From owner-gann-list Tue May 2 12:32:45 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id MAA28282 for gann-list-outgoing; Tue, 2 May 1995 12:05:40 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id MAA28269 for gann-list@cs.iastate.edu; Tue, 2 May 1995 12:05:33 -0500 From: GANN List Admin Reply-To: raffaele@caio.irmkant.rm.cnr.it X-Originator: raffaele@caio.irmkant.rm.cnr.it Date: Tue, 2 May 1995 16:50:02 -0500 Message-Id: <9505022150.AA13740@caio.irmkant.rm.cnr.it> To: gann-list@cs.iastate.edu Subject: simulation of protein folding process (paper) Sender: owner-gann-list@cs.iastate.edu Precedence: bulk sent on 2/5/95 at 5 p.m to: connectionists gann biosci: computational biology molecular modelling ------------------------------ FTP-host: kant.irmkant.rm.cnr.it FTP-filename: /pub/econets/calabretta.folding.ps.Z The following paper has been placed in the anonymous-ftp archive (see above for ftp-host) and is now available as a compressed postscript file named calabretta.folding.ps.Z (14 pages of output) The paper is also available by World Wide Web: http://kant.irmkant.rm.cnr.it/gral.html It will appear in Proceedings of 3rd European Conference on Artificial Life (Granada, Spain, 4-6 June 1995). Comments welcome. Raffaele Calabretta email address: raffaele@caio.irmkant.rm.cnr.it ------------------------------------------------------------------ "An Artificial Model for Predicting the Tertiary Structure of Unknown Proteins that Emulates the Folding Process" Raffaele Calabretta, Stefano Nolfi, Domenico Parisi Department of Neural Systems and Artificial Life Institute of Psychology National Research Council V.le Marx, 15 00137 ROME ITALY ---------------------------------------------------------------------------- Abstract: We present an "ab initio" method that tries to determine the tertiary structure of unknown proteins by modelling the folding process without using potentials extracted from known protein structures. We have been able to obtain appropriate matrices of folding potentials, i.e. 'forces' able to drive the folding process to produce correct tertiary structures, using a genetic algorithm. Some initial simulations that try to simulate the folding process of a fragment of the crambin that results in an alpha- helix, have yielded good results. We discuss some general implications of an Artificial Life approach to protein folding which makes an attempt at simulating the actual folding process rather than just trying to predict its final result. ---------------------------------------------------------------------------- From owner-gann-list Wed May 3 21:32:31 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id VAA04555 for gann-list-outgoing; Wed, 3 May 1995 21:03:08 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id VAA04540 for gann-list@cs.iastate.edu; Wed, 3 May 1995 21:03:05 -0500 From: GANN List Admin Reply-To: Robert Elliott Smith X-Originator: Robert Elliott Smith Message-Id: <9505031334.AA16118@comec4.mh.ua.edu> To: ml@ics.uci.edu, psych%tcsvm.bitnet@cunyvm.cuny.edu, news-announce-conferences@uunet.uu.net, neuron@hplabs.hpl.hp.com, biosci@presto.ig.com, ETHOLOGY%FINHUTC.BITNET@cunyvm.cuny.edu, neuro-evolution@cse.ogi.edu, alife@cognet.ucla.edu, neuron@hplabs.hpl.hp.com, biosci%net.bio.net@vm1.nodak.edu, ga-list@aic.nrl.navy.mil, Connectionists@CS.CMU.EDU, simulation@bikini.cis.ufl.edu, IE-list@cs.ucl.ac.uk, EP-List@magenta.me.fau.edu, genetic-programming@CS.Stanford.EDU, gann@cs.iastate.edu, neuron-request@cattell.psych.upenn.edu, reinforce@cs.uwa.edu.au Subject: Papers to be presented at ICGA6 Date: Wed, 03 May 95 08:34:15 -0600 Sender: owner-gann-list@cs.iastate.edu Precedence: bulk The organizers of the Sixth International Conference on Genetic Algorithms, to be held in Pittsburgh, PA, July 15-19, 1995, are please to present the following list of papers that will be presented at the conference. This list is followed by registration information for the conference. =================== ICGA-95: PAPERS ACCECPTED FOR PRESENTATION SELECTION Generalized Convergence Models for Tournament- and (mu,lambda)-Selection Thomas Baeck A Mathematical Analysis of Tournament Selection Tobias Blickle, Lothar Thiele On Decentralizing Selection Algorithms Kenneth De Jong, Jayshree Sarma Finding Multimodal Solutions Using Restricted Tournament Selection Georges Harik Analysis of Genetic Algorithms Evolution under Pure Selection Filippo Neri, Lorenza Saitta MUTATION AND RECOMBINATION A New Class of Crossover Operators for Numerical Optimization Jaroslaw Arabas, Jan J. Mulawka, Jacek Pokrasniewicz On Multi-Dimensional Encoding/Crossover Thang N. Bui, Byung-Ro Moon On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation Nikolaus Hansen, Andreas Ostermeier, Andreas Gawelczyk The Nature of Mutation in Genetic Algorithms Robert Hinterding, Harry Gielewski, T. C. Peachey Crossover, Macromutation, and Population-based Search Terry Jones What Have You Done for Me Lately? Adapting Operator Probabilities in a Steady-State Genetic Algorithm Bryant A. Julstrom Metabits: Generic Endogenous Crossover Control Jim Levenick Toward More Powerful Recombinations Byung Ro Moon, Andrew B. Kahng Fuzzy Recombination for the Continuous Breeder Genetic Algorithm H.-M. Voigt, H. Muhlenbein, D. Cvetkovic EVOLUTIONARY COMPUTATION TECHNIQUES The Distributed Genetic Algorithm Revisited Theodore C. Belding Solving Constraint Satisfaction Problems Using a Genetic/Systematic Search James Bowen, Gerry Dozier Enhancing GA Performance Through Incest Prohibitions Based on Ancestry Robert Craighurst, Worthy Martin A Comparison of Parallel and Sequential Niching Methods Samir W. Mahfoud Selectively Destructive Re-start Jonathan Maresky, Yuval Davidor, Daniel Gitler, Gad Aharoni, Amnon Barak Genetic Algorithms, Numerical Optimization, and Constraints Zbigniew Michalewicz, Sita S. Raghavan Function Optimization Khim Peow Ng, Kok Cheong Wong When Seduction Meets Selection Edmund Ronald Population-Oriented Simulated Annealing: A Genetic/Thermodynamic Hybrid Approach to Optimization James M. Varanelli, James P. Cohoon FORMAL ANALYSIS OF EVOLUTIONARY COMPUTATION AND PROBLEM DIFFICULTY Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms Terry Jones, Stephanie Forrest Signal-to-noise, Crosstalk and Long Range Problem Difficulty in Genetic Algorithms Hillol Kargupta Efficient Tracing of the Behaviour of Genetic Algorithms using Expected Values of Bit and Walsh Products J.N. Kok, P. Floreen Optimization Using Replicators Anil Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka Epistasis in Genetic Algorithms: An Experimental Design Perspective Colin Reeves, Christine Wright Epistasis in Periodic Programs Stefan Voget Hyperplane Ranking in Simple Genetic Algorithms Darrell Whitley, Keith Mathias, Larry Pyeatt Building Better Test Functions D. Whitley, K. Mathias, S. Rana, J Dzubera GENETIC PROGRAMMING The Evolution of Agents that Build Mental Models and Create Simple Plans Using Genetic Programming Causality in Genetic Programming Dana H. Ballard, Justinian Rosca Solving Complex Problems with Genetic Algorithms Bertrand Daniel Dunay, Frederic E. Petry Strongly Typed Genetic Programming in Evolving Cooperation Strategies Thoms Haynes, Roger L. Wainwright, Sandip Sen, Dale A. Schoenefeld Temporal Data Processing Using Genetic Programming Hitoshi Iba, Hugo de Garis, Taisuke Sato Two Ways of Discovering the Size and Shape of a Computer Program to Solve a Problem John R. Koza Evolving Data Structures Using Genetic Programming W.B. Langdon Accurate Replication in Genetic Programming Nicholas Freitag McPhee, Justin Darwin Miller Complexity Compression and Evolution Peter Nordin, Wolfgang Banzhaf Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code Peter Nordin, Wolfgang Banzhaf CO-EVOLUTION AND EMERGENT ORGANIZATION Biological Symbiosis as a Metaphor for Computational Hybridization Jason M. Daida, Steven J. Ross, Brian C. Hannan Evolving Globally Synchronized Cellular Automata Rajarshi Das, James P. Crutchfield, Melanie Mitchell, James E. Hanson The Evolution of Emergent Organization in Immune System Gene Libraries Ron Hightower, Stephanie Forrest, Alan S. Perelson Co-evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces Hugues Juille The Symbiotic Evolution of Solutions and their Representations Jan Paredis A Coevolutionary Approach to Learning Sequential Decision Rules Mitchell A. Potter, Kenneth A. De Jong, John J. Grefenstette Methods for Competitive Co-evolution: Finding Opponents Worth Beating Christopher D. Rosin, Richard K. Belew EVOLUTIONARY COMPUTATION IN COMBINATION WITH MACHINE LEARNING OR NEURAL NETS Evolution in Multi-agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrapedal Robot Lawrence Bull, Terrence C. Fogarty Adaptive Distributed Routing using Evolutionary Fuzzy Control Brian Carse, Terry Fogarty, Alistair Munro Relational Schemata: A Way to Improve the Expressiveness of Classifiers Philippe Collard, Cathy Escazut The Mating Pool: A Testbed for Experiments in the Evolution of Symbol Systems Genetic Algorithm Enlarges the Capacity of Associative Memory Akira Imada, Keijiro Araki A Genetic Algorithm for Optimizing Fuzzy Decision Trees Cezary Z. Janikow PLEASE: A Prototype Learning System using Genetic Algorithms Leslie Knight, Sandip Sen A Parallel Genetic Algorithm for Concept Learning Filippo Neri, Attilio Giordana Evolutionary Grown Semi-Weighted Neural Networks Steve G. Romaniuk Combining Genetic Algorithms with Memory Based Reasoning John W. Sheppard, Steven L. Salzberg Cellular Encoding Applied to Neurocontrol Darrell Whitley, Frederic Gruau, Larry Pyeatt EVOLUTIONARY COMPUTATION APPLICATIONS I Determining Factorization: A New Encoding Scheme for Spanning Trees Applied to the Probabilistic Minimum Spanning Tree Problem Faris N. Abuali, Roger L. Wainwright, Dale A. Schoenefeld Thang Nguyen Bui, Paul H. Eppley Finding (Near-)Optimal Steiner Trees in Large Graphs Henrik Esbensen Solving Equal Piles with the Grouping Genetic Algorithm Emanuel Falkenauer A Study of Genetic Algorithm Hybrids for Facility Layout Problems Kazuhiro Kado, Dave Corne, Peter Ross An Efficient Genetic Algorithm for Job Shop Scheduling Problems Shigenobu Kobayashi, Isao Ono, Masayuki Yamamura A Comparative Study of Genetic Search Kihong Park Inference of Stochastic Regular Grammars by Massively Parallel Genetic Algorithms Markus Schwehm, Alexander Ost Genetic Algorithm Approach to the Search for Golomb Rulers Stephen W. Soliday, Abdollah Homaifar, Gary L. Lebby An Adaptive Clustering Method using a Geometric Shape for Vehicle Routing Problems with Time Windows Sam R. Thangiah EVOLUTIONARY COMPUTATION APPLICATIONS II Applying Genetic Algorithms to Outlier Detection Kelly D. Crawford, Roger L. Wainwright Design of Statistical Quality Control Procedures Using Genetic Algorithms Aristides T. Hatjimihail, Theophanes T. Hatjimihail A Segregated Genetic Algorithm for Constrained Structural Optimization R. Le Riche, C. Knopf-Lenoir, R.T. Haftka A Preliminary Study of Genetic Data Compression Wee K. Ng A Standard GA Approach to Native Protein Conformation Prediction Arnold L. Patton, W. F. Punch, III, E. D. Goodman Using GAs to Characterize Workloads Chrisila C. Pettey, Thomas D. Wagner, Lawrence W. Dowdy Development of the Genetic Function Approximation Algorithm A Parallel Genetic Algorithm for Multi-objective Microprocessor Design Timothy J. Stanley, Trevor Mudge Rupert Weare, Edmund Burke, Dave Ellilman Evolutionary Computation in Air Traffic Control Planning C.H.M. van Kemenade, C.F.W. Hendriks, J.N. Kok Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses Frank Vavak, Terence C. Fogarty, Philip Cheng ========= Registration Information: 6TH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS July 15-19, 1995 University of Pittsburgh Pittsburgh, Pennsylvania, USA CONFERENCE COMMITTEE Stephen F. Smith, Chair Carnegie Mellon University Peter J. Angeline, Finance Loral Federal Systems Larry J. Eshelman, Program Philips Laboratories Terry Fogarty, Tutorials University of the West of England, Bristol Alan C. Schultz, Workshops Naval Research Laboratory Alice E. Smith, Local Arrangements University of Pittsburgh Robert E. Smith, Publicity University of Alabama The 6th International Conference on Genetic Algorithms (ICGA-95) brings together an international community from academia, government, and industry interested in algorithms suggested by the evolutionary process of natural selection, and will include pre-conference tutorials, invited speakers, and workshops. Topics will include: genetic algorithms and classifier systems, evolution strategies, and other forms of evolutionary computation; machine learning and optimization using these methods, their relations to other learning paradigms (e.g., neural networks and simulated annealing), and mathematical descriptions of their behavior. The conference host for 1995 will be the University of Pittsburgh located in Pittsburgh, Pennsylvania. The conference will begin Saturday afternoon, July 15, for those who plan on attending the tutorials. A reception is planned for Saturday evening. The conference meeting will begin Sunday morning July 16 and end Wednesday afternoon, July 19. The complete conference program and schedule will be sent later to those who register. TUTORIALS ICGA-95 will begin with three parallel sessions of tutorials on Saturday. Conference attendees may attend up to three tutorials (one from each session) for a supplementary fee (see registration form). Tutorial Session I 11:00 a.m.-12:30 p.m. I.A Introduction to Genetic Algorithms Melanie Mitchell - A brief history of Evolutionary Computation. The appeal of evolution. Search spaces and fitness landscapes. Elements of Genetic Algorithms. A Simple GA. GAs versus traditional search methods. Overview of GA applications. Brief case studies of GAs applied to: the Prisoner's Dilemma, Sorting Networks, Neural Networks, and Cellular Automata. How and why do GAs work? I.B Application of Genetic Algorithms Lawrence Davis - There are hundreds of real-world applications of genetic algorithms, and a considerable body of engineering expertise has grown up as a result. This tutorial will describe many of those principles, and present case studies demonstrating their use. I.C Genetics-Based Machine Learning Robert Smith - This tutorial discusses rule-based, neural, and fuzzy techniques that utilize GAs for exploration in the context reinforcement learning control. A rule-based technique, the learning classifier system (LCS), is shown to be analogous to a neural network. The integration of fuzzy logic into the LCS is also discussed. Research issues related to GA-based learning are overviewed. The application potential for genetics-based machine learning is discussed. Tutorial Session II 1:30-3:00 p.m. II.A Basic Genetic Algorithm Theory Darrell Whitley - Hyperplane Partitions and the Schema Theorem. Binary and Nonbinary Representations; Gray coding, Static hyperplane averages, Dynamic hyperplane averages and Deception, the K-armed bandit analogy and Hyperplane ranking. II.B Basic Genetic Programming John Koza - Genetic Programming is an extension of the genetic algorithm in which populations of computer programs are evolved to solve problems. The tutorial explains how crossover is done on program trees and illustrates how the user goes about applying genetic programming to various problems of different types from different fields. Multi-part programs and automatically defined functions are briefly introduced. II.C Evolutionary Programming 1960s, has recently been successfully applied to difficult, diverse real-world problems. This tutorial will provide information on the history, theory, and practice of evolutionary programming. Case-studies and comparisons will be presented. Tutorial Session III 3:30-5:00 p.m. III.A Advanced Genetic Algorithm Theory Darrell Whitley - Exact Non-Markov models of simple genetic algorithms. Markov models of simple genetic algorithms. The Schema Theorem and Price's Theorem. Convergence Proofs, Exact Non-Markov models for permutation based representations. III.B Advanced Genetic Programming John Koza - The emphasis is on evolving multi-part programs containing reusable automatically defined functions in order to exploit the regularities of problem environments. ADFs may improve performance, improve parsimony, and provide scalability. Recursive ADFs, iteration-performing branches, various types of memories (including indexed memory and mental models), architecturally diverse populations, and point typing are explained. III.C Evolution Strategies Hans-Paul Schwefel and Thomas Baeck - Evolution Strategies in the context of their historical origin for optimization in Berlin in the 1960s. Comparison of the computer-versions (1+1) and (10,100) ES with classical optimum seeking methods for parameter optimization. Formal descriptions of ES. Global convergence conditions. Time efficiency in some simple situations. The role of recombination. Auto-adaptation of internal models of the environment. Multi-criteria optimization. Parallel versions. Short list of application examples. GETTING TO PITTSBURGH The Pittsburgh International Airport is served by most of the major airlines. Information on transportation from the airport and directions to the University of Pittsburgh campus, will be sent along with your conference registration confirmation letter. LODGING University Holiday Inn, 100 Lytton Avenue two blocks from convention site $92/day (single) $9 /day parking charge pool (indoor), exercise facilities Reserve by June 18. Call 412-682-6200. Hampton Inn, 3315 Hamlet Street 12 blocks from convention site $72/day (single) free parking, breakfast, and one-way airport transportation Reserve by July 1. Call 412-681-1000. Howard Johnson's, 3401 Boulevard of the Allies 12 blocks from convention site $56/day (single) free parking and Oakland transportation pool (outdoor) Reserve by June 13. Call 412-683-6100. Sutherland Hall (dorm), University Drive-Pitt campus 10 blocks from convention site (steep hill) $30/day, single no amenities (phone, TV, etc.) shared bathroom Reserve by July 1. Call 412-648-1100. CONFERENCE FEES REGISTRATION FEE Registrations received by June 11 are $250 for participants and $100 for students. Registrations received on or after June 12 and walk-in registrations at the conference will be $295 for participants and $125 for students. Included in the registration fee are entry to all technical sessions, several lunches, coffee breaks, reception Saturday evening, conference materials, and conference proceedings. TUTORIALS There is a separate fee for the Saturday tutorial sessions. Attendees may register for up to three tutorials (one from each tutorial session). The fee for one tutorial is $40 for participants and $15 for students; two tutorials, $75 for participants and $25 for students; three tutorials, $110 for participants and $35 for students. The deadline to register without a late fee is June 11. After this date, participants and students will be assessed a flat $20 late fee, whether they register for one, two, or all three tutorials. CONFERENCE BANQUET Not included in the registration fee is the ticket for the banquet. Participants may purchase banquet tickets for an additional $30. Note - Please purchase your banquet tickets nowQyou will be unable to buy them upon arrival. GUEST TICKETS Guest tickets for the Saturday evening reception are $10 each; guest tickets for the conference banquet are $30 each for adults and $10 each for children. Note - Please purchase additional tickets now - you will be unable to buy them upon arrival. CANCELLATION/REFUND POLICY For cancellations received up to and including June 1, a full refund will be given minus a $25 handling fee. FINANCIAL ASSISTANCE FOR STUDENTS With support from the Naval Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, a limited fund has been set aside to assist students with travel expenses. Students should have their advisor certify their student status and that sufficient funds are not available. Students interested in obtaining such assistance should send a letter before May 22 describing their situation and needs to: Peter J. Angeline, c/o Advanced Technologies Dept, Loral Federal Systems, State Route 17C, Mail Drop 0210, Owego, NY 13827-3994 USA. TO REGISTER Early registration is recommended. You may register by mail, fax, or email using a credit card (MasterCard or VISA). You may also pay by check if registering by mail. Note: Students must also send with their registration a Complete the registration form and return with payment. If more than one registrant from the same institution will be attending, make additional copies of the registration form. Mail ICGA 95 Department of Industrial Engineering University of Pittsburgh 1048 Benedum Hall Pittsburgh, PA 15261 USA Fax Fax the registration form to 412-624-9831 Email Receive email form by contacting: icga@engrng.pitt.edu Up-to-date conference information is available on the World Wide Web (WWW) http://www.aic.nrl.navy.mil/galist/icga95/ CALL FOR ICGA '95 WORKSHOP PROPOSALS ICGA workshop proposals are now being solicited. Workshops tend to range from informal sessions to more formal sessions with presentations and working notes. Each accepted workshop will be supplied with space and an overhead projector. VCRs might be available. If you are interested in organizing a workshop, send a workshop title, short description, proposed format, and name of the organizers to the workshop coordinator by April 15, 1995. Alan C. Schultz - schultz@aic.nrl.navy.mil Code 5510, Navy Center for Artificial Intelligence Naval Research Laboratory Washington DC 30375-5337 USA REGISTRATION FORM Prof / Dr / Mr / Ms / Mrs Name ______________________________________________________ Last First MI I would like my name tag to read _____________________________________________ Affiliation/Business ______________________________________________________ Address ______________________________________________________ City ______________________________________________________ State ___________________ Zip ________________________ Country_____________________________________________ Telephone (include area code) Business _______________________________ Home______________________________ FEES (all figures in US dollars) Conference Registration Fee By June 11 ___ participant, $250 ___ student, $100 =$_________ On or after June 12 ___ participant, $295 ___ student, $125 =$_________ July 15 Tutorials Select up to three tutorials, but no more than one tutorial per tutorial session. Tutorial Session I: ___I.A Introduction to Genetic Algorithms ___I.B Application of Genetic Algorithms ___I.C Genetics-Based Machine Learning Tutorial Session II: ___II.A Basic Genetic Algorithm Theory ___II.B Basic Genetic Programming ___II.C Evolutionary Programming Tutorial Session III: ___III.A Advanced Genetic Algorithm Theory ___III.B Advanced Genetic Programming ___III.C Evolution Strategies Tutorial Registration Fee By June 11 ___one tutorial: participant, $40 student, $15 ___two tutorials: participant, $75 student, $25 = $_________ ___three tutorials: participant, $110 student, $35 On or after June 12, participants and students add a $20 late fee for tutorials = $_________ Banquet Ticket (not included in the Registration Fee; no tickets may be purchased upon arrival) participants/adult guest #______ ticket(s) @ $30 = $_________ child #______ ticket(s) @ $10 = $_________ Additional Saturday reception tickets (no tickets may be purchased upon arrival) guest #______ ticket(s) @ $10 = $_________ TOTAL (US dollars) $____________ METHOD OF PAYMENT ___ Check (payable to the University of Pittsburgh, US banks only) ___ MasterCard ___ VISA #__________________________________________ Expiration Date ____________________ Signature of card holder ______________________________________________ Note: Students must submit with their registration a photocopy of their Mail ICGA 95, Department of Industrial Engineering, University of Pittsburgh, 1048 Benedum Hall, Pittsburgh, PA 15261 USA Fax 412-624-9831 Email To receive email form: icga@engrng.pitt.edu World Wide Web (WWW) For up-to-date conference information: http://www.aic.nrl.navy.mil/galist/icga95/ ------------------------------------------- Robert Elliott Smith Department of Engineering Science and Mechanics Room 210 Hardaway Hall The University of Alabama Box 870278 Tuscaloosa, Alabama 35487 <> rob@comec4.mh.ua.edu <> (205) 348-1618 <> (205) 348-7240 <> http://hamton.eng.ua.edu/college/home/mh/faculty/rsmith/Web/smith.html ------------------------------------------- From owner-gann-list Tue May 9 12:59:16 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id MAA06879 for gann-list-outgoing; Tue, 9 May 1995 12:35:47 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id MAA06872 for gann-list@cs.iastate.edu; Tue, 9 May 1995 12:35:43 -0500 From: GANN List Admin Reply-To: Gerhard Paass X-Originator: Gerhard Paass (5.65c8/IDA-1.4.4 for ); Mon, 8 May 1995 17:29:21 +0200 (5.67b8/IDA-1.5); Mon, 8 May 1995 17:29:02 +0200 Date: Mon, 8 May 1995 17:29:02 +0200 Message-Id: <199505081529.AA02663@sein.gmd.de> To: gann-list@cs.iastate.edu, ml@ics.uci.edu Subject: CFP: Autumn School in Connectionism and Neural Networks, Muenster Sender: owner-gann-list@cs.iastate.edu Precedence: bulk CALL FOR PARTICIPATION ================================================================= = = = H e K o N N 9 5 = = = Autumn School in C o n n e c t i o n i s m and N e u r a l N e t w o r k s October 2-6, 1995 Muenster, Germany Conference Language: German ---------------------------------------------------------------- A comprehensive description of the Autumn School together with abstracts of the courses can be found at the following addresses: WWW: http://borneo.gmd.de/~hekonn anonymous FTP: ftp.gmd.de directory: Learning/neural/hekonn95 = = = O V E R V I E W = = = Artificial neural networks (ANN's) have in recent years been discussed in many diverse areas, ranging from the modelling of learning in the cortex to the control of industrial processes. The goal of the Autumn School in Connectionism and Neural Networks is to give a comprehensive introduction to conectionism and artificial neural networks and to give an overview of the current state of the art. Courses will be offered in five thematic tracks. (The conference language is German.) The FOUNDATION track will introduce basic concepts (A. Zell, Univ. Stuttgart), as well as present lectures on information processing in biological neural systems (G. Palm, Univ. Ulm), on the relationship between ANN's and fuzzy logic (R. Kruse, Univ. Braunschweig), and on genetic algorithms (S. Vogel, Univ. Cologne). The THEORY track is devoted to the properties of ANN's as abstract learning algorithms. Courses are offered on approximation properties of ANN's (K. Hornik, Univ. Vienna), the algorithmic complexity of learning procedures (M. Schmitt, TU Graz), prediction uncertainty and model selection (G. Paass, GMD St. Augustin), and "neural" solutions of optimization problems (J. Buhmann, Univ. Bonn). This year, special emphasis will be put on APPLICATIONS of ANN's to real-world problems. This track covers courses on vision (H.Bischof, TU Vienna), character recognition (J. Schuermann, Daimler Benz Ulm), speech recognition (R. Rojas, FU Berlin), industrial applications (B. Schuermann, Siemens Munich), robotics (K.Moeller, Univ. Bonn), and hardware for ANN's (U. Rueckert, TU Hamburg-Harburg). In the track on SYMBOLIC CONNECTIONISM, there will be courses on: knowledge processing with ANN's (F. Kurfess, New (S. Wermter, Univ. Hamburg), connectionist aspects of natural language processing (U. Schade, Univ. Bielefeld), and procedures for extracting rules from ANN's (J. Diederich, QUT Brisbane). In the section on COGNITIVE MODELLING, we have courses on representation and cognitive models (G. Dorffner, Univ. Vienna), aspects of cognitive psychology (R. Mangold-Allwinn, Univ. Saarbruecken), self-organizing ANN's in the visual system (C. v.d. Malsburg, Univ. Bochum), and information processing in the visual cortex (J.L. v. Hemmen, TU Munich). In addition, there will be courses on PROGRAMMING and SIMULATORS. Participants will have the opportunity to work with the SESAME system (J. Kindermann, GMD St.Augustin) and the SNNS simulator (A.Zell, Univ. Stuttgart). From owner-gann-list Fri May 12 14:11:14 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id NAA12575 for gann-list-outgoing; Fri, 12 May 1995 13:54:24 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id NAA12568 for gann-list@cs.iastate.edu; Fri, 12 May 1995 13:54:20 -0500 From: GANN List Admin Reply-To: terry@santafe.edu X-Originator: terry@santafe.edu Date: Wed, 10 May 95 08:11:22 MDT Message-Id: <9505101411.AA14968@wijiji> To: ml@ics.uci.edu, psych%tcsvm.bitnet@cunyvm.cuny.edu, neuron@hplabs.hpl.hp.com, biosci@presto.ig.com, neuro-evolution@cse.ogi.edu, alife@cognet.ucla.edu, biosci%net.bio.net@vm1.nodak.edu, ga-list@aic.nrl.navy.mil, Connectionists@cs.cmu.edu, simulation@bikini.cis.ufl.edu, IE-list@cs.ucl.ac.uk, EP-List@magenta.me.fau.edu, genetic-programming@CS.Stanford.EDU, gann@cs.iastate.edu, neuron-request@cattell.psych.upenn.edu Subject: Technical Reports Available for ftp. Sender: owner-gann-list@cs.iastate.edu Precedence: bulk The following technical reports are now available via anonymous ftp. Both of these were rejected by conferences (ML and IJCAI respectively). Caveat Emptor. These can also be obtained by sending mail to mat@santafe.edu. Terry Jones (terry@santafe.edu). -------------------------------------------------------------------------- One Operator, One Landscape Terry Jones Santa Fe Institute 1399 Hyde Park Road Santa Fe, NM 87501, USA terry@santafe.edu ABSTRACT The use of the term "landscape" is increasing rapidly in the field of evolutionary computation, yet in many cases it remains poorly, if at all, defined. This situation has perhaps developed because everyone grasps the imagery immediately, and the questions that would be asked of a less evocative term do not get asked. This paper presents an important consequence of a new model of landscapes. The model is general enough to encompass most of what computer scientists would call search, though it is not restricted to either the field or the viewpoint. The consequence is a "one-operator, one-landscape" view of search algorithms that is particularly relevant for algorithms that search via the use of multiple operators, and hence to genetic algorithms and other members of the evolutionary computing family. Crossover and selection landscapes are presented as siblings of the traditional mutation landscape. The model encourages a perspective on search algorithms that makes a clear division between landscape structures and navigation upon them. The model also establishes a strong connection with the heuristic state space search algorithms of Artificial Intelligence. 15 pages. FTP: ftp.santafe.edu:pub/terry/oool.ps.gz -------------------------------------------------------------------------- Genetic Algorithms and Heuristic Search Terry Jones Stephanie Forrest Santa Fe Institute Department of Computer Science 1399 Hyde Park Road University of New Mexico Santa Fe, NM 87501, USA Albuquerque, NM 87131, USA terry@santafe.edu forrest@cs.unm.edu ABSTRACT Genetic algorithms (GAs) and heuristic search are shown to be structurally similar. The strength of the correspondence and its practical consequences are demonstrated by considering the relationship between fitness functions in GAs and the heuristic functions of AI. By examining the extent to which fitness functions approximate an AI ideal, a measure of GA search difficulty is defined and applied to previously studied problems. The success of the measure in predicting GA performance (1) illustrates the potential advantages of viewing evolutionary search from a heuristic search perspective and (2) appears to be an important step towards answering a question that has been the subject of much research in the GAs community: what makes search hard (or easy) for a GA? 19 pages. FTP: ftp.santafe.edu:pub/terry/gahs.ps.gz -------------------------------------------------------------------------- From owner-gann-list Fri May 12 14:11:14 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id NAA11435 for gann-list-outgoing; Fri, 12 May 1995 13:53:08 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id NAA11428 for gann-list@cs.iastate.edu; Fri, 12 May 1995 13:53:05 -0500 From: GANN List Admin Reply-To: terry@santafe.edu X-Originator: terry@santafe.edu Date: Wed, 10 May 95 08:10:19 MDT Message-Id: <9505101410.AA14965@wijiji> To: ml@ics.uci.edu, psych%tcsvm.bitnet@cunyvm.cuny.edu, neuron@hplabs.hpl.hp.com, biosci@presto.ig.com, neuro-evolution@cse.ogi.edu, alife@cognet.ucla.edu, biosci%net.bio.net@vm1.nodak.edu, ga-list@aic.nrl.navy.mil, Connectionists@cs.cmu.edu, simulation@bikini.cis.ufl.edu, IE-list@cs.ucl.ac.uk, EP-List@magenta.me.fau.edu, genetic-programming@CS.Stanford.EDU, gann@cs.iastate.edu, neuron-request@cattell.psych.upenn.edu Subject: ICGA95 paper available --- Crossover, Macromutation, and Population-based Search. Sender: owner-gann-list@cs.iastate.edu Precedence: bulk The following paper is available for ftp from ftp.santafe.edu in pub/terry/ch.ps.gz. This paper will be presented in July at the Sixth International Conference on Genetic Algorithms in Pittsburgh. Crossover, Macromutation, and Population-based Search Terry Jones Santa Fe Institute 1399 Hyde Park Road Santa Fe, NM 87501, USA terry@santafe.edu ABSTRACT A major reason for the maintenance of a population in a Genetic Algorithm (GA) is the hope of increased performance via direct communication of information between individuals. This communication is achieved through the use of a crossover operator. If crossover is not a useful method for this exchange, the GA may not, on average, perform any better than a variety of simpler algorithms that are not population-based. A simple method for testing the usefulness of crossover for a particular problem instance is presented. This allows the identification of situations in which crossover is apparently useful but is actually only producing gains that could be obtained, or exceeded, with macromutation and no population. Terry Jones (terry@santafe.edu). From owner-gann-list Fri May 12 14:11:19 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id NAA12161 for gann-list-outgoing; Fri, 12 May 1995 13:54:00 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id NAA12153 for gann-list@cs.iastate.edu; Fri, 12 May 1995 13:53:55 -0500 From: GANN List Admin Reply-To: terry@santafe.edu (Terry Jones) X-Originator: terry@santafe.edu (Terry Jones) Date: Wed, 10 May 95 09:56:33 MDT Message-Id: <9505101556.AA02085@sfi.santafe.edu> To: ml@ics.uci.edu, psych%tcsvm.bitnet@cunyvm.cuny.edu, neuron@hplabs.hpl.hp.com, biosci@presto.ig.com, neuro-evolution@cse.ogi.edu, alife@cognet.ucla.edu, biosci%net.bio.net@vm1.nodak.edu, ga-list@aic.nrl.navy.mil, Connectionists@cs.cmu.edu, simulation@bikini.cis.ufl.edu, IE-list@cs.ucl.ac.uk, EP-List@magenta.me.fau.edu, genetic-programming@CS.Stanford.EDU, gann@cs.iastate.edu, neuron-request@cattell.psych.upenn.edu Subject: Dissertation available for ftp Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Dissertation Available for ftp ============================== Evolutionary Algorithms, Fitness Landscapes and Search by Terry Jones Department of Computer Science University of New Mexico Albuquerque NM 87131 terry@santafe.edu A postscript version of my dissertation is now available for ftp from ftp.santafe.edu in the directory pub/terry/phd. The entire dissertation is in the file phd.ps.gz. When uncompressed (using gunzip), the postscript file is over 5MB. If you are on UNIX, you may want to print this using "lpr -s phd.ps" which will cause the printer software to make a symbolic link to the file rather than copying it (if you use -s you cannot remove phd.ps until the printing is done). If your printer cannot deal with a 5MB postscript file, you can ftp the dissertation in smaller pieces: part00.ps.gz (25 pages) Title, Abstract, Lists, Abbreviations etc. part01.ps.gz (12 pages) Introduction part02.ps.gz (34 pages) A Model of Landscapes part03.ps.gz (38 pages) Crossover, Macromutation & Population-based Search part04.ps.gz (36 pages) Reverse Hillclimbing part05.ps.gz (41 pages) Evolutionary Algorithms and Heuristic Search part06.ps.gz (19 pages) Related Work & Conclusions part07.ps.gz (44 pages) Appendices & Bibliography Mail me if you don't have gunzip, or don't know how to use anonymous ftp. If you do not have access to a postscript printer, you can get a hardcopy of the dissertation by requesting SFI TR 95-05-048 from mat@santafe.edu. These were just sent to be spiral bound, and will be available early next week. ABSTRACT -------- A new model of fitness landscapes suitable for the consideration of evolutionary and other search algorithms is developed and its consequences are investigated. Answers to the questions "What is a landscape?" "Are landscapes useful?" and "What makes a landscape difficult to search?" are provided. The model makes it possible to construct landscapes for algorithms that employ multiple operators, including operators that act on or produce multiple individuals. It also incorporates operator transition probabilities. The consequences of adopting the model include a "one operator, one landscape" view of algorithms that search with multiple operators. An investigation into crossover landscapes and hillclimbing algorithms on them illustrates the dual role played by crossover in genetic algorithms. This leads to the "headless chicken" test for the usefulness of crossover to a given genetic algorithm and to serious questions about the usefulness of maintaining a population. A "reverse hillclimbing" algorithm is presented that allows the determination of details of the basin of attraction of points on a landscape. These details can be used to directly compare members of a class of hillclimbing algorithms and to accurately predict how long a particular hillclimber will take to discover a given point. A connection between evolutionary algorithms and the heuristic search algorithms of Artificial Intelligence and Operations Research is established. One aspect of this correspondence is investigated in detail: the relationship between fitness functions and heuristic functions. By considering how closely fitness functions approximate the ideal for heuristic functions, a measure of search difficulty is obtained. This measure, fitness distance correlation, is a remarkably reliable indicator of problem difficulty for a genetic algorithm on many problems taken from the genetic algorithms literature, even though the measure incorporates no knowledge of the operation of a genetic algorithm. This leads to one answer to the question "What makes a problem hard (or easy) for a genetic algorithm?" The answer is perfectly in keeping with what has been well known in Artificial Intelligence for over thirty years. Terry Jones (terry@santafe.edu). From owner-gann-list Fri May 12 14:11:19 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id OAA13595 for gann-list-outgoing; Fri, 12 May 1995 14:06:22 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id OAA13585 for gann-list@cs.iastate.edu; Fri, 12 May 1995 14:06:20 -0500 From: GANN List Admin Reply-To: rosca@cs.rochester.edu X-Originator: rosca@cs.rochester.edu Date: Wed, 10 May 1995 18:51:39 -0400 Message-Id: <199505102251.SAA11664@tbird.cs.rochester.edu> To: genetic-programming@CS.Stanford.EDU, ml@ics.uci.edu, ga-list@aic.nrl.navy.mil, neuron@hplabs.hpl.hp.com, neuro-evolution@cse.ogi.edu, alife@cognet.ucla.edu, Connectionists@cs.cmu.edu, EP-List@magenta.me.fau.edu, gann@cs.iastate.edu, neuron-request@cattell.psych.upenn.edu Cc: rosca@cs.rochester.edu Subject: postscript preprints (Genetic Programming) Sender: owner-gann-list@cs.iastate.edu Precedence: bulk This is to announce the availability of two new postscript preprints: AN ANALYSIS OF HIERARCHICAL GENETIC PROGRAMMING Justinian P. Rosca Technical Report 566 University of Rochester ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.tr566.ps.gz ABSTRACT: Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an analysis of the evolution static point of view, the use of an HGP approach enables the manipulation of a population of higher diversity programs. Higher diversity increases the exploratory ability of the genetic search process, as demonstrated by theoretical and experimental fitness distributions and expanded structural complexity of individuals. the crossover operator. Causality relates changes in the structure of an object with the effect of such changes, i.e. changes in the properties or behavior of the object. The analyses of crossover causality suggests that HGP discovers and exploits useful structures in a bottom-up, hierarchical manner. Diversity and causality are complementary, affecting exploration and exploitation in genetic search. Unlike other machine learning techniques that need extra machinery to control the tradeoff between them, HGP automatically trades off exploration and exploitation. Some of the discussions in this report are summarized in the following paper, to be presented in July at the Sixth International Conference on Genetic Algorithms in Pittsburgh: CAUSALITY IN GENETIC PROGRAMMING Justinian P. Rosca Dana H. Ballard to appear in Proc. ICGA95 ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.icga.causality.ps.gz ABSTRACT: Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting control parameters for speeding up GP search. We first analyze the effects of crossover to show the weak causality of the GP representation and operators. Hierarchical GP approaches based on the discovery and evolution of functions amplify this phenomenon. However, selection gradually retains strongly causal changes. Causality is correlated to search space exploitation and is discussed in the context of the exploration-exploitation tradeoff. The results described argue for a bottom-up GP evolutionary thesis. Finally, new developments based on the idea of GP architecture evolution [Koza94] are discussed from the causality perspective. For abstracts of other papers or reports, see the file ftp://ftp.cs.rochester.edu/pub/u/rosca/CATALOG. Justinian (rosca@cs.rochester.edu) From owner-gann-list Fri May 12 14:11:19 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id NAA11679 for gann-list-outgoing; Fri, 12 May 1995 13:53:31 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id NAA11672 for gann-list@cs.iastate.edu; Fri, 12 May 1995 13:53:28 -0500 From: GANN List Admin Reply-To: terry@santafe.edu X-Originator: terry@santafe.edu Date: Wed, 10 May 95 08:09:36 MDT Message-Id: <9505101409.AA14962@wijiji> To: ml@ics.uci.edu, psych%tcsvm.bitnet@cunyvm.cuny.edu, neuron@hplabs.hpl.hp.com, biosci@presto.ig.com, neuro-evolution@cse.ogi.edu, alife@cognet.ucla.edu, biosci%net.bio.net@vm1.nodak.edu, ga-list@aic.nrl.navy.mil, Connectionists@cs.cmu.edu, simulation@bikini.cis.ufl.edu, IE-list@cs.ucl.ac.uk, EP-List@magenta.me.fau.edu, genetic-programming@CS.Stanford.EDU, gann@cs.iastate.edu, neuron-request@cattell.psych.upenn.edu Subject: ICGA95 paper available --- Fitness Distance Correlation. Sender: owner-gann-list@cs.iastate.edu Precedence: bulk The following paper is available for ftp from ftp.santafe.edu in pub/terry/fdc.ps.gz. This paper will be presented in July at the Sixth International Conference on Genetic Algorithms in Pittsburgh. Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms Terry Jones Stephanie Forrest Santa Fe Institute Department of Computer Science 1399 Hyde Park Road University of New Mexico Santa Fe, NM 87501, USA Albuquerque, NM 87131, USA terry@santafe.edu forrest@cs.unm.edu ABSTRACT A measure of search difficulty, fitness distance correlation (FDC), is introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy deceptive problems as easy and difficult non-deceptive problems as difficult, indicates when Gray coding will prove better than binary coding, and is consistent with the surprises encountered when GAs were used on the Tanese and royal road functions. The FDC measure is a consequence of an investigation into the connection between GAs and heuristic search. Terry Jones (terry@santafe.edu). From owner-gann-list Fri May 19 11:42:40 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id LAA22121 for gann-list-outgoing; Fri, 19 May 1995 11:18:52 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id LAA22108 for gann-list@cs.iastate.edu; Fri, 19 May 1995 11:18:43 -0500 From: GANN List Admin Reply-To: ipmu96@robinson.ugr.es X-Originator: ipmu96@robinson.ugr.es X400-Received: by mta rica in /PRMD=/ADMD=/C=/; Relayed; Tue, 16 May 1995 12:58:52 UTC+0200 X400-Received: by /PRMD=iris/ADMD=mensatex/C=es/; Relayed; Tue, 16 May 1995 12:58:52 UTC+0200 X400-Received: by /ADMD=/C=/; Relayed; Tue, 16 May 1995 13:02:34 UTC+0200 Date: Tue, 16 May 1995 13:02:34 UTC+0200 X400-Originator: ipmu96@robinson.ugr.es X400-Recipients: non-disclosure:; X400-Content-Type: P2-1984 (2) X400-MTS-Identifier: [/ADMD=/C=/;950516131056] Content-Identifier: LV950516130234 To: gannadm@cs.iastate.edu Cc: carlos.es@ugr.es, ipmu96.es@ugr.es Message-ID: <9505161202.AA28923@robinson.ugr.es> Subject: Conference Announcement MIME-Version: 1.0 (Generated by Ean X.400 to MIME gateway) Status: RO Sender: owner-gann-list@cs.iastate.edu Precedence: bulk >X-Envelope-to: gannadm@cs.iastate.edu Dear Sir, You can find enclosed the preliminary call for paper for IPMU'96 International Conference. As you can see in the topics, it is of primary interest for the genetic community, so that we hope it is an important contribution to your list. Yours Sincerely. --------- Mail Contribution to the List begin here -------- ***************************************************** * Preliminary Call for Paper * * * * IPMU'96 * * * * Granada, Spain, July 1-5, 1996 * ***************************************************** -------------------------------------------------------- Organized by Departamento de Ciencias de la Computacion e Inteligencia Artificial. Universidad de Granada. Sponsored by Junta de Andalucia. Universidad de Granada. Ayuntamiento de Granada. -------------------------------------------------------- -------------------------------------------------------- Aims and Scope -------------------------------------------------------- Organized at a regular two-year interval, the IPMU International Conference deals with the difficulties existing in the acquisition, representation, management and transmission of data in knowledge-based and decision-making systems. It brings together researches working on various methodologies for the management of uncertain information and provides a useful exchange between theorists and practitioners using these different methodologies. -------------------------------------------------------- Topics of particular interest -------------------------------------------------------- * Uncertainty Methods: Measures of Information and Uncertainty, Bayesian and Probabilistic Methods, Fuzzy Methods, Mathematical Theory of Evidence, Belief Networks, Chaos Theory. * Non-standard Logics: Non-monotonic Logics, Approximate Reasoning, Multivalued Logics, Modal Logics, Temporal Reasoning, Case-based Reasoning. * Knowledge Acquisition and Representation: Machine Learning, Inductive Methods, Commonsense Knowledge, Intelligent Databases and Information Systems. * Intelligent Systems: Fuzzy Control, Neural Networks, Genetic Algorithms and Evolutionary Computation, Expert Systems under Uncertainty, Decision Support Systems, Multicriteria and Group Decision Making, Pattern Recognition, Image Processing, Classification, Belief Updating and Inconsistency Handling. -------------------------------------------------------- Address and Location -------------------------------------------------------- IPMU'96 Dpto. Ciencias de la Computacion e Inteligencia Artificial. E.T.S.I. Informatica. Avda. Andalucia, 38 Universidad de Granada. 18071 Granada. Spain. Phone: +34.58.244019 Fax: +34.58.243317 e-mail: ipmu96@robinson.ugr.es e-mail for submissions: ipmu96-submissions@robinson.ugr.es URL: http://pirata.ugr.es/ipmu96.html Granada, a world-famous city, whose history spans over thousand years, also has outstanding features as a modern conference town. The Alhambra, the city's monuments, cultural and University traditions, as well as excellent leisure facilities, good restaurants, lively night life, the Sierra Nevada mountains and the Coast, all attract thousand of visitors to Granada every year. -------------------------------------------------------- Submission Information -------------------------------------------------------- Authors should submit three copies of each full paper by November 1st. There will be a six page (two columns) limit on the final versions of accepted papers. Papers will be carefully reviewed and authors will be notified on the acceptance/rejection by February 1st, 1996. Final camera-ready copies for publication will required by April 1st, 1996. Submissions may be sent by mail to the address included in this call. Electronic submissions are encouraged. To submit a paper electronically, send an e-mail to ipmu96-submissions@robinson.ugr.es including the following information (in this order): a) Paper title (plain text) b) Author's names, including professional status. c) Surface mail and e-mail address for a contact author (plain text) d) A short abstract, including keywords or topic indicators (plain text) e) Paper body (Postscript format) Proceedings will be available at the opening of the Conference. Relevant papers may be selected for publication in special issues of leading international journals. -------------------------------------------------------- Dates and Deadlines -------------------------------------------------------- Nov. 1 1995: Deadline for submission of papers. Feb. 1 1996: Notification of acceptance/rejection. Apr. 1 1996: Reception of final camera-ready. May 15 1996: Deadline for early registration. July 1-5 1996: CONFERENCE. Early Registration Fee: 60.000 pesetas (ptas). Late Registration Fee: 70.000 pesetas (ptas). -------------------------------------------------------- Invited sessions -------------------------------------------------------- A number of invited sessions on special topics will be included in the program of IPMU'96. Authors will be invited to contribute to these sessions, which will be chaired by recognized experts in these topics. Proposals to organize invited sessions are welcome to be considered by the committee until September 15th. -------------------------------------------------------- Honorary President -------------------------------------------------------- Lotfi A. Zadeh(University of California, Berkeley, USA) -------------------------------------------------------- General Chairpersons Committee -------------------------------------------------------- Bernardette Bouchon-Meunier (CNRS, France) Miguel Delgado (University of Granada, Spain) Jose Luis Verdegay (University of Granada, Spain) Maria Amparo Vila (University of Granada, Spain) Ronald R. Yager (Iona College, NY, USA) -------------------------------------------------------- International Program Committee -------------------------------------------------------- J. Aczel (Canada) J. Aguilar-Martin (France) J. Baldwin (UK) S. Barro (Spain) A. Blanco (Spain) H. Berenji (USA) J. Bezdek(USA) P. Bonissone (USA) P. Bosc (France) J.L. Castro(Spain) D. Dubois (France) F. Esteva (Spain) M. Fedrizzi (Italy) M.A. Gil (Spain) A. Gonzalez (Spain) S. Guiasu (Canada) J. Gutierrez (Spain) F. Herrera (Spain) K. Hirota(Japan) J. Jacas (Spain) J.Y. Jaffray (France) A. Kandel (USA) E.P. Klement (Austria) G. Klir (USA) R. Kruse (Germany) M.T. Lamata (Spain) H.L. Larsen (Denmark) S.L. Lauritzen (Denmark) R. Lopez de Mantaras (Spain) R. Marin (Spain) J. Montero (Spain) S. Moral (Spain) H. Nguyen (USA) S. Ovchinnikov(USA) J. Pearl (USA) H. Prade(France) I. Requena (Spain) A. Rocha (Brazil) E. Ruspini (USA) A. Sage (USA) E. Sanchez (France) G. Shafer (USA) P. Shenoy (USA) P. Smets (Belgium) M. Sugeno (Japan) S. Termini (Italy) A. Titli (France) E. Trillas (Spain) I.B. Turksen (USA) R. Valle (France) L. Valverde (Spain) T. Yamakawa (Japan) H.J. Zimmermann (Germany) -------------------------------------------------------- Organizing Committee: -------------------------------------------------------- S. Moral (President) A. Blanco J.L. Castro J.C. Cubero A. Gonzalez F. Herrera M.T. Lamata J.M. Medina O. Pons I. Requena J.M. Zurita -------------------------------------------------------- PRE-REGISTRATION FORM -------------------------------------------------------- If you want to be kept informed, please fill in this form and return it to the address included in this call. ....................... cut here ....................... Pre-Registration Form Surname: First Name: Mailing Address: Phone: Fax: E-Mail: _ |_| I am interested in attending the conference. _ |_| I am interested in submitting a paper. From owner-gann-list Fri May 19 11:42:42 1995 Received: (from mdomo@localhost) by cs.iastate.edu (8.6.10/8.6.9) id LAA20844 for gann-list-outgoing; Fri, 19 May 1995 11:18:00 -0500 Received: (from gannadm@localhost) by cs.iastate.edu (8.6.10/8.6.9) id LAA20806 for gann-list@cs.iastate.edu; Fri, 19 May 1995 11:17:33 -0500 From: GANN List Admin Reply-To: fukuda@mein.nagoya-u.ac.jp (Toshio Fukuda) X-Originator: fukuda@mein.nagoya-u.ac.jp (Toshio Fukuda) Message-Id: <9505160210.AA12886@mein.mein.nagoya-u.ac.jp> Date: Tue, 16 May 1995 11:15:07 +0900 To: GA-List@aic.nrl.navy.mil, GA-List@aic.nrl.navy.mil, BA-GP@CS.Stanford.EDU, ga-molecule-approval@interval.com, EP-List@magenta.me.fau.edu, alife@cognet.ucla.edu, ml@ics.uci.edu, colt@cs.uiuc.edu, neuron-request@CATTELL.psych.upenn.edu, Reinforce@cs.uwa.edu.au, TIERRA@life.slhs.udel.edu, cellular-automata@think.com, cells@tce.ing.uniroma1.it, connectionists@cs.cmu.edu, ilpnet@ijs.si, INDUCTIVE@hermes.csd.unb.ca, evolutionary-computing@mailbase.ac.uk, gann-list@cs.iastate.edu, IE-list@cs.ucl.ac.uk, psych%tcsvm.bitnet@cunyvm.cuny.edu, news-announce-conferences@uunet.uu.net, neuron@hplabs.hpl.hp.com, biosci@presto.ig.com, ETHOLOGY%FINHUTC.BITNET@cunyvm.cuny.edu, neuron@hplabs.hpl.hp.com, biosci%net.bio.net@vm1.nodak.edu, simulation@bikini.cis.ufl.edu, IE-list@cs.ucl.ac.uk Subject: ICEC'96 call for papers Mime-Version: 1.0 Content-Type: text/plain; charset=iso-2022-jp X-Mailer: Eudora-J(1.3.8.5-J13) Sender: owner-gann-list@cs.iastate.edu Precedence: bulk Dear Sirs, Attached below is the Call For Papers of 1996 IEEE International Conference on Evolutionary Computation (ICEC'96), which will be held in May 20-22, 1996, Nagoya, Japan. I would like you to encourage your colleagues and yourself to submit the papers to ICEC'96. Sincerely yours, Toshio Fukuda General Chair, ICEC'96 =================================================================== CALL FOR PAPERS 1996 IEEE International Conference on Evolutionary Computation (ICEC'96) May 20-22, 1996, Nagoya, Japan Co-sponsored by IEEE Neural Network Council (NNC) and Society of Instrument and Control Engineers (SICE) Topics: Theory of evolutionary computation, Applications of evolutionary computation, Efficiency / robustness comparisons with other direct search algorithms, Parallel computer implementations, Artificial life and biologically inspired evolutionary computation, Evolutionary algorithms for computational intelligence, Comparisons between difference variants of evolutionary algorithms, Machine learning applications, Genetic algorithm and selforganization, Evolutionary computation for neural networks, Fuzzy logic in evolutionary algorithms Submission Procedure: Prospective authors are invited to submit papers related to the listed topics for oral or poster presentation. Five (5) copies of the paper must be submitted for review. Papers should be printed on letter size white paper, written in English in two-column format in Times or similar font style, 10 points or larger with 2.5 cm margins on all four sides. A length of four pages is encouraged, and a limit of six pages, including figures, tables and references will be enforced. Centered at the top of the first page should be the complete title of the paper and the name(s), affiliation(s) and address(es) of the author(s). All papers (except those submitted for special sessions - which may have different deadlines - see information on special sessions below) should be sent to: Toshio Fukuda, General Chair Nagoya University Dept. of Micro System Engineering and Dept. of Mechano-Informatics and Systems Furo-cho, Chikusa-ku, Nagoya 464-01, JAPAN Phone: +81-52-789-4478, Fax: +81-52-789-3909, Email: fukuda@mein.nagoya-u.ac.jp ICEC'96 will be organized in conjunction with the conference of Artificial Life (May 16-18, 1996, Kyoto, JAPAN). General Chair: Toshio Fukuda Nagoya University fukuda@mein.nagoya-u.ac.jp Program Co-chairs: There are several special sessions organized for the 3rd IEEE ICEC '96; so far these include: *********************************************************** Constrained Optimization, Constraint Satisfaction and EC *********************************************************** Organized by Gusz Eiben, chair (Utrecht University, gusz@cs.ruu.nl) Dave Corne (University of Edinburgh,dave@aifh.ed.ac.uk) Jurgen Dorn (Technical University of Vienna, dorn@vexpert.dbai.tuwien.ac.at) Peter Ross (University of Edinburgh, peter@aisb.ed.ac.uk) Evolutionary Computation has proved its merit in treating difficult problems in, for example, numerical optimization and machine learning. Nevertheless, problems where constraints on the search space (i.e., on the candidate solutions) play an important role have received relatively little attention. In real-world problems, however, the presence of constraints seems to be rather the rule than the exception. The class of constrained problems can be divided into Constraint Satisfaction Problems (CSP) and Constrained Optimization Problems (COP). This special session addresses both subclasses, and aims to explore the extent to which EC can usefully tackle problems of these kinds. All correspondence regarding this special session should be addressed to: A.E. Eiben Department of Computer Science, Utrecht University P.O.Box 80089, 3508 TB Utrecht, The Netherlands Phone: +31-(0)30-533619, Fax: +31-(0)30-513791, Email: gusz@cs.ruu.nl ******************************************** Evolutionary Artificial Neural Networks ******************************************** Organized by X. Yao (The University of New South Wales, xin@cs.adfa.oz.au) Evolutionary Artificial Neural Networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search algorithms. Three levels of evolution in EANNs have been studied recently, i.e., the evolution of connection weights, architectures, and learning rules. Major issues in the research of EANNs include their scalability, generalization ability and interactions among different levels of evolution. This special session will serve as a forum for both researchers and practitioners to discuss these important issues and exchange their latest research results/ideas in the area. All correspondence regarding this special session should be addressed to: Xin Yao Department of Computer Science, University College, The University of New South Wales Australian Defence Force Academy Canberra, ACT 2600, Australia Phone: +61 6 268 8819, Fax: +61 6 268 8581, Email: xin@csadfa.cs.adfa.oz.au ****************************************** Evolutionary Robotics and Automation ****************************************** Organized by J. Xiao (University of North Carolina, xiao@uncc.edu) More and more researchers are applying evolutionary computation techniques to challenging problems in robotics and automation, where classical methods fail to be effective. In addition to being vastly applicable to many hard problems, evolutionary concepts inspire many researchers as well as users to be fully creative in inventing their own versions of evolutionary algorithms for the specific needs of different domains of problems. This special session serves as a forum for exchanging research results in this growing interdisciplinary area and for encouraging further exploration of the fusion between evolutionary computation and intelligent robotics and automation. All correspondence regarding this special session should be addressed to: Jing Xiao Department of Computer Science, University of North Carolina - Charlotte Charlotte, NC 28223 Phone: (704) 547-4883, Fax: (704) 547-3516, Email: xiao@uncc.edu ************************* Genetic Programming ************************* Organized by John R. Koza (Stanford University , Koza@Cs.Stanford.Edu) Lee Spector (Hampshire College, LSPECTOR@hampshire.edu) Yuji Sato (Hitachi Ltd. Central Research Lab., yuji@crl.hitachi.co.jp) The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Genetic programming extends the genetic algorithm to the domain of computer programs. In genetic programming, populations of program are genetically bred to solve problems. Genetic programming is a domain-independent method for evolving computer programs that solves, or approximately solves, a variety of problems from a variety of fields, including many benchmark problems from machine learning and artificial intelligence such as problems of control, robotics, optimization, game playing, and symbolic regression (i.e., system identification, concept learning). Early versions of genetic programming evolved programs consisting of only a single part (i.e., one main program). All correspondence regarding this special session should be addressed to: John R. Koza Computer Science Department, Margaret Jacks Hall, Stanford University Stanford, California 94305-2140 USA Phone: 415-723-1517, Fax: 415-941-9430, Email: Koza@Cs.Stanford.Edu ********************************************** Self-adaptation in Evolutionary Algorithms ********************************************** Organized by Guenter Rudolph (ICD Informatik Centrum Dortmund e.V., Rudolph@LS11.InformatikUni-Dortmund.de) Evolutionary algorithms (EAs) with the ability to adapt internal strategic parameters (like population size, mutation distribution, type of recombination operator, selective pressure etc.) during the search process usually find better solutions than variants with fixed strategic parameters. Self-adaptation is very useful if different (fixed) parameter settings produce large differences in the solution quality of the algorithm. Most experiences are available for (real-coded) EAs whose individuals adapt their mutation distributions (or step sizes). Here, the property to adjust the step size is induced by competitive pressure among individuals. Evidently, self-adapting mechanisms can be realized by competing subpopulations as well. The potential of those EAs is essentially unexplored. All correspondence regarding this special session should be addressed to: Guenter Rudolph ICD Informatik Centrum Dortmund e.V. Joseph-von-Fraunhofer-Str. 20, D-44227 Dortmund, Germany Phone: +49-(0)231-9700-365, Fax: +49-(0)231-9700-959, Email: Rudolph@LS11.Informatik.Uni-Dortmund.de ********************************************** Evolutionary Algorithms and Fuzzy Systems ********************************************** Organized by Witold Pedrycz (University of Manitoba, pedrycz@ee.umanitoba.ca) Fuzzy sets (FS) and evolutionary algorithms have been already successfully applied to many areas including fuzzy control and fuzzy clustering. There are a number of facets of symbiosis between the technologies of FS and GA. On one hand evolutionary computation enriches the optimization environment for fuzzy systems. On the other, fuzzy sets supply a new macroscopic and domain-specific insight into the fundamental mechanisms of evolutionary algorithms (including fuzzy crossover, fuzzy reproduction, fuzzy fitness function, etc.). The objective of this session is to foster further interaction between researchers actively engaged in FS and GAs. All correspondence regarding this special session should be addressed to: Witold Pedrycz Department of Electrical and Computer Engineering, University of Manitoba Winnipeg Canada RT 2N2 Phone: (204) 474-8380, Fax: (204) 261-4639, Email: pedrycz@ee.umanitoba.ca ********************************************************************* BE DARWINIAN: MAKE YOUR EVOLUTIONARY-BASED OPTIMIZATION ALGORITHM COMPETE WITH ALL OTHERS ********************************************************************* Organised by Hugues Bersini and Marco Dorigo from IRIDIA - ULB - Brussels. A special competition session will be organized during the 1996 IEEE International Conference on Evolutionary Computing in which all candidate evolutionary-based algorithms will compete on benchmark problems of real and combinatorial optimization. The rules of this competition will be announced in a near future but basically they will present the benchmark problems together with some standard results (best solution, computer time to reach it, etc...) to at least reproduce (but hopefully improve) for being accepted to participate in the session. This competition first aims at clarifying a situation which is every day more confused (just look at the email on the GA list) on the real potentialities of evolutionary-based or evolutionary-inspired algorithms as compared with classical optimization algorithms (Hill-Climbers, Simplex, ..) and less classical ones (Simulated annealing, Tabu search). Secondly it aims at establishing a firm standard and a natural selectionist test for each improvements on previous algorithms which recurrently appear in conferences like ICGA, PPSN and IEEE. The winner algorithms will be joined together and presented in a book to be released following the conference. All correspondence regarding this special session should be addressed to: Hugues Bersini IRIDIA , cp 194/6, Universite Libre de Bruxelles 50, av. Franklin Roosevelt, 1050 Bruxelles - Belgium Phone:+32.2650.27.33, Fax:+32.2650.27.15, Email:bersini@ulb.ac.be Submission to Special Sessions: Four (4) copies of complete papers (6 pages maximum) should be submitted to each session organizer. All papers will be reviewed. ******************************************************************************** The deadline for proposals for organizing other special sessions during the 3rd IEEE ICEC '96 is August 20, 1995; submit your proposal to any Program Co-Chairs. ******************************************************************************** Program Committee: Treasurer: Chisato Numaoka ( Sony Computer Science Lab.) Publicity Co-Chairs: Pierre Borne(Ministere de l'Education Nationale), Takanori Shibata(MEL) Local Arrangement Chair: Takeshi Furuhashi ( Nagoya Univ. ) *************************************** Prof. Toshio Fukuda Nagoya University, Dept. of Micro System Engineering & Dept. of Mechano-Informatics and Systems Furo-cho, Chikusa-ku, Nagoya 464-01 JAPAN phone: +81-52-789-4478 fax: +81-52-789-3909 / 3115 E-mail: fukuda@mein.nagoya-u.ac.jp ***************************************