Genetic Algorithms Digest Tuesday, June 19, 2001 Volume 15 : Issue 23 - Send submissions (articles) to GA-List@gmu.edu. DO NOT send submissions to the listproc@gmu.edu address. - To subscribe send email to listproc@gmu.edu containing the following text in the body of the message: subscribe ga-list - To unsubscribe send email to listproc@gmu.edu containing the following text in the body of the message: unsubscribe ga-list - To change your email address, simply unsubscribe the old address and subscribe the new one. - Send other administrative requests to GA-List@gmu.edu. - You can access back issues, GA code, conference announcements, etc., either through the WWW at http://www.aic.nrl.navy.mil/galist/ or through anonymous ftp at ftp.aic.nrl.navy.mil in /pub/galist. -------------------------------- Today's Topics: - 2002 Congress on Evolutionary Computation - GAs for E-commerce - IlliGAL New Technical Reports Announcement - The interaction of explicit & implicit learning: Symp. at CogSci2001 -------------------------------- CALENDAR OF GA-RELATED ACTIVITIES: (with GA-List issue reference) IC-AI2001 Int Conf on AI, Las Vegas, NV Jun 25-28, 01 (v14n16) SOCO Soft Computing & Intell Sys for Industry, Scotland Jun 26-29, 01 (v14n18) CEF'2001 Sessions on EC in Econ. and Fin., New Haven, CN Jun 28-30, 01 (v15n4) ICML2001 18th Int Conf on Machine Learning, MA Jun 28-Jul 1, 01 (v14n16) AIME01 8th Euro Conf on AI in Medicine, Portugal Jul 1-4, 01 (v14n16) CIMCA2001 Int Conf on Comp Intelligence, Las Vegas Jul 4-6, 01 (v14n19) WOMAII Workshop on Memetic Algorithms, SF, CA Jul 7, 01 (v15n4) IWLCS-2001 4rth Int. Conf. on Classifier Sys., SF, CA Jul 7-8, 01 (v15n10) GECCO2001 Gen & Evolutionary Computation Conf, SF, CA Jul 7-11, 01 (v14n16) TARK VIII 8th Conf Theor Aspects of Ratnlty & Knowl, It Jul 8-10, 01 (v14n16) CIMCA2001 Int. Conf. on Comp. Int.,..., Las Vegas, NV Jul 9-11, 01 (v15n4) IAWTIC2001 Int. Conf. on Int. Agents,..., Las Vegas, NV Jul 9-11, 01 (v15n4) WATT2001 Wrkshp of...Automated Timetabling, Rotterdam Jul 9-11, 01 (v15n8) NASAEH 3rd Wrkshp on Evolvable Hardware, Pasadena, CA Jul 12-14, 01 (v15n2) SCI2001 Evolvable Sys. and Gen. Prog., Orlando, FL USA Jul 22-25, 01 (v15n8) IJCAI-01 WS on Empirical MEthods in AI, Seattle, USA Aug 4, 01 (v15n5) IDAMAP2001 Intelligent Data Analysis in Medicine & Phar Sep 4, 01 (v15n3) FUZZY DAYS Int Conf on Comp Intell, Dortmund, Germany Oct 1-3, 01 (v14n17) ICES2001 4th Int Conf on Evolvable Systems, Tokyo Oct 3-5, 01 (v14n19) MCCS 2001 6th Int. Conf on Msr & Ctrl in Compl. Sys, Ukr Oct. 8-12, 01 (v15n10) IAT2001 2nd Asia Pac Conf on Intell Agent Tech, Japan Oct 23-26, 01 (v14n14) EA01 ÉVOLUTION ARTIFICIELLE 2001, Le Creusot, France Oct 29-31, 01 (v15n5) ICDM01 IEEE Int Conf on Data Mining, Silicon Valley, Nov 29-Dec 2, 01 (v14n14) ANNIE 2001 Smart Eng. Systems Design Conf, StL, MO, USA Nov 4-7, 01 (v15n5) FUZZ-IEEE01 10th IEEE Int Conf on Fuzzy Systems, Austr Dec 2- 5, 01 (v14n20) AMT01 6th Int Conf Active Media Tech, Hong Kong, China Dec 18-20, 01 (v15n16) NF2002 1st Int ICSC Congress on Neuro-Fuzzy, Cuba Jan 15-18, 02 (v14n18) ICAIS2002 1st ICSC on Aut. Int. Sys., Geelong, Australia Feb 12-15, 02 (v15n22) EVOLANG2002 4th Int Conf on Evolution of Language, USA Mar 27-30, 02 (v15n21) CEC2002 Congress on Evolutionary Comput., Honolulu, HI May 12-17, 02 (v15n23) PATAT 2002 4rth Int. Conf. ... Auto. Timetbl., Belgium Aug 21-23, 02 (v15n10) PPSN VII 7th Int Conf on Parallel Prob.., Granada, Spain Sep 7-11, 02 (v15n21) Send announcements of other activities to GA-List@gmu.edu -------------------------------- Sender: "Ali Zalzala" Subject: 2002 Congress on Evolutionary Computation CALL FOR PAPERS 2002 Congress on Evolutionary Computation May 12-17, 2002 Hilton Hawaiian Village, Honolulu, HI held as part of the World Congress on Computational Intelligence The annual Congress on Evolutionary Computation (CEC) is one of the premier international conferences in the field. It covers all topics in evolutionary computation: from combinatorial to numerical optimization, from supervised to unsupervised learning, from co-evolution to collective behaviors, from evolutionary design to evolvable hardware, from molecular to quantum computing, from ant colony to artificial ecology, etc. The emphasis of the Congress will be on original theories and novel applications of evolutionary computation techniques. The Congress welcomes paper submissions from researchers, practitioners, and students worldwide. The 2002 Congress will be held in conjunction with the International Joint Conference on Neural Networks (IJCNN) and the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) as part of the World Congress on Computational Intelligence (WCCI). Crossfertilization of the three fields will be strongly encouraged. The Congress will feature keynote speeches and tutorials by world-leading researchers. It also will include a number of special sessions and workshops on the latest hot topics. Your registration admits you to all events and includes the World Congress proceedings and banquet. The deadline for submissions is 15 October 2001. Look for more details on paper submission and conference registration coming soon at http://www.wcci2002.org. CEC is jointly supported by the IEEE Neural Networks Council, the Evolutionary Programming Society, and the Institution of Electrical Engineers. -------------------------------- Sender: Alex Alves Freitas Subject: GAs for E-commerce I wonder if some reader of this list could give me some pointers to papers on GAs (or another kind of evolutionary algorithm) for e-commerce applications. Thanks in advance, Alex === Alex A. Freitas, Ph.D. PUCPR (Pontificia Universidade Catolica do Parana) PPGIA - CCET Rua Imaculada Conceicao, 1155 Curitiba - PR, 80215-901 Brasil alex@ppgia.pucpr.br http://www.ppgia.pucpr.br/~alex === -------------------------------- Sender: Martin Pelikan Subject: IlliGAL New Technical Reports Announcement The Illinois Genetic Algorithms Laboratory (IlliGAL) is pleased to announce the publication of the following new technical reports and software. Most IlliGAL technical reports, as well as reprints of other publications, are available in hardcopy and can be ordered from the IlliGAL librarian, (see below for ordering information). The technical reports in this announcement are also available electronically on our ftp and WWW servers (see the end of this announcement for ftp and WWW access instructions). --- IlliGAL Report No 2001021 On the importance of the second largest eigenvalue on the convergence rate of genetic algorithms Schmitt, F., Rothlauf, F. Abstract: Genetic algorithms are sometimes disparagingly denoted as just a fancier form of a plain, stupid heuristic. One of the main reasons for this kind of critique is that users believed a GA could not guarantee global convergence in a certain amount of time. Because the proof of global convergence of GAs using elitism has been performed elsewhere , in this work we want to extend previous work by J. Suzuki and focus on the identification of the determinants that influence the convergence rate of genetic algorithms. The convergence rate of genetic algorithms is addressed using Markov chain analysis. Therefore, we could describe an elitist GA using mutation, recombination and selection as a discrete stochastic process. Evaluating the eigenvalues of the transition matrix of the Markov chain we can prove that the convergence rate of a GA is determined by the second largest eigenvalue of the transition matrix. The proof is first performed for diagonalizable transition matrices and then transferred to matrices in Jordan normal form. The presented proof allows a more detailed and deeper understanding of the principles of evolutionary search. As an extension to this work we want to encourage researchers to work on proper estimations of the second largest eigenvalue of the transition matrix. With a good approximation, the convergence behavior of GAs could be described more exactly and GAs would be one step ahead on the road to a fast, reliable and widely accepted optimization method. --- IlliGAL Report No 2001022 The parameter-less genetic algorithm in practice Lobo, F.G. and Goldberg, D.E. Abstract: The parameter-less genetic algorithm (Harik & Lobo, 1999), (Lobo, 2000), was recently introduced as a technique that makes genetic algorithms easier to use. This paper shows how that technique can be used in practice by applying it to a network expansion problem. The existence of the parameter-less genetic algorithm stresses the fact that some problems need more processing power than others. Such observation lead to the development of a problem difficulty measure which is also introduced in this paper. The measure can be very useful for comparing the difficulty of real-world problems. --- IlliGAL Report No 2001023 Combining the Strengths of the Bayesian Optimization Algorithm and Adaptive Evolution Strategies Pelikan, M., Goldberg, D.E., Tsutsui, S. Abstract: A method which combines competent genetic algorithms working in discrete domains with adaptive evolution strategies working in continuous domains is described. Discretization is used to transform solutions between the two domains. Experiments with Bayesian optimization algorithm, sigma-self-adaptive mutation, and three different discretization methods are presented. The results suggest that the algorithm scales up well on all tested problems. --- RETRIEVAL/ORDERING: The above IlliGAL reports and publications, along with other publications and source code, are available electronically via FTP or WWW, or as hardcopy directly from us: FTP: ftp ftp-illigal.ge.uiuc.edu login: anonymous password: (your email address) cd /pub/papers/IlliGALs (for reports) or cd /pub/papers/Publications (for preprints) or cd /pub/src (for GA and classifier system source code) binary get 99022.ps.Z (for example) Please look at the README files for explanations of what the file names mean. IlliGAL reports are all compressed postscript files. WWW: To access the IlliGAL home page, open http://www-illigal.ge.uiuc.edu/ HARDCOPY: You can also order hardcopy versions of most IlliGAL publications Use the order form in the web or request them directly (by IlliGAL number or title) from the IlliGAL librarian: Internet: library@illigal.ge.uiuc.edu Phone: 217/333-2346 Fax: 217/244-5705 Surface mail: IlliGAL Librarian Department of General Engineering 117 Transportation Building 104 South Mathews Avenue Urbana, IL 61801-2996 USA When ordering hardcopy, please include your surface mail address! --- Martin Pelikan Illinois Genetic Algorithms Laboratory University of Illinois at Urbana Champaign 117 Transportation Building 104 S. Mathews Avenue, Urbana, IL 61801 tel: (217) 333-2346, fax: (217) 244-5705 --- -------------------------------- Sender: rsun@cecs.missouri.edu Subject: THE INTERACTION OF EXPLICIT AND IMPLICIT LEARNING: A Symposium at CogSci'2001 THE INTERACTION OF EXPLICIT AND IMPLICIT LEARNING A Symposium at CogSci'2001 (August 1-4, 2001), Edinburgh, Scotland === Titles of the Talks: Axel Cleeremans: ``Behavioral, neural, and computational correlates of implicit and explicit learning" Zoltan Dienes: ``The effect of prior knowledge on implicit learning" Bob Mathews: ``Finding the optimal mix of implicit and explicit learning" Ron Sun: ``The synergy of the implciit and the explicit" === The symposium will be held on August 4th, 2001, 2:30 - 4:10 pm. See http://www.hcrc.ed.ac.uk/cogsci2001/programme.html for futher details of the 23rd Cognitive Science Conference, Edinburgh, Scotland. === Background: The role of implicit learning in skill acquisition and the distinction between implicit and explicit learning have been widely recognized in recent years (see, e.g., Reber 1989, Stanley et al 1989, Willingham et al 1989, Anderson 1993), Although implicit learning has been actively investigated, the complex and multifaceted interaction between the implicit and the explicit and the importance of this interaction have not been universally recognized; to a large extent, such interaction has been downplayed or ignored, with only a few notable exceptions. Research has been focused on showing the LACK of explicit learning in various learning settings (see especially Lewicki et al 1987) and on the controversies stemming from such claims. Despite the lack of studies of interaction, it has been gaining recognition that it is difficult, if not impossible, to find a situation in which only one type of learning is engaged (Reber 1989, Seger 1994, but see Lewicki et al 1987). Our review of existing data has indicated that, while one can manipulate conditions to emphasize one or the other type, in most situations, both types of learning are involved, with varying amounts of contributions from each (see, e.g., Sun et al 2000; see also Stanley et al 1989, Willingham et al 1989). Likewise, in the development of cognitive architectures (e.g., Rosenbloom et al 1993, Anderson 1993), the distinction between procedural and declarative knowledge has been proposed for a long time, and advocated or adopted by many in the field (see especially Anderson 1993). The distinction maps roughly onto the distinction between the explicit and implicit knowledge, because procedural knowledge is generally inaccessible while declarative knowledge is generally accessible and thus explicit. However, in work on cognitive architectures, focus has been almost exclusively on ``top-down" models (that is, learning first explicit knowledge and then implicit knowledge on the basis of the former), the bottom-up direction (that is, learning first implicit knowledge and then explicit knowledge, or learning both in parallel) has been largely ignored, paralleling and reflecting the related neglect of %the complex and multifaceted the interaction of explicit and implicit processes in the skill learning literature. However, there are a few scattered pieces of work that did demonstrate the parallel development of the two types of knowledge or the extraction of explicit knowledge from implicit knowledge (e.g, Willingham et al 1989, Stanley et al 1989, Sun et al 2000), contrary to usual top-down approaches in developing cognitive architectures. Many issues arise with regard to the interaction between implicit and explicit processes, which we need to look into if we want to better understand this interaction: How can we best capture implicit processes computationally? How can we best capture explicit processes computationally? How do the two types of knowledge develop along side each other and influence each other's development? Is bottom-up learning (or parallel learning) possible, besides top-down learning? How can they (bottom-up learning, top-down learning, and parallel learning) be realized computationally? How do the two types of acquired knowledge interact during skilled performance? What is the impact of that interaction on performance? How do we capture such impact computationally? === Prof. Ron Sun http://www.cecs.missouri.edu/~rsun CECS Department phone: (573) 884-7662 University of Missouri-Columbia fax: (573) 882 8318 201 Engineering Building West Columbia, MO 65211-2060 email: rsun@cecs.missouri.edu http://www.cecs.missouri.edu/~rsun http://www.cecs.missouri.edu/~rsun/journal.html http://www.elsevier.com/locate/cogsys === -------------------------------- End of Genetic Algorithms Digest ********************************