theory and practice - fuzzy logic: data analysis, signal processing and pattern recognition - fuzzy classification systems - fuzzy decision support systems - fuzzy logic in non-technical areas: business administration, management etc. - fuzzy databases - theorie and principles of artificial neural networks - hybrid learning algorithms - neural networks: pattern recognition, classification, process monitoring and production control - theory and principles of evolutionary algorithms: genetic algorithms and evolution strategies - discrete parameter and structural optimization - hybrid systems: neuro-fuzzy systems, connectionistic expert systems etc. - special hardware and software Program Committee ----------------- Prof. Dr. W. Becker, University of Paderborn, Department Soest Prof. Dr. F. Belli, University of Paderborn Prof. Dr. W. Bibel, Technical University of Darmstadt Prof. Dr. W. Brauer, Technical University of Munich Prof. Dr. C. Freksa, University of Hamburg Prof. Dr. M. Glesner, Technical University of Darmstadt Prof. Dr. S. Gottwald, University of Leipzig Prof. Dr. A. Grauel, University of Paderborn, Department Soest (Chairman) Dr. H. Hellendoorn, Siemens AG, Munich Prof. Dr. R. Isermann, Technical University of Darmstadt Prof. Dr. P. Klement, University of Linz Prof. Dr. R. Kruse, Technical University of Braunschweig Dr. R. Palm, Siemens AG, Munich Prof. Dr. B. Reusch, University of Dortmund Prof. Dr. W. von Seelen, University of Bochum Prof. Dr. H. Tolle, Technical University of Darmstadt Prof. Dr. W. Wahlster, University of Saarbruecken Prof. Dr. H.-J. Zimmermann, Technical University of Aachen Submission of Contributions --------------------------- Please pay attention to the following deadlines for submission of contributions: 30/08/1996: abridged version (German or English, 3 to 4 pages DIN A4 size) of following structure: - title - author(s) - address - phone - fax - e-mail Contents: 1. abstract 2. key words (not more than 5) 3. state of the art 4. new aspects 5. theory, simulation or experiment 6. results and conclusion 7. references October 96: notification of acceptance or rejection of contribution 29/11/1996: final camera-ready papers for proceedings (up to 8 pages DIN A4) Please send your scientific contribution in four copies to: Prof. Dr. Adolf Grauel Universitaet-Gesamthochschule Paderborn Abteilung Soest, Fachbereich 16 Fachgebiet Mathematische Methoden und Systemtheorie Steingraben 21 D-59494 Soest GERMANY WWW: http://www.uni-paderborn.de/~fns97/ E-mail: fns97@uni-paderborn.de Workshop Fees ------------- Workshop fees are: industry rate DM 600,- university rate DM 450,- rate for GI members or speakers DM 400,- rate for students without income DM 100,- (excluding proceedings and banquet) A surcharge of DM 100,- is payable for registration after 15/2/1996. Services of Gesellschaft fuer Informatik e. V. (GI) are tax-free according to German law § 4 Nr. 22a UStG. The fee includes proceedings, drinks during breaks as well as DM 45,- for attending the banquet. DM 45,- incl. VAT paid for the banquet will be directly remitted to the account of a local caterer. Registration ------------ Please send the completed application form to: DLGI Dienstleistungsgesellschaft fuer Informatik mbH Frau Gabriele Trapp Ahrstrasse 45 D-53175 Bonn GERMANY Phone: ++ 49 / 2 28 / 30 21 64 Fax: ++ 49 / 2 28 / 37 86 90 Application Form ---------------- I am interested in (please tick) O submitting a contribution; O attending the workshop. Last name:__________________________________________________________________ First name:_________________________________________________________________ Title:______________________________________________________________________ Affiliation:________________________________________________________________ Address:____________________________________________________________________ ____________________________________________________________________________ ____________________________________________________________________________ Phone:______________________________________________________________________ Fax:________________________________________________________________________ E-mail:_____________________________________________________________________ GI member number:___________________________________________________________ Place/Date:_________________________________________________________________ Signature:__________________________________________________________________ From owner-gann-list Thu May 2 10:00:41 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id JAA00769 for gann-list-outgoing; Thu, 2 May 1996 09:54:10 -0500 (CDT) Date: Thu, 2 May 96 15:42:19 BST From: Noel Sharkey Message-Id: <9605021442.AA19951@dcs.shef.ac.uk> To: alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, colt@cs.uiuc.edu, connectionists@CS.CMU.EDU, gann-list@cs.iastate.edu, hybrid-list@cs.ua.edu, intcon@phoenix.ee.unsw.edu.au, reinforce@cs.uwa.edu.au, mscb18%teach@dcs.shef.ac.uk, mscb40%teach@dcs.shef.ac.uk, mscb0%teach@dcs.shef.ac.uk Subject: GANN: 1st CALL FOR PAPERS Cc: A.Timmer@dcs.shef.ac.uk Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: Noel Sharkey ****** ROBOT LEARNING: THE NEW WAVE ****** Special Issue of the journal Robotics and Autonomous Systems Submission Deadline: August, 1st, 1996 Decisions to authors: October, 1st, 1996 Final papers back: November, 7th, 1996 SPECIAL EDITOR Noel Sharkey (Sheffield) SPECIAL EDITORIAL BOARD Michael Arbib (USC) Ronald Arkin (GIT) George Bekey (USC) Randall Beer (Case Western) Bartlett Mel (USC) Maja Mataric (Brandeis) Carme Torras (Barcelona) Lina Massone (Northwestern) Lisa Meeden (Swarthmore) INTERNATIONAL REVIEW PANEL S Perkins (UK) T Ziemke (Sweden) P Zhang (France) S Wilson (USA) P Bakker (Japan) J Tani (Japan) C Thornton (UK) M Wilson (UK) M Recce (UK) D Cliff (UK) G Hayes (UK) U Zimmer (Germany) S Thrun (USA) S Nolfi (Italy) P van der Smagt (Germany) C Touzet (France) U Nehmzow (UK) R Salmon (Switzerland) J Hallam (UK) M Nilsson (Sweden) M Dorigo (Belgium) A Prescott (UK) C Holgate (UK) E Celaya (Spain) P Husbands (UK) I Harvey (UK) The objective of the Special Issue is to provide a focus for the new wave of research on the use of learning techniques to train real robots. We are particularly interested in research using neural computing techniques, but would also like submissions of work using genetic algorithms or other novel techniques. The nature of the new wave research is transdisciplinary bringing on board control engineering, artificial intelligence, animal learning, neurophysiology, embodied cognition, and ethology. We would like to encourage work discussing replicability and quantification provided that the research has been conducted or tested on real robots. AREAS OF RESEARCH INCLUDE: Mobile autonomous robotics, Fixed Arm robotics, Dextrous robots, Walking Machines, High level robotics, Behaviour-based robotics, Biologically inspired robots. TOPICS OF INTEREST INCLUDE * Reinforcement learning * Supervised learning * Self organisiation * Genetic algorithms * Learning brainstyle control systems * High level robot learning * Hybrid learning * Imitation Learning * The learning and use of representations * Adaptive approaches to dynamic planning * Place recognition Send submissions to Ms Jill Martin, RAS Special, Department of Computer Science, Regent Court, Portobello Rd., University of Sheffield, Sheffield, S1 4DP, UK. Updates will appear on the web page: http:\www.dcs.shef.ac.uk/research/groups/nn/RASspecial.html From owner-gann-list Sat May 4 10:44:16 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id KAA18016 for gann-list-outgoing; Sat, 4 May 1996 10:20:39 -0500 (CDT) Date: Sat, 4 May 1996 08:20:11 -0700 (PDT) From: "John R. Koza" Posted-Date: Sat, 4 May 1996 08:20:11 -0700 (PDT) Message-Id: <199605041520.IAA08340@Sunburn.Stanford.EDU> To: gann-list@cs.iastate.edu Subject: GANN: GP-96 Registration and Papers Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: "John R. Koza" CALL FOR PARTICIPATION, LIST OF TUTORIALS, LIST OF PAPERS, LIST OF PROGRAM COMMITTEES, AND REGISTRATION FORM (Largest discount availabe until May 15) Genetic Programming 1996 Conference (GP-96) July 28 - 31 (Sunday - Wednesday), 1996 Fairchild Auditorium and other campus locations Stanford University Stanford, California Proceedings will be published by The MIT Press In cooperation with -the Association for Computing Machinery (ACM), - SIGART - IEEE Neural Network Council, - American Association for Artificial Intelligence. Genetic programming is an automatic programming technique for evolving computer programs that solve (or approximately solve) problems. Starting with a primordial ooze of thousands of randomly created computer programs composed of programmatic ingredients appropriate to the problem, a population of computers programs is progressively evolved over many generations using the Darwinian principle of survival of the fittest, a sexual recombination operation, and occasional mutation. Since 1992, over 500 technical papers have been published in this rapidly growing field. This first genetic programming conference will feature 75 papers and 27 poster papers, 12 tutorials, 2 invited speakers, a session featuring late-breaking papers, and informal birds-of-a-feather meetings. Topics include, but are not limited to, applications of genetic programming, theoretical foundations of genetic programming, implementation issues and technique extensions, use of memory and state, cellular encoding (developmental genetic programming), evolvable hardware, evolvable machine language programs, automated evolution of program architecture, evolution and use of mental models, automatic programming of multi-agent strategies, distributed artificial intelligence, automated circuit synthesis, automatic programming of cellular automata, induction, system identification, control, automated design, compression, image analysis, pattern recognition, molecular biology applications, grammar induction, and parallelization. ------------------------------------------------- HONORARY CHAIR: John Holland, University of Michigan INVITED SPEAKERS: John Holland, University of Michigan and David E. Goldberg, University of Illinois GENERAL CHAIR: John Koza, Stanford University PUBLICITY CHAIR: Patrick Tufts, Brandeis University ------------------------------------------------- TUTORIALS -Sunday July 28 9:15 AM - 11:30 AM - Genetic Algorithms - David E. Goldberg, University of Illinois - Machine Language Genetic Programming - Peter Nordin, University of Dortmund, Germany - Genetic Programming using Mathematica P Robert Nachbar P Merck Research Laboratories - Introduction to Genetic Programming - John Koza, Stanford University ------------------------------------------------- Sunday July 28 1:00 PM - 3: 15 PM - Classifier Systems- Robert Elliott Smith, University of Alabama - Evolutionary Computation for Constraint Optimization - Zbigniew Michalewicz, University of North Carolina - Advanced Genetic Programming - John Koza, Stanford University ------------------------------------------------- Sunday July 28 3:45 PM - 6 PM - Evolutionary Programming and Evolution Strategies - David Fogel, University of California, San Diego - Cellular Encoding P Frederic Gruau, Stanford University (via videotape) and David Andre, Stanford University (in person) - Genetic Programming with Linear Genomes (one hour) - Wolfgang Banzhaf, University of Dortmund, Germany -JECHO - Terry Jones, Santa Fe Institute ------------------------------------------------- Tuesday July 30 - 3 PM - 5:15PM - Neural Networks - David E. Rumelhart, Stanford University - Machine Learning - Pat Langley, Stanford University -JMolecular Biology for Computer Scientists - Russ B. Altman, Stanford University ------------------------------------------------- Additional tutorial P Time to be Announced % Evolvable Hardware - Hugo De Garis,ATR, Nara, Japan and Adrian Thompson, University of Sussex, U.K. ------------------------------------------------- FOR MORE INFORMATION ABOUT THE GP-96 CONFERENCE: See the GP-96 home page on the World Wide Web: http://www.cs.brandeis.edu/~zippy/gp-96.html or contact GP-96 at via e-mail at gp@aaai.org. PHONE: 415-328- 3123. FAX: 415-321-4457. Conference operated by Genetic Programming Conferences, Inc. (a California not- for-profit corporation). ABOUT GENETIC PROGRAMMING IN GENERAL: http://www-cs- faculty.stanford.edu/~koza/. FOR GP-96 TRAVEL INFORMATION: See the GP-96 home page on the World Wide Web: http://www.cs.brandeis.edu/~zippy/gp-96.html. For further information regarding special GP-96 airline and car rental rates, please contact Conventions in America at e-mail flycia@balboa.com; or phone 1-800-929-4242; or phone 619-678-3600; or FAX 619-678-3699. FOR HOTEL AND UNIVERSITY HOUSING INFORMATION: See the GP-96 home page on the World Wide Web: http://www.cs.brandeis.edu/~zippy/gp-96.html or via e- mail at gp@aaai.org. FOR STUDENT TRAVEL GRANTS: See the GP-96 home page on the World Wide Web: http://www.cs.brandeis.edu/~zippy/gp-96.html. ABOUT THE SAN FRANCISCO BAY AREA AND SILICON VALLEY SIGHTS: Try the Stanford University home page at http://www.stanford.edu/, the Hyperion Guide at http://www.hyperion.com/ba/sfbay.html; the Palo Alto weekly at http://www.service.com/PAW/home.html; the California Virtual Tourist at http://www.research.digital.com/SRC/virtual- tourist/California.html; and the Yahoo Guide of San Francisco at http://www.yahoo.com/Regional_Information/States/Califor nia/San_Francisco. ABOUT OTHER CONTEMPORANEOUS WEST COAST CONFERENCES: Information about the AAAI-96 conference on August 4 P 8 (Sunday P Thursday), 1996, in Portland, Oregon is at http://www.aaai.org/. Information on the International Conference on Knowledge Discovery and Data Mining (KDD- 96) in Portland on August 3 P 5, 1996 is at http://www- aig.jpl.nasa.gov/kdd96. Information about the Protein Society conference on August 3 P 7, 1996 in San Jose is at http://www.faseb.org. Information about the Foundations of Genetic Algorithms (FOGA) workshop on August 3 P 5 (Saturday P Monday), 1996, in San Diego is at http://www.aic.nrl.navy.mil/galist/foga/. Information about the Parallel and Distributed Processing Techniques and Applications (PDPTA-96) conference on August 6 P 9 (Friday P Sunday), 1996 in Sunnyvale, California is at http://www.ece.neu.edu/pdpta96.html. ABOUT MEMBERSHIP IN THE ACM, AAAI, or IEEE: For information about ACM membership, try http://www.acm.org/; for information about SIGART, try http://sigart.acm.org/; for AAAI membership, go to http://www.aaai.org/; and for membership in the IEEE, go to http://www.ieee.org. PHYSICAL MAIL ADDRESS FOR GP-96: GP-96 Conference, c/o American Association for Artificial Intelligence, 445 Burgess Drive, Menlo Park, CA 94025. PHONE: 415-328- 3123. FAX: 415-321-4457. WWW: http://www.aaai.org/. E-MAIL: gp@aaai.org. ------------------------------------------------ REGISTRATION FORM FOR GENETIC PROGRAMMING 1996 CONFERENCE TO BE HELD ON JULY 28 P 31, 1996 AT STANFORD UNIVERSITY First Name _________________________ Last Name_______________ Affiliation________________________________ Address__________________________________ ________________________________________ City__________________________ State/Province _________________ Zip/Postal Code____________________ Country__________________ Daytime telephone__________________________ E-Mail address_____________________________ Conference registration fee includes copy of proceedings, attendance at 4 tutorials of your choice, syllabus books for the tutorials, conference reception, copy of a book of late-breaking papers, a T-shirt, coffee breaks, lunch (on at least Sunday), and admission to conference sessions. Students must send legible proof of full-time student status. Conference proceedings will be mailed to registered attendees with U.S. mailing addresses via 2-day U.S. priority mail about 1 P 2 weeks prior to the conference at no extra charge (at addressee's risk). If you are uncertain as to whether you will be at that address at that time or DO NOT WANT YOUR PROCEEDINGS MAILED to you at the above address for any other reason, your copy of the proceedings will be held for you at the conference registration desk if you CHECK HERE ____. Postmarked by May 15, 1996: Student P ACM, IEEE, or AAAI Member $195 Regular P ACM, IEEE, or AAAI Member $395 Student P Non-member $215 Regular P Non-member $415 Postmarked by June 26, 1996: Student P ACM, IEEE, or AAAI Member $245 Regular P ACM, IEEE, or AAAI Member $445 Student P Non-member $265 Regular P Non-member $465 Postmarked later or on-site: Student P ACM, IEEE, or AAAI Member $295 Regular P ACM, IEEE, or AAAI Member $495 Student P Non-member $315 Regular P Non-member $515 Member number: ACM # ___________ IEEE # _________ AAAI # _________ Total fee (enter appropriate amount) $ _________ __ Check or money order made payable to "AAAI" (in U.S. funds) __ Mastercard __ Visa __ American Express Credit card number __________________________________________ Expiration Date ___________ Signature _________________________ TUTORIALS: Check off a box for one tutorial from each of the 4 columns: Sunday July 28, 1996 P 9:15 AM - 11:30 AM __ Genetic Algorithms __ Machine Language GP __ GP using Mathematica __ Introductory GP Sunday July 28, 1996 P 1:00 PM - 3: 15 PM __ Classifier Systems __ EC for Constraint Optimization __ Advanced GP Sunday July 28, 1996 P 3:45 PM - 6 PM __ Evolutionary Programming and Evolution Strategies __ Cellular Encoding __ GP with Linear Genomes __ ECHO Tuesday July 30, 1996 P3:00 PM - 5:15PM __ Neural Networks __ Machine Learning __ Molecular Biology for Computer Scientists __ Check here for information about housing and meal package at Stanford University. __ Check here for information on student travel grants. T-shirt size ___ small ___ medium ___ large ___ extra-large No refunds will be made; however, we will transfer your registration to a person you designate upon notification. SEND TO: GP-96 Conference, c/o American Association for Artificial Intelligence, 445 Burgess Drive, Menlo Park, CA 94025. ------------------------------------------------- 90 PAPERS APPEARING IN PROCEEDINGS OF THE GP-96 CONFERENCE TO BE HELD AT STANFORD UNIVERSITY ON JULY 28-31, 1996 -------------------------------------------------- LONG GENETIC PROGRAMMING PAPERS Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem --- David Andre, Forrest H Bennett III, and John R. Koza A Study in Program Response and the Negative Effects of Introns in Genetic Programming --- David Andre and Astro Teller An Investigation into the Sensitivity of Genetic Programming to the Frequency of Leaf Selection During Subtree Crossover --- Peter J. Angeline Automatic Creation of an Efficient Multi-Agent Architecture Using Genetic Programming with Architecture-Altering Operations --- Forrest H Bennett III Evolving Deterministic Finite Automata Using Cellular Encoding --- Scott Brave Genetic Programming and the Efficient Market Hypothesis --- Shu-Heng Chen and Chia-Hsuan Yeh Bargaining by Artificial Agents in Two Coalition Games: A Study in Genetic Programming for Electronic Commerce --- Garett Dworman, Steven O. Kimbrough, and James D. Laing Waveform Recognition Using Genetic Programming: The Myoelectric Signal Recognition Problem --- Jaime J. Fernandez, Kristin A. Farry, and John B. Cheatham Benchmarking the Generalization Capabilities of A Compiling Genetic programming System using Sparse Data Sets --- Frank D. Francone, Peter Nordin, and Wolfgang Banzhaf A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks --- Frederic Gruau, Darrell Whitley, and Larry Pyeatt Entailment for Specification Refinement --- Thomas Haynes, Rose Gamble, Leslie Knight, and Roger Wainwright Genetic Programming of Near-Minimum-Time Spacecraft Attitude Maneuvers --- Brian Howley Evolving Evolution Programs: Genetic Programming and L-Systems --- Christian Jacob Genetic Programming using Genotype-Phenotype Mapping from Linear Genomes into Linear Phenotypes --- Robert E. Keller and Wolfgang Banzhaf Automated WYWIWYG Design of Both the Topology and Component Values of Electrical Circuits Using Genetic Programming --- John R. Koza, Forrest H Bennett III, David Andre, and Martin A. Keane Use of Automatically Defined Functions and Architecture-Altering Operations in Automated Circuit Synthesis Using Genetic Programming --- John R. Koza, David Andre, Forrest H Bennett III, and Martin A. Keane Using Data Structures within Genetic Programming --- W. B. Langdon Evolving Teamwork and Coordination with Genetic Programming --- Sean Luke and Lee Spector Using Genetic Programming to Develop Inferential Estimation Algorithms --- Ben McKay, Mark Willis, Gary Montague, and Geoffrey W. Barton Dynamics of Genetic Programming and Chaotic Time Series Prediction --- Brian S. Mulloy, Rick L. Riolo, and Robert S. Savit Genetic Programming, the Reflection of Chaos, and the Bootstrap: Towards a useful Test for Chaos --- E. Howard N. Oakley Solving Facility Layout Problems Using Genetic Programming --- Jaime Garces-Perez, Dale A. Schoenefeld, and Roger L. Wainwright Variations in Evolution of Subsumption Architectures Using Genetic Programming: The Wall Following Robot Revisited --- Steven J. Ross, Jason M. Daida, Chau M. Doan, Tommaso F. Bersano- Begey, and Jeffrey J. McClain MASSON: Discovering Commonalties in Collection of Objects using Genetic Programming --- Tae-Wan Ryu and Christoph F. Eick Cultural Transmission of Information in Genetic Programming --- Lee Spector and Sean Luke Code Growth in Genetic Programming --- Terence Soule, James A. Foster, and John Dickinson High-Performance, Parallel, Stack-Based Genetic Programming --- Kilian Stoffel and Lee Spector Search Bias, Language Bias, and Genetic Programming --- P. A. Whigham Learning Recursive Functions from Noisy Examples using Generic Genetic Programming --- Man Leung Wong and Kwong Sak Leung SHORT GENETIC PROGRAMMING PAPERS Classification using Cultural Co-Evolution and Genetic Programming --- Myriam Abramson and Lawrence Hunter Type-Constrained Genetic Programming for Rule-Base Definition in Fuzzy Logic Controllers --- Enrique Alba, Carlos Cotta, and Jose J. Troyo The Evolution of Memory and Mental Models Using Genetic Programming --- Scott Brave Automatic Generation of Object-Oriented Programs Using Genetic Programming --- Wilker Shane Bruce Evolving Event Driven Programs --- Mark Crosbie and Eugene H. Spafford Computer-Assisted Design of Image Classification Algorithms: Dynamic and Static Fitness Evaluations in a Scaffolded Genetic Programming Environment --- Jason M. Daida, Tommaso F. Bersano-Begey, Steven J. Ross, and John F. Vesecky Improved Direct Acyclic Graph Handling and the Combine Operator in Genetic Programming --- Herman Ehrenburg An Adverse Interaction between Crossover and Restricted Tree Depth in Genetic Programming --- Chris Gathercole and Peter Ross The Prediction of the Degree of Exposure to Solvent of Amino Acid Residues via Genetic Programming --- Simon Handle y A New Class of Function Sets for Solving Sequence Problems --- Simon Handley Evolving Edge Detectors with Genetic Programming --- Christopher Harris and Bernard Buxton Toward Simulated Evolution of Machine Language Iteration --- Lorenz Huelsbergen Robustness of Robot Programs Generated by Genetic Programming --- Takuya Ito, Hitoshi Iba, and Masayuki Kimura Signal Path Oriented Approach for Generation of Dynamic Process Models --- Peter Marenbach, Kurt D. Betterhausen, and Stephan Freyer Evolving Control Laws for a Network of Traffic Signals --- David J. Montana and Steven Czerwinski Distributed Genetic Programming: Empirical Study and Analysis --- Tatsuya Niwa and Hitoshi Iba Programmatic Compression of Images and Sound --- Peter Nordin and Wolfgang Banzhaf Investigating the Generality of Automatically Defined Functions --- Una-May O'Reilly Parallel Genetic Programming: An Application to Trading Models Evolution --- Mouloud Oussaidene, Bastien Chopard, Olivier V. Pictet, and Marco Tomassini Genetic Programming for Image Analysis --- Riccardo Poli Evolving Agents --- Adil Qureshi Genetic Programming for Improved Data Mining: An Application to the Biochemistry of Protein Interactions --- M. L. Raymer, W. F. Punch, E. D. Goodman, and L. A. Kuhn Generality Versus Size in Genetic Programming --- Justinian Rosca Genetic Programming in Database Query Optimization --- Michael Stillger and Myra Spiliopoulou Ontogenetic Programming --- Lee Spector and Kilian Stoffel Using Genetic Programming to Approximate Maximum Clique --- Terence Soule, James A. Foster, and John Dickinson Paragen: A Novel Technique for the Autoparallelisation of Sequential Programs using Genetic Programming --- Paul Walsh and Conor Ryan The Benefits of Computing with Introns --- Mark Wineberg and Franz Oppacher GENETIC PROGRAMMING POSTER PAPERS Co-Evolving Classification Programs using Genetic Programming --- Manu Ahluwalia and Terence C. Fogarty Genetic Programming Tools Available on the Web: A First Encounter --- Anthony G. Deakin and Derek F. Yates Speeding up Genetic Programming: A Parallel BSP Implementation --- Dimitris C. Dracopoulos and Simon Kent Easy Inverse Kinematics using Genetic Programming --- Jonathan Gibbs Noisy Wall-Following and Maze Navigation through Genetic Programming --- Andrew Goldfish Genetic Programming for Classification of Brain Tumours from Nuclear Magnetic Resonance Biopsy Spectra --- H. F. Gray, R. J. Maxwell, I. Martinez-Perez, C. Arus, and S. Cerdan GP-COM: A Distributed Component-Based Genetic Programming System in C++ --- Christopher Harris and Bernard Buxton Clique Detection via Genetic Programming --- Thomas Haynes and Dale Schoenefeld Functional Languages on Linear Chromosomes --- Paul Holmes and Peter J. Barclay Improving the Accuracy and Robustness of Genetic Programming through Expression Simplification --- Dale Hooper and Nicholas S. Flann COAST: An Approach to Robustness and Reusability in Genetic Programming --- Naohiro Hondo, Hitoshi Iba, and Yukinori Kakazu Recurrences with Fixed Base Cases in Genetic Programming --- Stefan J. Johansson Evolutionary and Incremental Methods to Solve Hard Learning Problems --- Ibrahim Kuscu Detection of Patterns in Radiographs using ANN Designed and Trained with the Genetic Algorithm --- Alejandro Pazos Julian Dorado and Antonino Santos The Logic-Grammars-Based Genetic Programming System --- Man Leung Wong and Kwong Sak Leung LONG GENETIC ALGORITHMS PAPERS Genetic Algorithms with Analytical Solution --- Erol Gelenbe Silicon Evolution --- Adrian Thompson SHORT GENETIC ALGORITHMS PAPERS On Sensor Evolution in Robotics --- Karthik Balakrishnan and Vasant Honavar Testing Software using Order-Based Genetic Algorithms --- Edward B. Boden and Gilford F. Martino Optimizing Local Area Networks Using Genetic Algorithms --- Andy Choi A Genetic Algorithm for the Construction of Small and Highly Testable OKFDD Circuits --- Rold Drechsler, Bernd Becker, and Nicole Gockel Motion Planning and Design of CAM Mechanisms by Means of a Genetic Algorithm --- Rodolfo Faglia and David Vetturi Evolving Strategies Based on the Nearest Neighbor Rule and a Genetic Algorithm --- Matthias Fuchs Recognition and Reconstruction of Visibility Graphs Using a Genetic Algorithm --- Marshall S. Veach GENETIC ALGORITHMS POSTER PAPERS The Use of Genetic Algorithms in the Optimization of Competitive Neural Networks which Resolve the Stuck Vectors Problem --- Tin Ilakovac, Zeljka Perkovic, and Strahil Ristov An Extraction Method of a Car License Plate using a Distributed Genetic Algorithm --- Dae Wook Kim, Sang Kyoon Kim, and Hang Joon Kim EVOLUTIONARY PROGRAMMING AND EVOLUTION STRATEGIES PAPERS Evolving Fractal Movies --- Peter J. Angeline Preliminary Experiments on Discriminating between Chaotic Signals --- David B. Fogel and Lawrence J. Fogel Discovering Patterns in Spatial Data using Evolutionary Programming --- Adam Ghozeil and David B. Fogel Evolving Reduced Parameter Bilinear Models for Time Series Prediction using Fast Evolutionary Programming --- Sathyanarayan S. Rao and Kumar Chellapilla CLASSIFIER SYSTEMS PAPERS Three-Dimensional Shape Optimization Utilizing a Learning Classifier System --- Robert A. Richards and Sheri D. Sheppard Classifier System Renaissance: New Analogies, New Directions --- H. Brown Cribbs III and Robert E. Smith Natural Niching for Cooperative Learning in Classifier Systems --- Jeffrey Horn and David E. Goldberg From owner-gann-list Thu May 16 09:05:49 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id IAA26267 for gann-list-outgoing; Thu, 16 May 1996 08:42:52 -0500 (CDT) Newsgroups: comp.ai,comp.ai.neural-nets,comp.theory.self-org-sys Date: Thu, 16 May 1996 16:32:57 +0300 (WET) From: Christos Schizas Cc: Christos Schizas , Frank Schnorrenberg Subject: GANN: CFP: Special Issue on Computational Intelligent Diagnostic Systems in Medicine Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: Christos Schizas Call for Papers Special Issue on Computational Intelligent Diagnostic Systems in Medicine in the Journal of Intelligent Systems http://http2.brunel.ac.uk:8080/~hs92jis/ As medicine becomes more specialised and complex, and the technology succeeds in offering more possibilities in gathering related medical data, doctors are faced with the challenge of processing vast amounts of data. The development of useful intelligent systems has always been a goal for scientists and engineers. Artificial Intelligence has been providing a shed to the above efforts for many years. However, artificial Intelligence is traditionally concerned with symbolic manipulation and rule-based systems. Although advanced practical systems have been constructed based on these traditional methods, the incentive to continue along these lines can be enhanced by the development of stronger links to human intelligence. It is anticipated that the proper connection to human intelligence will maintain the focus and inspiration for building even "smarter" systems. Computational Intelligence (CI) was introduced in this field for providing solutions or an alternative methodology, and it is claimed that one of the major research areas in CI is the modelling of human problem-solving and decision making. Some of the components of this field that are related to medicine are for example, the acquisition of medical knowledge, the problem-solving system, a decision making strategy by capturing the abilities of the expert doctors, a means of adding new knowledge and modifying previous knowledge, and the development of a friendly user interface with the system. The breadth of clinical knowledge is an impediment to the development of symbolic knowledge bases, that are comprehensive and flexible enough to cope with reality. It has been lately demonstrated that neural networks, genetic algorithms, and fuzzy systems can offer new hope for allowing computers to assist in the challenging and expensive process of medical diagnosis. What turns these new propositions into promising tools are their natural fault-tolerant properties, and their capability of finding near-optimum solutions from limited or incomplete data. In this context, new propositions are introduced as tools for building intelligent diagnostic systems. Such systems do not mean to replace the physician from being the decision maker but, rather, they attempt to enhance ones abilities to reach a correct decision. Original papers regarding the theory and application of Computational Intelligent Diagnostic Systems in Medicine are solicited for a Special Issue of the Journal of Intelligent Systems for April 1997. Topics include, but are not limited to, artificial neural systems, evolutionary computing, genetic-based machine learning, fuzzy systems, combinatorial optimization, pattern recognition, and the relationship or combination of these topics for forming hybrid diagnostic/prognostic systems. By November 1st, 1996, prospective authors should submit 5 copies of their papers to the guest Editor: Christos N. Schizas Department of Computer Science University of Cyprus 75 Kallipoleos Str., P.O.Box 537 CY-1678 Nicosia, CYPRUS Tel: +357-2-338705 Fax +357-2-339062 email: schizas@turing.cs.ucy.ac.cy From owner-gann-list Fri May 17 09:23:36 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id JAA23549 for gann-list-outgoing; Fri, 17 May 1996 09:05:14 -0500 (CDT) Message-Id: <2.2.32.19960517140207.0068a678@1.1.250.210> X-Sender: ere@1.1.250.210 X-Mailer: Windows Eudora Pro Version 2.2 (32) Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Fri, 17 May 1996 10:02:07 -0400 To: connect-bb@ed.eusip, incon@dcs.shef.ac.uk, reinforce@cs.uwa.edu.au, gann-list@cs.iastate.edu, neuron@CATTELL.psych.upenn.edu, alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, connectionists@cs.cmu.edu, epsynet@uhupvm1.bitnet, elsnet-list@cogsci.ed.ac.uk, irl-net@irlearn.bitnet, arpanet-bboards@edu.mit.lcs.mc, hybrid-list@cs.ua.edu, colt@cs.uiuc.edu, ilpnet@ijs.si, cphc-jobs@ukc.ac.uk, ai-stats@watstat.uwaterloo.ca From: Edward Engler Subject: GANN: Research and development positions Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: Edward Engler Empirical Media Corporation Pittsburgh, PA Positions Available: Research Scientists and Software Developers Empirical Media Corporation is a venture capital-backed software startup directed at bringing the latest technology in machine learning and collaborative filtering to the Internet. EMC has positions open in both product development and scientific research for highly qualified candidates. EMC's web-based service provides highly personalized information filtering to general and vertical market Internet users. User feedback regarding content is collected and used to increase the system's understanding of the user's interests. The work in the product development area includes user interface development, machine learning, collaborative filtering, information retrieval, agent technology, and distributed systems. EMC is pushing the edge of research particularly hard in the areas of machine learning, neural networks, collaborative filtering, and virtual society communications (a branch of HCI). Candidates must have a research background in one of the relevant areas, have extensive programming experience, excellent analytical skills, strong interpersonal and communication skills, and be self-motivated. Ideal Candidates will have experience in object-oriented design and C++ programming. Experience with Oracle, Windows NT, Visual C++, RPC technology, and Internet-related areas is also beneficial. For further information, please contact Ed Engler at (412)688-8870. Additional information can be found at http://www.empirical.com, however our service is currently in a limited beta, and has not yet been made available to the general public. Edward Engler {M( -- Empirical Media Corporation 5001 Centre Ave., Pittsburgh, PA 15213 Voice: (412) 688-8870 Fax:(412) 688-8853 From owner-gann-list Fri May 17 16:24:39 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id QAA10574 for gann-list-outgoing; Fri, 17 May 1996 16:19:18 -0500 (CDT) Message-Id: <319CEE1E.421D@mond1.ccrc.uga.edu> Date: Fri, 17 May 1996 17:22:38 -0400 From: Faramarz Valafar Organization: CCRC, UGA X-Mailer: Mozilla 2.0 (WinNT; I) Mime-Version: 1.0 To: gann-list@cs.iastate.edu Cc: faramarz@mond1.ccrc.uga.edu Subject: GANN: Announcing a Neural Network based www server Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: 8bit Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: Faramarz Valafar The Complex Carbohydrate Research Center (CCRC) of the University of Georgia is announcing an analytical glyco-chemistry service via the World Wide Web. The software is called CarbNet (Carbohydrate Neural Networks), and is available at http://www.ccrc.uga.edu/ CarbNet is an analytical tool for identification and verification of carbohydrate spectra. The system uses a neural network pattern matching engine to match spectra of unknown compounds submitted by the user with spectra of compounds from CCRCs databases. Each search of CarbNet produces a ranked "hit list" of compounds in our library whose spectra most closely match the unknown spectrum, and a graphical presentation of both the submitted spectrum and the "best match" sample spectrum from CarbNet. Each compound in the hit list is associated with a score that represents the closeness of the match. The graphical presentation assists in the visual verification of the search result. A capability is provided for "static" zooming into a certain region of the spectra. The CCRC currently offers analytical services for two types of complex carbohydrates. These are: combined Gas chromatography-electron impact mass spectra (GC-EIMS) of partially methylated alditol acetates (PMAAs), and for 500-MHz NMR spectra of xyloglucan subunit oligosaccharides. The CarbNet software is currently a prototype. We hope that, after using this prototype, your feedback will assist us in developing an improved version. Some improvements such as "dynamic" zooming capability will be incorporated as soon as JAVA (or an alternative such as OLE) compatible web browsers become more comon. CarbNet currently uses some extensions to HTML 2.0. Therefor, it is best presented by Netscapes or Microsofts web browsers. If you have a spectrum of the above-mentioned types, we encourage you to submit it to CarbNet and let us know how well CarbNet performs in helping to identify your compound. We especially encourage you to send us an e-mail message if you encounter a problem or receive an incorrect response from CarbNet. You should be aware that these spectral libraries are not complete; we plan to add spectra to each of these libraries and to introduce libraries of other types of complex carbohydrates. If either library does not produce a "match" for your submitted spectrum, we ask that you contact us by e-mail at the address below so that we can arrange to add your spectrum to our library. Thereby increasing the value of the library to the scientific community. On the system, we have sample spectra for above mentioned libraries. If you do not have a spectrum to submit, we encourage you to use these spectra to initiate a search, and observe the functionality of the system. We welcome any comments that you might have. For further inquiries or any problems with CarbNet, please contact faramarz@mond1.ccrc.uga.edu. PS: We are soliciting proposals for expanding the CarbNet library system within the carbohydrate field and beyond. If you use a library of data that you often refer to, and could benefit from a Neural Network pattern matching engine, we would like you to send us an email. We are looking to expand the current system beyond carbohydrates, into recognition and analysis of various types of medical data, as well as fields such as synthetic Chemistry. The new libraries will be available for public use via the world wide web. -- Faramarz Valafar Complex Carbohydrate Research Center University of Georgia USA From owner-gann-list Sun May 19 18:12:59 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id RAA01632 for gann-list-outgoing; Sun, 19 May 1996 17:58:05 -0500 (CDT) Date: Sun, 19 May 1996 18:56:40 -0400 Message-Id: <199605192256.SAA03201@garnet.cs.brandeis.edu> From: Maja Mataric To: alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, connectionists@cs.cmu.edu, gann-list@cs.iastate.edu, hybrid-list@cs.ua.edu, intcon@phoenix.ee.unsw.edu.au, ml@ics.uci.edu, reinforce@cs.uwa.EDU.AU Subject: GANN: CALL for PAPERS Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: Maja Mataric CALL FOR PAPERS (http://www.cs.brandeis.edu:80/~maja/abj-special-issue/) ADAPTIVE BEHAVIOR Journal Special Issue on COMPLETE AGENT LEARNING IN COMPLEX ENVIRONMENTS Guest editor: Maja J Mataric Submission Deadline: June 1, 1996. Adaptive Behavior is an international journal published by MIT Press; Editor-in-Chief: Jean-Arcady Meyer, Ecole Normale Superieure, Paris. In the last decade, the problems being treated in AI, Alife, and Robotics have witnessed an increase in complexity as the domains under investigation have transitioned from theoretically clean scenarios to more complex dynamic environments. Agents that must adapt in environments such as the physical world, an active ecology or economy, and the World Wide Web, challenge traditional assumptions and approaches to learning. As a consequence, novel methods for automated adaptation, action selection, and new behavior acquisition have become the focus of much research in the field. This special issue of Adaptive Behavior will focus on situated agent learning in challenging environments that feature noise, uncertainty, and complex dynamics. We are soliciting papers describing finished work on autonomous learning and adaptation during the lifetime of a complete agent situated in a dynamic environment. We encourage submissions that address several of the following topics within a whole agent learning system: * learning from ambiguous perceptual inputs * learning with noisy/uncertain action/motor outputs * learning from sparse, irregular, inconsistent, and noisy reinforcement/feedback * learning in real time * combining built-in and learned knowledge * learning in complex environments requiring generalization in state representation * learning from incremental and delayed feedback * learning in smoothly or discontinuously changing environments We invite submissions from all areas in AI, Alife, and Robotics that treat either complete synthetic systems or models of biological adaptive systems situated in complex environments. Submitted papers should be delivered by June 1, 1996. Authors intending to submit a manuscript should contact the guest editor to discuss paper suitability for this issue. Use maja@cs.brandeis.edu or tel: (617) 736-2708 or fax: (617) 736-2741. Manuscripts should be typed or laser-printed in English (with American spelling preferred) and double-spaced. Both paper and electronic submission are possible, as described below. Copies of the complete Adaptive Behavior Instructions to Contributors are available on request--also see the Adaptive Behavior journal's home page at: http://www.ens.fr:80/bioinfo/www/francais/AB.html. For paper submissions, send five (5) copies of submitted papers (hard-copy only) to: Maja Mataric Volen Center for Complex Systems Computer Science Department Brandeis University Waltham, MA 02254-9110, USA For electronic submissions, use Postscript format, ftp the file to ftp.cs.brandeis.edu/incoming, and send an email notification to maja@cs.brandeis.edu. For a Web page of this call, and detailed ftp directions, see: http://www.cs.brandeis.edu/~maja/abj-special-issue/ From owner-gann-list Mon May 20 08:40:15 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id IAA00266 for gann-list-outgoing; Mon, 20 May 1996 08:35:08 -0500 (CDT) Message-Id: <9605201335.AA21434@kim.iscm.ulst.ac.uk> X-Sender: shauna@claudia.iscm.ulst.ac.uk Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Mon, 20 May 1996 14:35:49 +0100 To: gann-list@cs.iastate.edu From: shauna@iscm.ulst.ac.uk (Shaunna McClintock) Subject: GANN: Search for PhD Thesis X-Mailer: Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: shauna@iscm.ulst.ac.uk (Shaunna McClintock) Hi All, I trying to track two PhD thesis' from the Univeristy of California of 1994. They are entitled :: Cooper M.G. "Genetic Design of Rule-Based Fuzzy Controllers" and Lee M.A. "Automatic Design and Adaptation of Fuzzy Systems and Genetic Algorithms using Soft Computing Techniques" Any advice in finding these are greatly appreciated. Regards Shaunna McClintock ========================================================= Shaunna McClintock PhD Research Student Fuzzy Logic & Genetic Algorithms Interactive Systems Centre University of Ulster : Magee College Derry Northern Ireland shauna@iscm.ulst.ac.uk Tel: ( 01504 ) 375618 Fax: ( 01504) 370040 _| _|_|_|_| _|_|_|_|_| _| _| _| _| _|_|_|_| _| _| _| _| _| _|_|_|_| _|_|_|_|_| ========================================================== From owner-gann-list Tue May 21 12:09:26 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id LAA07785 for gann-list-outgoing; Tue, 21 May 1996 11:50:19 -0500 (CDT) Date: Tue, 21 May 1996 17:50:33 +0100 (BST) X-Sender: gds@mail.sys.uea.ac.uk Message-Id: Mime-Version: 1.0 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable To: connect-bb@ed.eusip, incon@dcs.shef.ac.uk, reinforce@cs.uwa.edu.au, gann-list@cs.iastate.edu, neuron-request@CATTELL.psych.upenn.edu, alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, connectionists@CS.CMU.EDU, epsynet@uhupvm1.bitnet, elsnet-list@cogsci.ed.ac.uk, irl-net@irlearn.bitnet, arpanet-bboards@edu.mit.lcs.mc, hybrid-list@cs.ua.edu, colt@cs.uiuc.edu, ilpnet@ijs.si, cphc-jobs@ukc.ac.uk From: gds@sys.uea.ac.uk (George Smith) Subject: GANN: ICANNGA97 Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: gds@sys.uea.ac.uk (George Smith) ICANNGA97 =3D=3D=3D=3D=3D=3D=3D=3D=3D Third International Conference on Artificial Neural Networks and Genetic Algorithms Preceded by a one-day Introductory Workshop Tuesday 1st - Friday 4th April, 1997 Norwich, England, UK CALL FOR PAPERS AND INVITATION TO PARTICIPATE Conference Theme: =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D The main theme of the ICCANGA series is the development and application of software paradigms based on natural processes, principally artificial neural networks, genetic algorithms and hybrids thereof. However, the scope of the conference extends to cover many related topics including fuzzy logic, genetic programming and other evolutionary computation systems, classifier systems and adaptive agent systems, distributed intelligence and artificial life, generic optimisation heuristics including simulated annealing and tabu search, and many more. =46ollowing the successes of ICANNGA93 (Innsbruck, Austria) and ICCANGA95 (Ales, France), the third meeting of this interdisciplinary conference will be held at the University of East Anglia in the picturesque, medieval city of Norwich, England. The ICANNGA series has quickly established itself as a platform, not only for established workers in the fields, but also for new and young researchers wishing to extend their knowledge and experience. The conference will be preceded by a one day workshop during which introductory sessions on a range of relevant topics will be held. There will be ample opportunity to gain practical experience in the techniques pertaining to the workshop and conference. The conference is hosted by the University of East Anglia, which is a campus university in a parkland setting, offering first class conference facilities including award winning en-suite accomodation and lecture theatres. The conference will include invited talks and contributed oral and poster presentations. It is expected that the ICANNGA97 Proceedings will be printed by Springer-Verlag (Vienna), following the tradition set by its predecessors. International Advisory Committee =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D Prof. R. Albrecht, University of Innsbruck, Austria Dr. D. Pearson, Ecole des Mines d'Ales, France Prof. N. Steele, Coventry University, England (Chair) Dr. G. D. Smith, University of East Anglia, England Programme Committee =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Thomas Baeck, Informatik Centrum, Dortmund, Germany Wilfried Brauer, TU M=FCnchen, Germany Marco Dorigo, Universit=E9 Libre de Bruxelles, Belgium Terry Fogarty, University of West England, Bristol, UK Jelena Godjevac, EPFL Laboratories, Lausanne, Switzerland Michael Heiss, Neural Network Group, Siemens AG, Austria Tom Harris, Brunel University, London, UK Anne Johannet, EMA-EERIE, Nimes, France Helen Karatza, Aristotle University of Thessaloniki, Greece Sami Kuri, San Jose State University, USA Pedro Larranaga, University Basque Country, San Sebastian, Spain =46rancesco Masulli, University of Genoa, Italy Josef Mazanec, WU Wien, Austria Janine Magnier, EMA-EERIE, N=EEmes, France =46ranz Oppacher, Carleton University, Ottawa, Canada Ian Parmee, University of Plymouth, UK David Pearson, EMA-EERIE, N=EEmes, France Vic Rayward-Smith, University of East Anglia, Norwich, UK Colin Reeves, Coventry University, Coventry, UK Bernardete Ribeiro, Universidade de Coimbra, Portugal Valentina Salapura, TU-Wien, Austria V. David S=E1nchez A., University of Miami, Florida, USA Henrik Sax=E9n, =C5bo Akademi, Finland George D. Smith, University of East Anglia, Norwich, UK Nigel Steele, Coventry University, Coventry, UK Kevin Warwick, Reading University, Reading, UK Darrell Whitley, Colorado State University, USA Diethelm W=FCrtz, Swiss Federal Inst. of Technology, Z=FCrich, Switzerland Organising Committee =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Dr. G. D. Smith, University of East Anglia, England Nigel Steele, Coventry University, Coventry Prof. Vic Rayward-Smith, University of East Anglia, Norwich Submission Instructions =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Contributions are sought in the following topic areas, which is not exhausti= ve: - Theoretical and Computational Aspects of Artificial Neural Networks: including computational learning, approximation theory, novel paradigms and training methods, dynamical systems, hardware implementation - Practical Applications of Artificial Neural Networks: including pattern recognition, speech and signal processing, visual processing, time series prediction, medical and other diagnostic systems, fault and anomaly detection, financial applications, data compression, datamining, machine learning - Theoretical and Computational Aspects of Genetic Algorithms: including schema theory developments, Markov models, convergence analysis, no free lunch theorem, computational analysis, novel sequential and parallel GA systems - Practical Applications of Genetic Algorithms; including function and combinatorial optimisation, machine learning, classifier and agent systems, datamining, real-world industrial and commercial applications - Hybrid and related topics: including genetic programming, evolutionary programming and evolution strategies, fuzzy logic and control, neuro-fuzzy systems, simulated annealing and tabu search, hybrid search algorithms, hybrid ANN/GA systems Authors should submit an extended abstract of around 1500-2000 words, or full paper, of their proposed contribution before 31st August 1996. Abstracts and papers must be in English and must contain a concise description of the problem, the results achieved, their relevance and a comparison with previous work. The abstract/paper should also contain the following details: Title Authors' names and affiliations Name, address and email address of contact author Keywords Three typed/printed copies should be sent to the following address: Dr George D. Smith School of Information Systems University of East Anglia Norwich, Norfolk, NR4 7TJ UK Alternatively, abstracts may be sent by email to either: gds@sys.uea.ac.uk or rs@sys.uea.ac.uk Notification of acceptance of the paper for presentation will be made by November 30th 1996. Papers accepted for both oral and poster presentations will be published in the Conference Proceedings. Pre-Conference Workshop =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D It is intended to hold a workshop on April 1st, 1997, prior to the Conference. This workshop is intended for those who are new to the topics and wish to gain a better understanding of the fundamental aspects of neural networks and genetic algorithms. The format of this workshop will be as follows: Theoretical issues of ANNs Key Issues in the application of ANNs Introduction to GAs and other heuristic search algorithms Key Issues in the application of GAs and related heuristics The second and fourth topics are backed up with laboratory sessions in which participants will have the opportunity to use some of the latest software toolkits supporting the respective technologies. Dates to remember: =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D =46irst Announcement & CFP: April/May 1996 Submission of Abstracts/Papers: August 31st 1996 Notification of Acceptance: November 30th 1996 Delivery of full paper: January 30th 1997 Pre-Conference Workshop: April 1st 1997 ICANNGA97: April 2nd-4th 1997 =46urther Information: =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D =46or more information on ICANNGA97, regularly updated, visit the WWW site at: http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/Default.html This web page also contains a pre-registration form. Pre-Registration form: =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Please enter your details below to receive further information about ICANNGA97 and a full registration form. =46irst name: ______________________________________ =46amily name: ______________________________________ Affiliation: ______________________________________ Address: ______________________________________ City: ______________________________________ State/Province/County: ______________________________________ ZIP/Postal Code: ______________________________________ Country: ______________________________________ Daytime telephone number: ______________________________________ Email address: ______________________________________ =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D Dr. George D Smith Computing Science Sector School of Information Systems University of East Anglia Norwich NR4 7TJ, UK Tel: + 44 (0)1603 593260 FAX: + 44 (0)1603 503344 Email: gds@sys.uea.ac.uk www: http://www.sys.uea.ac.uk/Teaching/Staff/gds.html =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D From owner-gann-list Sun May 26 01:43:10 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id BAA10984 for gann-list-outgoing; Sun, 26 May 1996 01:31:46 -0500 (CDT) Posted-Date: Sat, 25 May 1996 23:29:32 -0700 (PDT) Date: Sat, 25 May 96 23:29:01 PDT From: John Koza To: ga-molecule-approval@interval.com, EP-List@magenta.me.fau.edu Cc: gasched@acse.shef.ac.uk, gann-list@cs.iastate.edu Subject: GANN: GP gets best solution to GKL problem Message-ID: Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: John Koza The following paper is now available in Post Script. Evolution of Intricate Long-Distance Communication Signals in Cellular Automata using Genetic Programming ABSTRACT: It is exceedingly difficult to program cellular automata. This is especially true when the desired computation requires global communication and global integration of information across great distances of time and space in the cellular space. Various human-written algorithms have appeared in the past two decades for the vexatious majority classification task for one-dimensional two-state cellular automata. This paper describes how genetic programming with automatically defined functions evolved a rule for this task with an accuracy of 82.326%. This level of accuracy exceeds that of the original 1978 Gacs-Kurdyumov-Levin (GKL) rule, all other known human-written rules, and all other known rules produced by automated methods. The rule evolved by genetic programming is qualitatively different from all previous rules in that it employs a larger and more intricate repertoire of domains and particles to represent and communicate information across the cellular space. David Andre Visiting Scholar Computer Science Department Stanford University E-MAIL: andre@flamingo.stanford.edu John R. Koza Computer Science Department 258 Gates Building Stanford University Stanford, California 94305 E-MAIL: Koza@CS.Stanford.Edu Forrest H Bennett III Visiting Scholar Computer Science Department Stanford University E-MAIL: fhb3@slip.net Paper available in Postscript via WWW from http://www-cs-faculty.stanford.edu/~koza/ Look under "Research Publications" and "Recent Papers" on the home page. This paper was presented at the Artificial Life V conference in Nara, Japan on May 16- 18, 1996. A longer version of this paper will be presented at the GP-96 conference to be held at Stanford University on July 28-31, 1996. For information, see http://www.cs.brandeis.edu/~zippy/gp-96.html From owner-gann-list Sun May 26 01:43:10 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id BAA09085 for gann-list-outgoing; Sun, 26 May 1996 01:27:34 -0500 (CDT) Posted-Date: Sat, 25 May 1996 23:25:27 -0700 (PDT) Date: Sat, 25 May 96 23:25:27 PDT From: John Koza To: ga-molecule-approval@interval.com, ga-molecule-approval@interval.com Cc: EP-List@magenta.me.fau.edu, gann-list@cs.iastate.edu Subject: GANN: Call for Syllabi on GA Courses Message-ID: Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: John Koza CALL FOR SYLLABI Information for Instructors and Prospective Instructors of University Courses on Genetic Algorithms The booklet entitled "University Courses on Genetic Algorithms 1995" containing information on 30 known genetic algorithm courses ("the GA 30") around the world was published in December 1995. The information provided by each instructor varied, but included items such as course syllabi, reading lists, problem sets, examinations, background materials, etc. This booklet was well received by the contributing instructors and others (particular faculty contemplating starting up such courses at their own universities). Therefore, we have decided to publish an updated booklet in late 1996. Hopefully, this new booklet will contain even more information about the existing 30 GA courses as well as information about various additional genetic algorithm courses. Please send me the 9-part questionaire (below) plus whatever additional material, on paper, that you care to share about your university course in genetic algorithms or artificial life by Friday, November 1, 1996 to John R. Koza Computer Science Department 258 Gates Building Mail Code 9120 Stanford University Stanford, California 94305 USA The Questionaire (1) Name of Instructor (2) Physical mailing address of instructor (3) E-mail address of instructor (4) WWW URL of instructor (5) Department in which course is taught (6) Course number (7) When was course last taught (8) When will course be next taught (9) WWW URL of the course (if there is a "course home page") We will publish your 9-part questionaire and whatever additional pages (e.g., course syllabi, reading lists, problem sets, examinations, background materials, etc.) that you provide concerning your course. Each instructor providing information will be sent a copy of the booklet when it is published (about December 1996). Arrangements will be made so that additional copies of the booklet may be subsequently obtained from the Stanford Bookstore. John Koza Stanford University From owner-gann-list Sun May 26 01:43:10 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id BAA11322 for gann-list-outgoing; Sun, 26 May 1996 01:32:06 -0500 (CDT) Posted-Date: Sat, 25 May 1996 23:29:55 -0700 (PDT) Date: Sat, 25 May 96 23:29:54 PDT From: John Koza To: ga-molecule-approval@interval.com, EP-List@magenta.me.fau.edu Cc: gasched@acse.shef.ac.uk, gann-list@cs.iastate.edu Subject: GANN: GP is competitive with humans on 4 problems Message-ID: Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: John Koza We have fixed the problem and the following paper is now available in Post Script. Four Problems for which a Computer Program Evolved by Genetic Programming is Competitive with Human Performance ABSTRACT: It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a domain-independent automatic programming technique in which "What You Want Is What You Get" ("WYWIWYG" - pronounced "wow-eee-wig"). Genetic programming is such a technique. This paper surveys three recent examples of problems (from the fields of cellular automata and molecular biology) in which genetic programming evolved a computer program that produced results that were slightly better than human performance for the same problem. This paper then discusses the problem of electronic circuit synthesis in greater detail. It shows how genetic programming can evolve both the topology of a desired electrical circuit and the sizing (numerical values) for each component in a crossover (woofer and tweeter) filter. Genetic programming has also evolved the design for a lowpass filter, the design of an amplifier, and the design for an asymmetric bandpass filter that was described as being difficult-to-design in an article in a leading electrical engineering journal. John R. Koza Computer Science Department 258 Gates Building Stanford University Stanford, California 94305 E-MAIL: Koza@CS.Stanford.Edu Forrest H Bennett III Visiting Scholar Computer Science Department Stanford University E-MAIL: Koza@CS.Stanford.Edu David Andre Visiting Scholar Computer Science Department Stanford University E-MAIL: fhb3@slip.net Martin A. Keane Econometrics Inc. Chicago, IL 60630 Paper available in Postscript via WWW from http://www-cs-faculty.stanford.edu/~koza/ Look under "Research Publications" and "Recent Papers" on the home page. This paper was presented at the IEEE International Conference on Evolutionary Computation on May 20-22, 1996 in Nagoya, Japan. Additional papers on evolving electrical circuits will be presented at the GP-96 conference to be held at Stanford University on July 28-31, 1996. For information, see http://www.cs.brandeis.edu/~zippy/gp-96.html From owner-gann-list Mon May 27 18:56:42 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id SAA04024 for gann-list-outgoing; Mon, 27 May 1996 18:40:45 -0500 (CDT) Posted-Date: Mon, 27 May 1996 16:38:21 -0700 (PDT) Date: Mon, 27 May 96 16:38:21 PDT From: John Koza To: ga-molecule-approval@interval.com, gasched@acse.shef.ac.uk Cc: gann-list@cs.iastate.edu, news-announce-conferences@uunet.uu.net Subject: GANN: GP-96 Detailed Time Schedule Message-ID: Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: John Koza The tentative time schedule for the talks at the GP-96 conference to be held on July 28 - 31, 1996 (Sunday through Wednesday) at Stanford University is below. Additional information is at the GP-96 home page on the World Wide Web at http://www.cs.brandeis.edu/~zippy/gp-96.html or via e-mail at gp@aaai.org. John Koza ------------------------------------------------------ SUNDAY- July 28, 1996 - 11 Tutorials ------------------------------------------------------ MONDAY - July 29, 1996 MORNING PLENARY SESSION IN FAIRCHILD AUDITORIUM 8:45 - 9:00 John Koza, GP-96 General Chair 9:00 - 10:00 Hidden Order John Holland 10:00 - 10:25 Discovery by Genetic Programming of a Cellular Automata Rule that is Better than any Known Rule for the Majority Classification Problem David Andre, Forrest H Bennett III, and John R. Koza 10:25 - 10:40 Break 10:40 - 11:05 Solving Facility Layout Problems Using Genetic Programming Jaime Garces-Perez, Dale A. Schoenefeld, and Roger L. Wainwright 11:05 - 11:30 Silicon Evolution Adrian Thompson 11:30 - 11:55 Genetic Programming of Near-Minimum-Time Spacecraft Attitude Maneuvers Brian Howley 11:55 - 12:20 Automated WYWIWYG Design of Both the Topology and Component Values of Electrical Circuits Using Genetic Programming John R. Koza, Forrest H Bennett III, David Andre, and Martin A. Keane --------------------------------------------------------- 12:20 - 1:30 Lunch --------------------------------------------------------- TRACK 1 1:30 - 1:55 Evolving Fractal Movies Peter J. Angeline 1:55 - 2:20 Preliminary Experiments on Discriminating between Chaotic Signals and Noise Using Evolutionary Programming David B. Fogel and Lawrence J. Fogel 2:20 - 2:45 Discovering Patterns in Spatial Data using Evolutionary Programming Adam Ghozeil and David B. Fogel 2:45 - 3:10 Evolving Reduced Parameter Bilinear Models for Time Series Prediction using Fast Evolutionary Programming Sathyanarayan S. Rao and Kumar Chellapilla --------------------------------------------------------- TRACK 2 1:30 - 1:55 High-Performance, Parallel, Stack-Based Genetic Programming Kilian Stoffel and Lee Spector 1:55 - 2:20 Toward Simulated Evolution of Machine Language Iteration Lorenz Huelsbergen 2:20 - 2:45 A New Class of Function Sets for Solving Sequence Problems Simon Handley 2:45 - 3:10 The Evolution of Memory and Mental Models Using Genetic Programming Scott Brave --------------------------------------------------------- TRACK 3 1:30 - 1:55 Learning Recursive Functions from Noisy Examples using Generic Genetic Programming Man Leung Wong and Kwong Sak Leung 1:55 - 2:20 Dynamics of Genetic Programming and Chaotic Time Series Prediction Brian S. Mulloy, Rick L. Riolo, and Robert S. Savit 2:20 - 2:45 Waveform Recognition Using Genetic Programming: The Myoelectric Signal Recognition Problem Jaime J. Fernandez, Kristin A. Farry, and John B. Cheatham 2:45 - 3:10 Optimizing Local Area Networks Using Genetic Algorithms Andy Choi --------------------------------------------------------- 3:10 - 3:25 Break --------------------------------------------------------- TRACK 4 3:25 - 3:50 Bargaining by Artificial Agents in Two Coalition Games: A Study in Genetic Programming for Electronic Commerce Garett Dworman, Steven O. Kimbrough, and James D. Laing 3:50 - 4:15 Genetic Programming and the Efficient Market Hypothesis Shu-Heng Chen and Chia-Hsuan Yeh 4:15 - 4:40 Parallel Genetic Programming: An Application to Trading Models Evolution Mouloud Oussaidene, Bastien Chopard, Olivier V. Pictet, and Marco Tomassini 4:40 - 5:05 Improved Direct Acyclic Graph Evaluation and the Combine Operator in Genetic Programming Herman Ehrenburg --------------------------------------------------------- TRACK 5 3:25 - 3:50 Distributed Genetic Programming: Empirical Study and Analysis Tatsuya Niwa and Hitoshi Iba 3:50 - 4:15 Paragen: A Novel Technique for the Autoparallelisation of Sequential Programs using Genetic Programming Paul Walsh and Conor Ryan 4:15 - 4:40 Motion Planning and Design of CAM Mechanisms by Means of a Genetic Algorithm Rodolfo Faglia and David Vetturi 4:40 - 5:05 An Adverse Interaction between Crossover and Restricted Tree Depth in Genetic Programming Chris Gathercole and Peter Ross --------------------------------------------------------- TRACK 6 3:25 - 3:50 Evolving Event Driven Programs Mark Crosbie and Eugene H. Spafford 3:50 - 4:15 Entailment for Specification Refinement Thomas Haynes, Rose Gamble, Leslie Knight, and Roger Wainwright 4:15 - 4:40 MASSON: Discovering Commonalities in Collection of Objects using Genetic Programming Tae-Wan Ryu and Christoph F. Eick 4:40 - 5:05 Recognition and Reconstruction of Visibility Graphs Using a Genetic Algorithm Marshall S. Veach --------------------------------------------------------- 5:05 - 6:00 Break --------------------------------------------------------- 6:00 - 7:30 Reception --------------------------------------------------------- 7:30 - 9:30 Posters and Late-Breaking Papers GENETIC PROGRAMMING POSTER PAPERS Co-Evolving Hierarchical Programs using Genetic Programming Manu Ahluwalia and Terence C. Fogarty Genetic Programming Tools Available on the Web: A First Encounter Anthony G. Deakin and Derek F. Yates Speeding up Genetic Programming: A Parallel BSP Implementation Dimitris C. Dracopoulos and Simon Kent Easy Inverse Kinematics using Genetic Programming Jonathan Gibbs Noisy Wall-Following and Maze Navigation through Genetic Programming Andrew Goldish Genetic Programming Classification of Magnetic Resonance Data H. F. Gray, R. J. Maxwell, I. Martinez-Perez, C. Arus, and S. Cerdan GP-COM: A Distributed Component-Based Genetic Programming System in C++ Christopher Harris and Bernard Buxton Clique Detection via Genetic Programming Thomas Haynes and Dale Schoenefeld Functional Languages on Linear Chromosomes Paul Holmes and Peter J. Barclay Improving the Accuracy and Robustness of Genetic Programming through Expression Simplification Dale C. Hooper and Nicholas S. Flann COAST: An Approach to Robustness and Reusability in Genetic Programming Naohiro Hondo, Hitoshi Iba, and Yukinori Kakazu Recurrences with Fixed Base Cases in Genetic Programming Stefan J. Johansson Evolutionary and Incremental Methods to Solve Hard Learning Problems Ibrahim Kuscu Detection of Patterns in Radiographs using ANN Designed and Trained with the Genetic Algorithm Alejandro Pazos, Julian Dorado, and Antonino Santos The Logic-Grammars-Based Genetic Programming System Man Leung Wong and Kwong Sak Leung Building Software Agents for Information Filtering on the Internet: A Genetic Programming Approach Byoung-Tak Zhang, Ju-Hyun Kwak, and Chang-Hoon Lee ---LBP BOOK--- GENETIC ALGORITHM POSTER PAPERS The Use of Genetic Algorithms in the Optimization of Competitive Neural Networks which Resolve the Stuck Vectors Problem Tin Ilakovac, Zeljka Perkovic, and Strahil Ristov An Extraction Method of a Car License Plate using a Distributed Genetic Algorithm Dae Wook Kim, Sang Kyoon Kim, and Hang Joon Kim GENETIC PROGRAMMING LATE BREAKING PAPERS Evolving Recurrent Neural Network Architectures by Genetic Programming Anna I. Esparcia-Alcazar and Kenneth C. Sharman ---LBP BOOK--- GENETIC ALGORITHM LATE BREAKING PAPERS EVOLUTIONARY PROGRAMMING LATE BREAKING PAPERS Evolutionary Algorithms for Natural Language Processing Ted E. Dunning and Mark W. Davis ---LBP BOOK--- --------------------------------------------------------- TUESDAY - July 30, 1996 MORNING PLENARY SESSION IN FAIRCHILD AUDITORIUM 8:45 - 9:00 John Koza, GP-96 General Chair 9:00 - 10:00 Genetic Algorithms David E. Goldberg 10:00 - 10:25 Using Data Structures within Genetic Programming W. B. Langdon 10:25 - 10:40 Break 10:40 - 11:05 Evolving Evolution Programs: Genetic Programming and L-Systems Christian Jacob 11:05 - 11:30 A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks Frederic Gruau, Darrell Whitley, and Larry Pyeatt 11:30 - 11:55 Code Growth in Genetic Programming Terence Soule, James A. Foster, and John Dickinson 11:55 - 12:20 Cultural Transmission of Information in Genetic Programming Lee Spector and Sean Luke --------------------------------------------------------- 12:20 - 1:30 Lunch --------------------------------------------------------- TRACK 7 1:30 - 1:55 Use of Automatically Defined Functions and Architecture-Altering Operations in Automated Circuit Synthesis with Genetic Programming John R. Koza, David Andre, Forrest H Bennett III, and Martin A. Keane 1:55 - 2:20 Investigating the Generality of Automatically Defined Functions Una-May O'Reilly 2:20 - 2:45 Evolving Deterministic Finite Automata Using Cellular Encoding Scott Brave 2:45 - 3:10 Variations in Evolution of Subsumption Architectures Using Genetic Programming: The Wall Following Robot Revisited Steven J. Ross, Jason M. Daida, Chau M. Doan, Tommaso F. Bersano-Begey, and Jeffrey J. McClain --------------------------------------------------------- TRACK 8 1:30 - 1:55 A Study in Program Response and the Negative Effects of Introns in Genetic Programming David Andre and Astro Teller 1:55 - 2:20 Ontogenetic Programming Lee Spector and Kilian Stoffel 2:20 - 2:45 Generality Versus Size in Genetic Programming Justinian P. Rosca 2:45 - 3:10 The Benefits of Computing with Introns Mark Wineberg and Franz Oppacher 1:30 - 1:55 --------------------------------------------------------- TRACK 9 1:30 - 1:55 Computer-Assisted Design of Image Classification Algorithms: Dynamic and Static Fitness Evaluations in a Scaffolded Genetic Programming Environment Jason M. Daida, Tommaso F. Bersano-Begey, Steven J. Ross, and John F. Vesecky 1:55 - 2:20 Programmatic Compression of Images and Sound Peter Nordin and Wolfgang Banzhaf 2:20 - 2:45 Genetic Programming for Image Analysis Riccardo Poli 2:45 - 3:10 Evolving Edge Detectors with Genetic Programming Christopher Harris and Bernard Buxton --------------------------------------------------------- 3:10 - 3:25 Break --------------------------------------------------------- 3:00 - 5:15 Tutorial 12 - David E. Rumelhart, Stanford University 3:00 - 5:15 Tutorial 13 - Machine Learning Pat Langley, Stanford University 3:00 - 5:15 Tutorial 14 - Molecular Biology for Computer Scientist Russ B. Altman, M. D., Ph.D , Stanford University. --------------------------------------------------------- 5:15 - 6:00 Break --------------------------------------------------------- 6:00 - 7:30 Reception --------------------------------------------------------- 7:30 - 9:30 Tutorial 15 - Evolvable Hardware Tutorial Hugo De Garis, ATR, Kyoto, Japan and Adrian Thompson, University of Sussex, UK --------------------------------------------------------- WEDNESDAY - July 31, 1996 MORNING PLENARY SESSION IN FAIRCHILD AUDITORIUM 8:45 - 9:10 An Investigation into the Sensitivity of Genetic Programming to the Frequency of Leaf Selection During Subtree Crossover Peter J. Angeline 9:10 - 9:35 Benchmarking the Generalization Capabilities of A Compiling Genetic programming System using Sparse Data Sets Frank D. Francone, Peter Nordin, and Wolfgang Banzhaf 9:35 - 10:00 Genetic Programming using Genotype-Phenotype Mapping from Linear Genomes into Linear Phenotypes Robert E. Keller and Wolfgang Banzhaf 10:00 - 10:25 Genetic Programming, the Reflection of Chaos, and the Bootstrap: Towards a useful Test for Chaos E. Howard N. Oakley --------------------------------------------------------- 10:25 - 10:40 Break --------------------------------------------------------- 10:40 - 11:05 Search Bias, Language Bias, and Genetic Programming P. A. Whigham 11:05 - 11:30 Using Genetic Programming to Develop Inferential Estimation Algorithms Ben McKay, Mark Willis, Gary Montague, and Geoffrey W. Barton 11:30 - 11:55 Evolving Teamwork and Coordination with Genetic Programming Sean Luke and Lee Spector 11:55 - 12:20 Automatic Creation of an Efficient Multi-Agent Architecture Using Genetic Programming with Architecture-Altering Operations Forrest H Bennett III --------------------------------------------------------- 12:20 - 1:30 Lunch --------------------------------------------------------- TRACK 10 1:30 - 1:55 Classifier System Renaissance: New Analogies, New Directions H. Brown Cribbs III and Robert E. Smith 1:55 - 2:20 Three-Dimensional Shape Optimization Utilizing a Learning Classifier System Robert A. Richards and Sheri D. Sheppard 2:20 - 2:45 Natural Niching for Evolving Cooperative Classifiers Jeffrey Horn and David E. Goldberg 2:45 - 3:10 Genetic Algorithms with Analytical Solution Erol Gelenbe --------------------------------------------------------- TRACK 11 1:30 - 1:55 Evolving Control Laws for a Network of Traffic Signals David J. Montana and Steven Czerwinski 1:55 - 2:20 Evolving Agents Adil Qureshi 2:20 - 2:45 Signal Path Oriented Approach for Generation of Dynamic Process Models Peter Marenbach, Kurt D. Bettenhausen, and Stephan Freyer 2:45 - 3:10 Robustness of Robot Programs Generated by Genetic Programming Takuya Ito, Hitoshi Iba, and Masayuki Kimura --------------------------------------------------------- TRACK 12 1:30 - 1:55 Genetic Programming for Improved Data Mining: An Application to the Biochemistry of Protein Interactions M. L. Raymer, W. F. Punch, E. D. Goodman, and L. A. Kuhn 1:55 - 2:20 The Prediction of the Degree of Exposure to Solvent of Amino Acid Residues via Genetic Programming Simon Handley 2:20 - 2:45 Using Genetic Programming to Approximate Maximum Clique Terence Soule, James A. Foster, and John Dickinson 2:45 - 3:10 Evolving Recurrent Neural Network Architectures by Genetic Programming Anna I. Esparcia-Alcazar and Kenneth C. Sharman --- LBP BOOK --- --------------------------------------------------------- 3:10 - 3:25 Break --------------------------------------------------------- TRACK 13 3:25 - 3:50 On Sensor Evolution in Robotics Karthik Balakrishnan and Vasant Honavar 3:50 - 4:15 Testing Software using Order-Based Genetic Algorithms Edward B. Boden and Gilford F. Martino 4:15 - 4:40 A Genetic Algorithm for the Construction of Small and Highly Testable OKFDD Circuits Rold Drechsler, Bernd Becker, and Nicole Gockel --------------------------------------------------------- TRACK 14 3:25 - 3:50 Automatic Generation of Object-Oriented Programs Using Genetic Programming Wilker Shane Bruce 3:50 - 4:15 Evolving Strategies Based on the Nearest Neighbor Rule and a Genetic Algorithm Matthias Fuchs 4:15 - 4:40 No talk scheduled here TRACK 15 3:25 - 3:50 Genetic Programming in Database Query Optimization Michael Stillger and Myra Spiliopoulou 3:50 - 4:15 Classification using Cultural Co-Evolution and Genetic Programming Myriam Z. Abramson and Lawrence Hunter 4:15 - 4:40 Type-Constrained Genetic Programming for Rule- Base Definition in Fuzzy Logic Controllers Enrique Alba, Carlos Cotta, and Jose J. Troyo --------------------------------------------------------- 4:40 - 5:30 FEEDBACK SESSION IN FAIRCHILD AUDITORIUM From owner-gann-list Mon May 27 23:54:16 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id XAA15481 for gann-list-outgoing; Mon, 27 May 1996 23:51:45 -0500 (CDT) Posted-Date: Mon, 27 May 1996 21:49:25 -0700 (PDT) Date: Mon, 27 May 96 21:49:25 PDT From: John Koza To: ga-molecule-approval@interval.com, gasched@acse.shef.ac.uk Cc: gann-list@cs.iastate.edu, news-announce-conferences@uunet.uu.net Subject: GANN: Revised Call for Late-Breaking Papers for GP-96 Message-ID: Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: John Koza ------------------------------------------------- NOTE: This Call differs from the first one in that it includes the "Permission to Publish" form found at the end of this message. Please be sure to include the "Permission to Publish" form with your late-breaking paper. JK 5-27-96 ------------------------------------------------- SECOND CALL (Version 1.01) FOR LATE-BREAKING PAPERS FOR GENETIC PROGRAMMING 1996 CONFERENCE (GP-96) ------------------------------------------------- DEADLINE: Wednesday, July 3, 1996 ------------------------------------------------- Papers describing late-breaking developments in the field of genetic programming are being solicited for inclusion in a special paper-bound book to be distributed to all attendees of the Genetic Programming 1996 Conference (GP-96) to be held on July 28 - 31 (Sunday - Wednesday), 1996 at Stanford University. This special book is distinct from the conference proceedings containing 73 papers and 17 poster papers that will be published by the MIT Press (also available at the conference). The purpose of late-breaking papers is to provide conference attendees with information about research that was initiated, enhanced, improved, or completed after the original paper submission deadline in January, 1996. Late-breaking papers will be presented during the poster session to be held on the evening of Monday, July 29, 1996 during the GP-96 conference at Stanford University. Arrangements will also be made to enable binterested persons to purchase this book from the Stanford Book Store after the conference. Late-breaking papers will be briefly examined for relevance and minimum standards of acceptability, but will not be peer reviewed. Authors will individually retain copyright (and all other rights) to their late-breaking papers and should feel free to submit them (either before or after our deadline) for publicaton by other conferences or journals. Late-breaking papers must be submitted in camera-ready form in accordance with the GP-96 format specifications that can be found at the GP-96 WWW site (see below). Late-breaking papers should be no more than 10 pages in length. Please send TWO camera-ready copies (printed with very high quality by laser printer) and the SIGNED "permission to publish" form (below) to GP-96 Late-Breaking Papers American Association for Artificial Intelligence 445 Burgess Drive Menlo Park, CA 94025.USA PHONE: 415-328-3123 ------------------------------------------------------ For additional information on GP-96 conference... - on the World Wide Web: http://www.cs.brandeis.edu/~zippy/gp-96.html - via e-mail at gp@aaai.org In cooperation with the Association for Computing Machinery (ACM), SIGART, the IEEE Neural Network Council, and the American Association for Artificial Intelligence. ------------------------------------------------------ PERMISSION TO PUBLISH FORM For Late-Breaking Papers at the GP-96 Conference Title of Paper: ____________________________ Author(s): _______________________________ The undersigned (hereinafter the "Author"), desiring that the paper identified above (hereinafter the "Paper") appear in a publication tentatively entitled "Late-Breaking Papers at the Genetic Programming 1996 Conference" and to be edited by John R. Koza, hereby grants non-exclusive permission to Genetic Programming Conferences Inc., a California non-profit corporation (hereinafter "GPCI"), to prepare and print the Paper, for sale throughout the world, in this publication. The Author retains copyright, right to transfer the copyright to other parties is in the future, right to use any and all portions of the Paper in future publications by the Author, all proprietary rights (patent rights, etc.), and all other rights. The Author assigns copyright to GPCI for publication of the Paper. The Author warrants that he/she is the author and/or proprietor of the Paper; that he/she has full power to make this agreement; that the Paper does not infringe upon any copyright, trademark, or patent; and that he/she has not granted or assigned any rights on the Paper to any person or entity that would interfere with this grant of permission. Authorized Signature: _____________________ Printed Name of Signer: ___________________ Date: _______________ Address: _______________________________ _______________________________________ _______________________________________ From owner-gann-list Wed May 29 21:31:30 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id VAA02315 for gann-list-outgoing; Wed, 29 May 1996 21:18:17 -0500 (CDT) Date: Wed, 29 May 1996 22:18:11 -0400 (EDT) From: "Una-May O'Reilly" X-Sender: unamay@venus To: gann-list@cs.iastate.edu Subject: GANN: CFP: Evolutionary Methods for Program Induction Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: "Una-May O'Reilly" Call for Papers EVOLUTIONARY COMPUTATION Special Issue on Evolutionary Methods for Program Induction Guest editors: Peter Angeline (Lockheed Martin Federal Systems) Una-May O'Reilly (Artificial Intelligence Lab, MIT) A forthcoming issue of Evolutionary Computation will be devoted to the presentation of original research involving evolutionary methods for program induction. The notion of harnessing evolution for the creation of executable programs has a long history, although it has only recently become a well studied topic. In 1950, Turing envisioned the possibility of evolving programs. Friedberg (1958) and Friedberg, Dunham and North (1959), working within the constraints of simple computers, evolved sequences of machine language instructions that performed modest computations. Fogel, Owens, and Walsh (1966) introduced evolutionary programming as a method to evolve behaviors using a finite state machine representation. Holland (1975) suggested that a genetic algorithm could be used with an encoding that represented a computer program. Innovative implementations such as those by Smith (1980), Hicklin (1986), and Fujicki (1986) followed. Much of the current work on evolving executable structures follows the work of Koza (1992) under the name of genetic programming. By using a more LISP-like representation, Koza extended the work of Cramer (1985) which demonstrated that parse trees could provide a natural representation for evolving programs. Koza (1992) applied this technique to a broad range of problems. More recently, genetic programming researchers have explored a diverse collection of topics related to program induction including modular representations, theoretical analysis, genetic operator design, parallel and distributed algorithms, and the use of program memory to represent intermediate states of the problems solving process. This notice solicits papers which relate original and innovative research concerning evolution and adaptation as computational paradigms for discovering, manipulating, or optimizing all forms of programs and other executable structures. Relevant topics include but are not restricted to: o Induction of modular or hierarchical programs o Morphogenic approaches to program induction o Program induction with adaptive and self-adaptive evolutionary computations o Encoding program semantics in evolvable structures (e.g. iteration, recursion, special purpose functionality) o Efficient evolutionary operators for program induction o Evolving object oriented, purely functional, declarative or alternative executable structures o Comparison of evolutionary computations with other forms of program induction and optimization o Control and analysis of program growth and development o Improving the readability and comprehension of evolved programs o The evolution of intelligent software agents o Scientific, practical or real-world applications of evolved executable structures Manuscripts should be approximately 8,000 to 12,000 words in length and formatted for 8.5 by 11 inch paper, single-sided and double-spaced. The first page should include the title, abstract, key words and author information. Authors should submit five copies of their manuscript to the following address no later than Sept 6th, 1996. Una-May O'Reilly (unamay@ai.mit.edu) Rm 812, NE-43 MIT Artificial Intelligence Lab, 545 Technology Square, Cambridge, MA, 02139 USA Important milestones for this special issue are as follows: Sept 6, 1996 Submission deadline December 4, 1996 Notification of acceptance December 23, 1996 Revised versions due to editors January 21, 1997 Finished articles to MIT Press This call for papers is also available from http://www.ai.mit.edu/people/unamay/ec-cfp.html References Cramer, Nichael Lynn, A Representation for the Adaptive Generation of Simple Sequential Programs, Grefenstette: Proceedings of First International Conference on Genetic Algorithms, 1985. Fogel, Lawrence J., Owens, Alvin J., and Walsh, Michael. J. 1966. Artificial Intelligence through Simulated Evolution. New York: John Wiley. Friedberg, R. M. l958. A learning machine: Part I. IBM Journal of Research and Development, 2(1) 2-13, Friedberg, R. M. Dunham, B., and North, J. H. l959. A learning machine: Part II. IBM Journal of Research and Development, 3(3) 282-287. Fujiki, Cory. l986. An Evaluation of Holland's Genetic Algorithm Applied to a Program Generator. M.S. thesis, Department of Computer Science, Moscow, ID: University of Idaho. Hicklin, Joseph F. l986. Application of the Genetic Algorithm to Automatic Program Generation. M. S. thesis, Department of Computer Science. Moscow, ID: University of Idaho. Holland, John H. Adaptation in Natural and Artificial Systems; The University of Michigan Press, Ann Arbor, 1975. Koza, John R. 1989. Hierarchical genetic algorithms operating on populations of computer programs. In Proc. of the 11th Int'l Joint Conf on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann. Volume I. Pages 768-774. Koza, John R. 1992. Genetic Programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, 1992. S.F. Smith, A Learning System Based on Genetic Adaptive Algorithms, Ph.D. Thesis, Computer Science Dept., University of Pittsburgh, Dec. 1980. Turing, Alan M. 1950. Computing Machinery and Intelligence. Mind, LIX, 2236, pages 433-460. From owner-gann-list Thu May 30 13:29:44 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id NAA08945 for gann-list-outgoing; Thu, 30 May 1996 13:05:42 -0500 (CDT) Message-Id: <199605131618.LAA08121@konark.ncst.ernet.in> To: gann@cs.iastate.edu Subject: GANN: CFP:KBCS-96 International Conference on Knowledge Based Computer Systems Date: Mon, 13 May 1996 11:18:33 -0500 From: KBCS96 Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: KBCS96 Call for Papers INTERNATIONAL CONFERENCE ON KNOWLEDGE BASED COMPUTER SYSTEMS National Centre for Software Technology Bombay, India December 16-18, 1996 URL : http://konark.ncst.ernet.in/~kbcs/kbcs96.html ____________________________________________________________________________ The International Conference on Knowledge Based Computer Systems will be held in Bombay, India during December 16-18, 1996. The conference is intended to act as a forum for promoting interaction among researchers in the field of Artificial Intelligence in India and abroad. There will be a two day conference during December 16-17, 1996 followed by one day of post-conference tutorials on December 18, 1996. Papers are invited on substantial, original and unpublished research on all aspects of Artificial Intelligence, including, but not limited to the following: o AI Applications o AI Architectures o Artificial Life o Automatic Programming o Cognitive Modeling o Expert Systems o Foundations of AI o Genetic Algorithms o Information Retrieval o Intelligent Tutoring Systems o Knowledge Acquisition o Knowledge Representation o Machine Learning o Machine Translation o Natural Language Processing o Neural Networks o Planning and Scheduling o Reasoning o Robotics o Search Techniques o Speech Processing o Theorem Proving o Uncertainty Handling o User Interfaces o User Modeling o Vision Programme Committee: S. Arunkumar, IIT, Bombay Amitava Bagchi, IIM, Calcutta Pushpak Bhattacharya, IIT, Bombay Margaret A. Boden, U of Sussex, UK B. B. Chaudhuri, ISI, Calcutta R. Chandrasekar, NCST, Bombay S. S. Gupta, TUL, Bombay J. R. Isaac, NIIT, New Delhi Aravind K. Joshi, R. A. Kowalski, Imperial College, UK U of Pennsylvania, USA H. N. Mahabala, INFOSYS, Bangalore M. Narasimha Murthy, IISc, Bangalore R. Narasimhan, CMC, Bangalore S. Ramani, NCST, Bombay (Chair) P. V. S. Rao, TIFR, Bombay Patrick Saint-Dizier, U of Paul Sabatier, France R. Uthurusamy, GMR, USA M. Vidyasagar, CAIR, Bangalore 2 Format of Submission: Authors should submit their papers, not to exceed 5000 words (including figures and references) either electronically or in hard copy. Papers should be in English. Papers should include an abstract of about 100-200 words in length. Papers outside the specified length are subject to rejection without review. Since reviewing will be "blind", the authors' names and affiliations along with the main area of the paper should be given only on a separate cover sheet. Hard copy submissions should be sent in triplicate. Papers in electronic form can be in any of the following formats: plain text, Postscript, Latex, Microsoft Word (RTF format) or Wordstar. Submissions in electronic form are preferred. Send papers to the KBCS-96 Secretariat at the address below. Paper Submission Deadlines: o Papers due: July 31, 1996 o Acceptance Notification: October 15, 1996 o Camera Ready Copy due: December 1, 1996 Call for Tutorials: Proposals are invited for post-conference tutorials. Tutorials can be half-day or full-day, and will be held on December 18th, 1996. The proposal should be presented in the form of a 200-word abstract, one page topical outline of the content, description of the proposers and their qualifications relating to the tutorial content. Tutorial Submission Deadlines: o Proposal Submission: July 31, 1996 o Acceptance Notification: August 31, 1996 o Complete Tutorial materials due: December 1, 1996 Send proposals to the KBCS-96 Secretariat at the address below. Organizing Committee: George Arakal, NCST (Chair) K.S.R. Anjaneyulu, NCST P. Ravi Prakash, NCST Durgesh D. Rao, NCST M. Sasikumar, NCST T. Suresh, NCST For further information please refer to the KBCS-96 home page or write to the KBCS-96 Secretariat. ___________________________________________________________________________ Address KBCS-96 Secretariat Phone : +91 (22) 620 1606 National Centre for Software Technology Fax : +91 (22) 621 0139 Gulmohar Cross Rd No. 9 E-mail : kbcs@konark.ncst.ernet.in Juhu, Bombay 400 049, India URL : http://konark.ncst.ernet.in/~kbcs/kbcs96.html ---------------------------------------------------------------------------- From owner-gann-list Thu May 30 15:52:20 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id PAA19939 for gann-list-outgoing; Thu, 30 May 1996 15:48:41 -0500 (CDT) Date: Thu, 30 May 96 16:45:21 EDT From: giles@research.nj.nec.com (Lee Giles) Message-Id: <9605302045.AA04882@alta> To: neuron@cattell.psych.upenn.edu, gann-list@cs.iastate.edu, ml@ics.uci.edu, INDUCTIVE@hermes.csd.unb.ca, gadistr@aic.nrl.navy.mil, EP-List@magenta.me.FAU.EDU, hybrid-list@cs.ua.edu, Connectionists@cs.cmu.edu Subject: GANN: Student summer job at NEC Research Institute Cc: giles@research.nj.nec.com Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: giles@research.nj.nec.com (Lee Giles) SUMMER RESEARCH POSITION Due to an unexpected situation, the NEC Research Institute in Princeton, NJ has an immediate opening for a student summer research position in the area of prediction methods applied to disk file organization. The successful candidate must have experience in research and be able to effectively communicate research results. He or she should have knowledge of prediction and time series modelling (e.g. markov modelling, etc. ) and operating systems (specifically disk file organization schemes). The ideal candidate would understand disk drive geometries and be capable of modifying a Linux kernel. Interested applicants should send their resumes by mail, fax or email to one of the following: Dr. C. Lee Giles Dr. Jim Philbin NEC Research Institute NEC Research Institute 4 Independence Way 4 Independence Way Princeton, NJ 08540 Princeton, NJ 08540 Phone: (609) 951-2642 Phone: (609) 951-2749 FAX: 609-951-2482 FAX: 609-951-2488 giles@research.nj.nec.com philbin@research.nj.nec.com http://www.neci.nj.nec.com Applicants must show DOCUMENTATION OF ELIGIBILITY FOR EMPLOYMENT. NEC is an equal opportunity employer. -- C. Lee Giles / Computer Sciences / NEC Research Institute / 4 Independence Way / Princeton, NJ 08540, USA / 609-951-2642 / Fax 2482 www.neci.nj.nec.com/homepages/giles.html == From owner-gann-list Thu May 30 16:54:31 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id QAA21923 for gann-list-outgoing; Thu, 30 May 1996 16:51:51 -0500 (CDT) Message-Id: Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Thu, 30 May 1996 14:50:46 +0100 To: connectionists@cs.cmu.edu, ml@ics.uci.edu, Reinforce@cs.uwa.edu.au, gann-list@cs.iastate.edu, corryfee@hasara11.bitnet, owner-csemlist@eco.utexas.edu.nonlin-l@list.nih.gov, comp-finance@teleport.com From: fisher@tweed.cse.ogi.edu (Therese Fisher) Subject: GANN: New Computational Finance Program Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: fisher@tweed.cse.ogi.edu (Therese Fisher) Computational Finance at Oregon Graduate of Institute of Science & Technology (OGI) A Concentration in the MS Programs of Computer Science & Engineering (CSE) Electrical Engineering & Applied Physics (EEAP) ---------------------------------------------------------------------------- 20000 NW Walker Road, PO Box 91000, Portland, OR 97291-1000 ---------------------------------------------------------------------------- Computational Finance at OGI is a 12-month intensive program leading to a Master of Science degree in Computer Science and Engineering (CSE track) or in Electrical Engineering & Applied Physics (EE track). The program features: * A 12 month intensive program to train scientists and engineers for doing state-of-the-art quantitative or information systems work in finance. * Provide an attractive alternative to the standard 2 year MBA for technically-sophisticated students. * Provide a solid foundation in finance. Cover three semesters of MBA level finance in three quarters, and go beyond that. * Provide a solid foundation in relevant techniques from CS and EE for modeling financial markets and developing investment analysis, trading, and risk management systems. * Give CS/EE graduates the necessary finance background to work as information system specialists in major financial firms. * Emphasize state-of-the-art techniques in neural networks, adaptive systems, signal processing, and data modeling. * Provide state-of-the-art computing facilities for doing course assignments using live and historical market data provided by Dow Jones Telerate. * Provide students an opportunity to do significant projects using extensive market data resources and state-of-the-art analysis packages, thereby making them more attractive to employers. * Through their course work and projects, students will develop significant expertise in using and programming important analysis packages, such as Mathematica, Matlab, SPlus, and Expo. ---------------------------------------------------------------------------- Major Components of Program: The curriculum includes 4 quarters with courses structured within the standard CSE/EEAP framework, with 5 courses in the finance specialty area, 7 or 8 core courses within the CSE or EEAP departments, and 3 electives. Students will enroll in either the CSE (CSE track) or EEAP (EE track) MS programs. ---------------------------------------------------------------------------- Admission Requirements & Contact Information ---------------------------------------------------------------------------- Admission requirements are the same as the general requirements of the institution. GRE scores are required for the 12-month concentration in Computational Finance, however they may be waived in special circumstances. A candidate must hold a bachelor's degree in computer science, engineering, mathematics, statistics, one of the biological or physical sciences, finance, or one of the quantitative social sciences. For more information, contact Computational Finance Betty Shannon, Academic Coordinator Computer Science and Engineering Department Oregon Graduate Institute of Science and Technology P.O.Box 91000 Portland, OR 97291-1000 E-mail: academic@cse.ogi.edu Phone: (503) 690-1255 or E-mail: CompFin@cse.ogi.edu WWW: http://www.cse.ogi.edu/CompFin/ From owner-gann-list Thu May 30 18:12:51 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id SAA24335 for gann-list-outgoing; Thu, 30 May 1996 18:10:36 -0500 (CDT) Date: Thu, 30 May 1996 16:08:21 -0700 (MST) From: Asim Roy Subject: GANN: Connectionist Learning - Some New Ideas/Questions To: gann-list@cs.iastate.edu Message-id: <01I5BMPV7TB68X2IH5@asu.edu> Content-transfer-encoding: 7BIT Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: Asim Roy (This is for posting to your mailing list.) This is an attempt to respond to some thoughts on one particular aspect of our learning theory - the one that requires connectionist/neural net algorithms to make an explicit "attempt" to build the smallest possible net (generalize, that is). One school of thought says that we should not attempt to build the smallest possible net because some extra neurons in the net (and their extra connections) provide the benefits of fault tolerance and reliability. And since the brain has access to billions of neurons, it does not really need to worry about a real resource constraint - it is practically an unlimited resource. (It is a fact of life, however, that at some age we do have difficulty memorizing and remembering things and learning- we perhaps run out of space (neurons) like a storage device on a computer. Even though billions of neurons is a large number, we must be using most of it at some age. So it is indeed a finite resource and some of it appears to be reused, like we reuse space on our storage devices. For memorization, for example, it is possible that the brain selectively erases some old memories to store some new ones. So a finite capacity system is a sensible view of the brain.) Another argument in favor of not trying to generalize is that by not worrying about attempting to create the smallest possible net, the connectionist algorithms are easier to develop and less complex. I hope researchers will come forward with other arguments in favor of not attempting to create the smallest possible net or to generalize. There is one main problem with the argument that adding lots of extra neurons to a net buys reliability and fault tolerance. First, we run the severe risk of "learning nothing" if we don't attempt to generalize. With lots of neurons available to a net, we would simply overfit the net to the problem data. (Try it next time on your back prop net. Add 10 or 100 times the number of hidden nodes you need and observe the results.) That is all we would achieve. Without good generalization, we may have a fault tolerant and reliable net, but it may be "useless" for all practical purposes because it may have "learnt nothing". Generalization is the fundamental part of learning - it perhaps should be the first learning criteria for our algorithms. We can't overlook or skip that part. If an algorithm doesn't attempt to generalize, it doesn't attempt to learn. It is as simple as that. So generalization needs to be our first priority and fault tolerance comes later. First we must "learn" something, then make it fault tolerant and reliable. Here is a practical viewpoint for our algorithms. Even though neurons are almost "unlimited" and free of cost to our brain, from a practical engineering stand point, "silicon" neurons are not so cheap. So our algorithms definitely need to be cost conscious and try to build the smallest possible net; they cannot be wasteful in their use of expensive "silicon" neurons. Once we obtain good generalization on a problem, fault tolerance can be achieved in many other ways. It would not hurt to examine the well established theory of reliability for some neat ideas. A few backup systems might be a more cost effective way to buy reliability than throwing in lots of extra silicon in a single system which may buy us nothing (it "learns nothing"). From controlling nuclear power plants with backup computer systems to adding extra tires in our trucks and buses, the backup idea works quite well. It is possible that "backup" is also what is used in our brains. We need to find out. "Redundancy" may be in the form of backup systems. "Repair" is another good idea used in our everyday lives for not so critical systems. Is fault tolerance and reliability sometimes achieved in the brain through the process of "repair"? Patients do recover memory and other brain functions after a stroke. Is that repair work by the biological system? It is a fact that biological systems are good at repairing things (look at simple things like cuts and bruises). We perhaps need to look closer at our biological systems and facts and get real good clues to how it works. Let us not jump to conclusions so quickly. Let us argue and debate with our facts. We will do our science a good service and be able to make real progress. I would welcome more thoughts and debate on this issue. I have included all of the previous responses on this particular issue for easy reference by the readers. I have also appended our earlier note on our learning theory. Perhaps more researchers will come forward with facts and ideas and enlighten all of us on this crucial question. ******************************************** On May 16 Kevin Cherkauer wrote: "In a recent thought-provoking posting to the connectionist list, Asim Roy said: >E. Generalization in Learning: The method must be able to >generalize reasonably well so that only a small amount of network >resources is used. That is, it must try to design the smallest possible >net, although it might not be able to do so every time. This must be >an explicit part of the algorithm. This property is based on the >notion that the brain could not be wasteful of its limited resources, >so it must be trying to design the smallest possible net for every >task. I disagree with this point. According to Hertz, Krogh, and Palmer (1991, p. 2), the human brain contains about 10^11 neurons. (They also state on p. 3 that "the axon of a typical neuron makes a few thousand synapses with other neurons," so we're looking at on the order of 10^14 "connections" in the brain.) Note that a period of 100 years contains only about 3x10^9 seconds. Thus, if you lived 100 years and learned continuously at a constant rate every second of your life, your brain would be at liberty to "use up" the capacity of about 30 neurons (and 30,000 connections) per second. I would guess this is a very conservative bound, because most of us probably spend quite a bit of time where we aren't learning at such a furious rate. But even using this conservative bound, I calculate that I'm allowed to use up about 2.7x10^6 neurons (and 2.7x10^9 connections) today. I'll try not to spend them all in one place. :-) Dr. Roy's suggestion that the brain must try "to design the smallest possible net for every task" because "the brain could not be wasteful of its limited resources" is unlikely, in my opinion. It seems to me that the brain has rather an abundance of neurons. On the other hand, finding optimal solutions to many interesting "real-world" problems is often very hard computationally. I am not a complexity theorist, but I will hazard to suggest that a constraint on neural systems to be optimal or near-optimal in their space usage is probably both impossible to realize and, in fact, unnecessary. Wild speculation: the brain may have so many neurons precisely so that it can afford to be suboptimal in its storage usage in order to avoid computational time intractability. References Hertz, J.; Krogh, A.; & Palmer, R.G. 1991. Introduction to the Theory of Neural Computation. Redwood City, CA:Addison-Wesley." ************************************************** On May 15 Richard Kenyon wrote on the subject of generalization: " The brain probably accepts some form of redundancy (waste). I agree that the brain is one hell of an optimisation machine. Intelligence whatever task it may be applied to is (again imho) one long optimisation process. Generalisation arises (even emerges or is a side effect) as a result of ongoing optimisation, conglomeration, reprocessing etc etc. This is again very important i agree, but i think (i do anyway) we in NN commumnity are aware of this as with much of the above. I thought that apart from point A we were doing all of this already, although to have it explicitly published is very valuable." ***************************************** On May 16 Lokendra Shastri replied to Kevin Cherkauer: "There is another way to look at the numbers. The retina provides 10^6 inputs to the brain every 200 msec! A simple n^2 algorithm to process this input would require more neurons than we have in our brain. We can understand (or at least process) a potentially unbounded number of sentences --- Here is one "the grandcanyon walked past the banana" I could have said anyone of a gazzilion sentences at this point and you would have probably understood it. Even if we just count the overt symbolic knowledge, we carry in our heads, we can enumerate about a million items. A coding scheme that consumed a 1000 neurons per item (which is not much) would soon run out neurons. Remember that a large fraction of our neurons are already taken up by sensorimotor processes (vision itself consumes a fair fraction of the brain).For an argument on the tight constraints posed by the "limited" number of neurons vis-a-vis common sense knowledge, you may want to see: ``From simple associations to systematic reasoning'', L. Shastri and V. Ajjanagadde. In Behavioral and Brain Sciences Vol. 16, No. 3, 417--494, 1993. My home page has a URL to a postscript version. There was also a nice paper by Tsotsos in Behavioral and Brains Sciences on this topic from the perspective of Visual Processing. Also you might want to see Feldman and Ballard 1982 paper in Cognitive Science." *********************************************** On May 17 Steven Small replied to Keven Cherkauer: "I agree with this general idea, although I'm not sure that "computational time intractability" is necessarily the principal reason. There are a lot of good reasons for redundancy, overlap, and space "suboptimality", not the least of which is the marvellous ability at recovery that the brain manifests after both small injuries and larger ones that give pause even to experienced neurologists." ************************************************* On May 17 Jonathan Stein replied to Steven Small and Kevin Cherkauer: "One needn't draw upon injuries to prove the point. One loses about 100,000 cortical neurons a day (about a percent of the original number every three years) under normal conditions. This loss is apparently not significant for brain function. This has been often called the strongest argument for distributed processing in the brain. Compare this ability with the fact that single conductor disconnection cause total system failure with high probability in conventional computers. Although certainly acknowledged by the pioneers of artificial neural network techniques, very few networks designed and trained by present techniques are anywhere near that robust. Studies carried out on the Hopfield model of associative memory DO show graceful degradation of memory capacity with synapse dilution under certain conditions (see eg. DJ Amit's book "Attractor Neural Networks"). Synapse pruning has been applied to trained feedforward networks (eg. LeCun's "Optimal Brain Damage") but requires retraining of the network." ****************************************** On May 18 Raj Rao replied to Kevin Cherkauer and Steven Small: " Does anyone have a concrete citation (a journal article) for this or any other similar estimate regarding the daily cell death rate in the cortex of a normal brain? I've read such numbers in a number of connectionist papers but none cite any neurophysiological studies that substantiate these numbers." ******************************************** On May 19 Richard Long wrote: "There may be another reason for the brain to construct networks that are 'minimal' having to do with Chaitin and Kolmogorov computational complexity. If a minimal network corresponds to a 'minimal algorithm' for implementing a particular computation, then that particular network must utilize all of the symmetries and regularities contained in the problem, or else these symmetries could be used to reduce the network further. Chaitin has shown that no algorithm for finding this minimal algorithm in the general case is possible. However, if an evolutionary programming method is used in which the fitness function is both 'solves the problem' and 'smallest size' (i.e. Occam's razor), then it is possible that the symmetries and regularities in the problem would be extracted as smaller and smaller networks are found. I would argue that such networks would compute the solution less by rote or brute force, and more from a deep understanding of the problem. I would like to hear anyone else's thoughts on this." ************************************************** On May 20 Juergen Schmidhuber replies to Richard Long: "Apparently, Kolmogorov was the first to show the impossibility of finding the minimal algorithm in the general case (but Solomonoff also mentions it in his early work). The reason is the halting problem, of course - you don't know the runtime of the minimal algorithm. For all practical applications, runtime has to be taken into account. Interestingly, there is an ``optimal'' way of doing this, namely Levin's universal search algorithm, which tests solution candidates in order of their Levin complexities: L. A. Levin. Universal sequential search problems, Problems of Information Transmission 9:3,265-266,1973. For finding Occam's razor neural networks with minimal Levin complexity, see J. Schmidhuber: Discovering solutions with low Kolmogorov complexity and high generalization capability. In A.Prieditis and S.Russell, editors, Machine Learning: Proceedings of the 12th International Conference, 488--496. Morgan Kaufmann Publishers, San Francisco, CA, 1995. For Occam's razor solutions of non-Markovian reinforcement learning tasks, see M. Wiering and J. Schmidhuber: Solving POMDPs using Levin search and EIRA.In Machine Learning: Proceedings of the 13th International Conference. Morgan Kaufmann Publishers, San Francisco, CA, 1996, to appear." ********************************************** On May 20 Sydney Lamb replied to Jonathan Stein and others: " There seems to be some differing information coming from different sources. The way I heard it, the typical person has lost only about 3% of the original total of cortical neurons after about 70 or 80 years. As for the argument about distributed processing, two comments: (1) there are different kinds of distributive processing; one of them also uses strict localization of points of convergence for distributed subnetworks of information (cf. A. Damasio 1989 --- several papers that year). (2) If the brain is like other biological systems, the neurons being lost are probably most the ones not being used --- ones that have been remaining latent and available to assume some function, but never called upon. Hence what you get with old age is not so much loss of information as loss of ability to learn new things --- varying in amount, of course, from one individual to the next." ***************************************** On May 20 Mark Johnson replies to Raj Rao: "From my reading of the recent literature massive postnatal cell loss in the human cortex is a myth. There is postnatal cortical cell death in rodents, but in primates (including humans) there is only (i) a decreased density of cell packing, and (ii) massive (up to 50%) synapse loss. (The decreased density of cell packing was apparently misinterpreted as cell loss in the past). Of course, there are pathological cases, such as Alzheimers, in which there is cell loss. I have written a review of human postnatal brain development which I can send out on request." ************************************************** *************************************************** APPENDIX We have recently published a set of principles for learning in neural networks/connectionist models that is different from classical connectionist learning (Neural Networks, Vol. 8, No. 2; IEEE Transactions on Neural Networks, to appear; see references below). Below is a brief summary of the new learning theory and why we think classical connectionist learning, which is characterized by pre-defined nets, local learning laws and memoryless learning (no storing of training examples for learning), is not brain-like at all. Since vigorous and open debate is very healthy for a scientific field, we invite comments for and against our ideas from all sides. "A New Theory for Learning in Connectionist Models" We believe that a good rigorous theory for artificial neural networks/connectionist models should include learning methods that perform the following tasks or adhere to the following criteria: A. Perform Network Design Task: A neural network/connectionist learning method must be able to design an appropriate network for a given problem, since, in general, it is a task performed by the brain. A pre-designed net should not be provided to the method as part of its external input, since it never is an external input to the brain. From a neuroengineering and neuroscience point of view, this is an essential property for any "stand-alone" learning system - a system that is expected to learn "on its own" without any external design assistance. B. Robustness in Learning: The method must be robust so as not to have the local minima problem, the problems of oscillation and catastrophic forgetting, the problem of recall or lost memories and similar learning difficulties. Some people might argue that ordinary brains, and particularly those with learning disabilities, do exhibit such problems and that these learning requirements are the attributes only of a "super" brain. The goal of neuroengineers and neuroscientists is to design and build learning systems that are robust, reliable and powerful. They have no interest in creating weak and problematic learning devices that need constant attention and intervention. C. Quickness in Learning: The method must be quick in its learning and learn rapidly from only a few examples, much as humans do. For example, one which learns from only 10 examples learns faster than one which requires a 100 or a 1000 examples. We have shown that on-line learning (see references below), when not allowed to store training examples in memory, can be extremely slow in learning - that is, would require many more examples to learn a given task compared to methods that use memory to remember training examples. It is not desirable that a neural network/connectionist learning system be similar in characteristics to learners characterized by such sayings as "Told him a million times and he still doesn't understand." On-line learning systems must learn rapidly from only a few examples. D. Efficiency in Learning: The method must be computationally efficient in its learning when provided with a finite number of training examples (Minsky and Papert[1988]). It must be able to both design and train an appropriate net in polynomial time. That is, given P examples, the learning time (i.e. both design and training time) should be a polynomial function of P. This, again, is a critical computational property from a neuroengineering and neuroscience point of view. This property has its origins in the belief that biological systems (insects, birds for example) could not be solving NP-hard problems, especially when efficient, polynomial time learning methods can conceivably be designed and developed. E. Generalization in Learning: The method must be able to generalize reasonably well so that only a small amount of network resources is used. That is, it must try to design the smallest possible net, although it might not be able to do so every time. This must be an explicit part of the algorithm. This property is based on the notion that the brain could not be wasteful of its limited resources, so it must be trying to design the smallest possible net for every task. General Comments This theory defines algorithmic characteristics that are obviously much more brain-like than those of classical connectionist theory, which is characterized by pre-defined nets, local learning laws and memoryless learning (no storing of actual training examples for learning). Judging by the above characteristics, classical connectionist learning is not very powerful or robust. First of all, it does not even address the issue of network design, a task that should be central to any neural network/connectionist learning theory. It is also plagued by efficiency (lack of polynomial time complexity, need for excessive number of teaching examples) and robustness problems (local minima, oscillation, catastrophic forgetting, lost memories), problems that are partly acquired from its attempt to learn without using memory. Classical connectionist learning, therefore, is not very brain-like at all. As far as I know, there is no biological evidence for any of the premises of classical connectionist learning. Without having to reach into biology, simple common sense arguments can show that the ideas of local learning, memoryless learning and predefined nets are impractical even for the brain! For example, the idea of local learning requires a predefined network. Classical connectionist learning forgot to ask a very fundamental question - who designs the net for the brain? The answer is very simple: Who else, but the brain itself! So, who should construct the net for a neural net algorithm? The answer again is very simple: Who else, but the algorithm itself! (By the way, this is not a criticism of constructive algorithms that do design nets.) Under classical connectionist learning, a net has to be constructed (by someone, somehow - but not by the algorithm!) prior to having seen a single training example! I cannot imagine any system, biological or otherwise, being able to construct a net with zero information about the problem to be solved and with no knowledge of the complexity of the problem. (Again, this is not a criticism of constructive algorithms.) A good test for a so-called "brain-like" algorithm is to imagine it actually being part of a human brain. Then examine the learning phenomenon of the algorithm and compare it with that of the human's. For example, pose the following question: If an algorithm like back propagation is "planted" in the brain, how will it behave? Will it be similar to human behavior in every way? Look at the following simple "model/algorithm" phenomenon when the back- propagation algorithm is "fitted" to a human brain. You give it a few learning examples for a simple problem and after a while this "back prop fitted" brain says: "I am stuck in a local minimum. I need to relearn this problem. Start over again." And you ask: "Which examples should I go over again?" And this "back prop fitted" brain replies: "You need to go over all of them. I don't remember anything you told me." So you go over the teaching examples again. And let's say it gets stuck in a local minimum again and, as usual, does not remember any of the past examples. So you provide the teaching examples again and this process is repeated a few times until it learns properly. The obvious questions are as follows: Is "not remembering" any of the learning examples a brain- like phenomenon? Are the interactions with this so-called "brain- like" algorithm similar to what one would actually encounter with a human in a similar situation? If the interactions are not similar, then the algorithm is not brain-like. A so-called brain-like algorithm's interactions with the external world/teacher cannot be different from that of the human. In the context of this example, it should be noted that storing/remembering relevant facts and examples is very much a natural part of the human learning process. Without the ability to store and recall facts/information and discuss, compare and argue about them, our ability to learn would be in serious jeopardy. Information storage facilitates mental comparison of facts and information and is an integral part of rapid and efficient learning. It is not biologically justified when "brain-like" algorithms disallow usage of memory to store relevant information. Another typical phenomenon of classical connectionist learning is the "external tweaking" of algorithms. How many times do we "externally tweak" the brain (e.g. adjust the net, try a different parameter setting) for it to learn? Interactions with a brain-like algorithm has to be brain-like indeed in all respect. The learning scheme postulated above does not specify how learning is to take place - that is, whether memory is to be used or not to store training examples for learning, or whether learning is to be through local learning at each node in the net or through some global mechanism. It merely defines broad computational characteristics and tasks (i.e. fundamental learning principles) that are brain-like and that all neural network/connectionist algorithms should follow. But there is complete freedom otherwise in designing the algorithms themselves. We have shown that robust, reliable learning algorithms can indeed be developed that satisfy these learning principles (see references below). Many constructive algorithms satisfy many of the learning principles defined above. They can, perhaps, be modified to satisfy all of the learning principles. The learning theory above defines computational and learning characteristics that have always been desired by the neural network/connectionist field. It is difficult to argue that these characteristics are not "desirable," especially for self-learning, self- contained systems. For neuroscientists and neuroengineers, it should open the door to development of brain-like systems they have always wanted - those that can learn on their own without any external intervention or assistance, much like the brain. It essentially tries to redefine the nature of algorithms considered to be brain- like. And it defines the foundations for developing truly self- learning systems - ones that wouldn't require constant intervention and tweaking by external agents (human experts) for it to learn. It is perhaps time to reexamine the foundations of the neural network/connectionist field. This mailing list/newsletter provides an excellent opportunity for participation by all concerned throughout the world. I am looking forward to a lively debate on these matters. That is how a scientific field makes real progress. Asim Roy Arizona State University Tempe, Arizona 85287-3606, USA Email: ataxr@asuvm.inre.asu.edu References 1. Roy, A., Govil, S. & Miranda, R. 1995. A Neural Network Learning Theory and a Polynomial Time RBF Algorithm. IEEE Transactions on Neural Networks, to appear. 2. Roy, A., Govil, S. & Miranda, R. 1995. An Algorithm to Generate Radial Basis Function (RBF)-like Nets for Classification Problems. Neural Networks, Vol. 8, No. 2, pp. 179-202. 3. Roy, A., Kim, L.S. & Mukhopadhyay, S. 1993. A Polynomial Time Algorithm for the Construction and Training of a Class of Multilayer Perceptrons. Neural Networks, Vol. 6, No. 4, pp. 535- 545. 4. Mukhopadhyay, S., Roy, A., Kim, L.S. & Govil, S. 1993. A Polynomial Time Algorithm for Generating Neural Networks for Pattern Classification - its Stability Properties and Some Test Results. Neural Computation, Vol. 5, No. 2, pp. 225-238. From owner-gann-list Fri May 31 19:26:27 1996 Received: (from mdomo@localhost) by cs.iastate.edu (8.7.4/8.7.1) id TAA19193 for gann-list-outgoing; Fri, 31 May 1996 19:15:19 -0500 (CDT) Date: Fri, 31 May 1996 18:16:39 -0600 From: svc@lacasa.com (Stephen V. Coggeshall) Message-Id: <199606010016.SAA02128@ives.lacasa.com> To: gann-list@cs.iastate.edu Subject: GANN: Message for distribution Mime-Version: 1.0 Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit Content-MD5: CXx1FA4vv5DjEjr4JqcBrg== Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Reply-To: svc@lacasa.com (Stephen V. Coggeshall) A small start-up company is looking for the right employees to work on a variety of problems in financial and other modeling. The Center for Adaptive Systems Applications (CASA, ~20 employees) has been in existence since June, 95, and is a result of a spin-off from the Los Alamos National Laboratory. The company is based in Los Alamos, NM. 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