From Peter.Bosman at cwi.nl Tue Jan 3 03:19:28 2006 From: Peter.Bosman at cwi.nl (Peter A.N. Bosman) Date: Tue Jan 3 18:24:15 2006 Subject: [Population-biology] CFP: OBUPM-2006 Workshop at GECCO 2006 Message-ID: <43BA3390.4070603@cwi.nl> [We apologize for cross-postings] Workshop Announcement and Call for Participation Workshop on OPTIMIZATION BY BUILDING AND USING PROBABILISTIC MODELS (OBUPM-2006) --- Special focus on continuous optimization --- http://minner.bwl.uni-mannheim.de/obupm06/ to be held as part of the 2006 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2006) July 8-12, 2006 (Saturday-Wednesday) Renaissance Seattle Hotel Seattle, Washington, USA Organized by ACM SIG-EVO www.sigevo.org/GECCO-2006 PAPER SUBMISSION DEADLINE FOR WORKSHOP: March 12, 2006 TOPIC ===== Genetic- and evolutionary algorithms (GEAs) evolve a population of candidate solutions to a given optimization problem using two basic operators: (1) selection and (2) variation. Selection introduces a pressure toward high-quality solutions, whereas variation ensures exploration of the space of all potential solutions. Two variation operators are common in current genetic- and evolutionary computation (GEC): (1) crossover, and (2) mutation. Crossover creates new candidate solutions by combining bits and pieces of promising solutions, whereas mutation introduces slight perturbations to promising solutions to explore their immediate neighborhood. However, fixed, problem-independent variation operators often fail to effectively exploit important features of high-quality selected solutions. One way to make variation operators more powerful and flexible is to replace traditional variation of GEAs by the following two steps: 1. Build a probabilistic model of the selected promising solutions, and 2. sample the built model to generate a new population of candidate solutions. Algorithms based on this principle are commonly called estimation-of-distribution algorithms (EDAs) but are also known as probabilistic model-building genetic algorithms (PMBGAs) and as iterated density-estimation evolutionary algorithms (IDEAs). The general purpose of this workshop is to present and discuss * recent advances in EDAs, * new theoretical and empirical results, * applications of EDAs, and * promising directions for future EDA research. SCOPE ===== The OBUPM-2006 workshop has a specific focus on continuous optimization. Most work in the OBUPM area, and concordantly the most promising results, has been on discrete optimization using discrete representations. An interesting research topic is how these successes can be carried over to continuous optimization. Initial investigations have met with less convincing results than in the case of discrete optimization. Recently, the amount of research into this direction has been increasing and has lead to new insights about the design of EDAs for continuous optimization. In the light of this trend the OBUPM-2006 workshop aims to push onward the state-of-the-art in EDAs for continuous optimization, both theoretical as well as empirical by * providing an opportunity for researchers to present their latest work and * organizing a panel discussion on the most important aspects regarding EDAs for continuous optimization. For completeness it should be noted that the special focus of the OBUPM-2006 workshop includes the use of any EDA approach for continuous optimization (i.e. also discrete EDAs), both single-objective as well as multi-objective. SUBMITTING TO OBUPM-2006 ======================== The workshop will feature a series of selected presentations. To submit your contribution, send your ACM-formatted paper in Postscript or PDF by e-mail to Peter A.N. Bosman at Peter.Bosman@cwi.nl. Papers should not exceed the limit of 8 pages and must meet with deadline of the workshop (see important dates for details). In case you can not submit your paper electronically, please contact one of the workshop chairs. Please note that all contributions must abide ACM formatting rules because all contributions will be on the conference CD as well as in the ACM digital library. Failing to comply with the ACM formatting rules will result in exclusion from the proceedings. Visit http://www.sigevo.org/gecco-2006/submitting.html for formatting details. IMPORTANT DATES FOR OBUPM-2006 ============================== March 12, 2006: Paper submission deadline April 1, 2006: Notification of acceptance April 19, 2006: Camera-ready copy deadline WEBSITE ======= The workshop program and further information can be found online. Please check http://minner.bwl.uni-mannheim.de/obupm06/ regularly for the latest information. In case you have any questions, please contact one of the workshop organizers. We are looking forward to meeting you at OBUPM-2006! WORKSHOP ORGANIZERS =================== Peter A.N. Bosman Centre for Mathematics and Computer Science Theme of Computational Intelligence and Multi-Agent Games Email: Peter.Bosman @ cwi.nl J?rn Grahl Mannheim Business School Department of Logistics Email: joern.grahl @ bwl.uni-mannheim.de Kumara Sastry University of Illinois at Urbana-Champaign Illinois Genetic Algorithms Laboratory Email: ksastry @ uiuc.edu Martin Pelikan University of Missouri at St. Louis Department of Mathematics and Computer Science Email: pelikan @ cs.umsl.edu From abraham.ajith at gmail.com Wed Jan 4 06:11:32 2006 From: abraham.ajith at gmail.com (Ajith Abraham) Date: Wed Jan 4 13:57:34 2006 Subject: [Population-biology] Call for Book Chapters - Springer SCI Series Message-ID: ------------------------------------------------ CALL FOR BOOK CHAPTERS -- (Springer SCI Series) ------------------------------------------------ Hybrid Evolutionary Systems http://www.softcomputing.net/cec06/ Evolutionary Computation has become an important problem solving methodology among many researchers working in the area of computational intelligence. The population based collective learning process, self adaptation and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce etc. As we all know, the problems of the future will be more complicated in terms of complexity and data volume. Hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty and vagueness. A fundamental stimulus to the investigations of hybrid approach is the awareness that combined approaches will be necessary to solve some of the real world problems. This edited volume is targeted to present the latest state-of-the-art methodologies in 'Hybrid Evolutionary Systems'. Editors invite authors to submit their original and unpublished work that communicates current research on 'Hybrid Evolutionary Systems', regarding both the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. Topics of interest include but not limited to the following: I. Optimizing the Performance of Evolutionary Algorithms * Neural networks assisted evolutionary computation * Bayesian methods assisted evolutionary computation * Fuzzy system assisted evolutionary computation * Rough sets assisted evolutionary computation * Hybridization of evolutionary algorithms with particle swarm optimization * Hybridization of evolutionary algorithms with other global optimization techniques (simulated annealing, Tabu search, GRASP etc.) * Hybridization of evolutionary algorithms with bacterial foraging * Hybridization of evolutionary algorithms with molecular computing (DNA computing and membrane computing) * Hybridization of evolutionary algorithms with quantum computing * Hybridization of evolutionary algorithms with optical computing * Hybridization of evolutionary algorithms with other bionics II. Optimization of Intelligent Systems Using Evolutionary Computation * Evolutionary artificial neural networks * Evolutionary fuzzy systems (genetic fuzzy systems) * Integration with connectionist learning and fuzzy inference systems * Integration of evolutionary computation with case-based reasoning, inductive logic programming, grammatical inference etc. * Integration with Multi-Agent Systems III. This volume is also oriented towards real world applications where a direct approach might fail. * Multiobjective optimization applications * Financial modeling * Intrusion detection and cryptography * NP hard problems * Bioinformatics * Data mining * Knowledge management * Natural language processing * Image processing * Nonlinear network problems * Planning and scheduling * Brain-computer interface technologies Chapters Submission The book is intended to be published in the Springer Verlag, Series - 'Studies in Computational Intelligence'. Please prepare the manuscript using the author guidelines and format given in the following link: Author Guidelines ** Author Guidelines and Format ** Authors are invited to submit their original and unpublished work by email to . Papers have to be no more than 40 pages length. All chapters will be peer - reviewed by three or more independent referees. The time schedule for this publication is given below. Deadlines: Authors Intention to Contribute (with an abstract): January 15, 2006 Chapter Submission: February 28, 2006 Notification of Acceptance: April 30, 2006 Camera-ready Submission: June 15, 2006 Publication: September 2006 Volume Editors Crina Grosan Ph.D. Babes-Bolyai University Cluj-Napoca, Romania http://www.cs.ubbcluj.ro/~cgrosan Ajith Abraham Ph.D. Chung-Ang University Seoul, Korea http://www.softcomputing.net Please direct all your queries to From tinman4 at yahoo.com Sat Jan 28 12:37:32 2006 From: tinman4 at yahoo.com (tinman4) Date: Sat Jan 28 20:06:06 2006 Subject: [Population-biology] K2D ***Hot stuff - check this out !!! K2D Message-ID: http://www.kaneva.com/checkout/stream.aspx?assetId=2017&free=0 From Yaochu.Jin at honda-ri.de Tue Jan 31 03:26:35 2006 From: Yaochu.Jin at honda-ri.de (Yaochu.Jin@honda-ri.de) Date: Tue Jan 31 12:55:57 2006 Subject: [Population-biology] Special Session on "Multi-objective Machine Learning" submission deadline extended Message-ID: (Apologize if you receive multiple copies of this message.) Call for Papers Special Session on "Multi-objective Machine Learning" 2006 International Joint Conference on Neural Networks (part of WCCI'06) July 16-21, Vancouver, Canada http://www.wcci2006.org/ Organized by Yaochu Jin (yaochu.jin@honda-ri.de) URL: http://www.soft-computing.de/CFP_SS_MOML.html The submission deadline has been extended to February 15, 2006 Motivation and Scope: Machine learning usually has to achieve multiple targets, which are often conflicting with each other. For example in feature selection, minimizing the number of features and the maximizing feature quality are two conflicting objectives. It is also well realized that model selection has to deal with the trade-off between model complexity and approximation or classification accuracy. Traditional learning algorithms attempt to deal with multiple objectives by combining them into a scalar cost function so that multi-objective machine learning problems are reduced to single-objective problems. Recently, increasing interest has been shown in applying Pareto-based multi-objective optimization to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful in 1) improving the performance of the traditional single-objective machine learning methods 2) generating highly diverse multiple Pareto-optimal models for constructing ensembles and, 3) in achieving a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This proposed special session intends to further promote research interests in multi-objective machine learning by presenting the most recent research results and discussing the main challenges in this area. Topics include but are not limited to * multi-objective clustering, feature extraction and feature selection * multi-objective model selection to improve the performance of learning models, such as neural networks, support vector machines, decision trees, and fuzzy systems * multi-objective model selection to improve the interpretability of learning models, e.g., to extract symbolic rules from neural networks, or to improve the interpretability of fuzzy systems * multi-objective generation of learning ensembles * multi-objective learning to deal with tradeoffs between plasticity and stability, long-term and short-term memories, specialization and generalization * multi-objective machine learning applications Submission: All special session papers must be submitted no later than January 31, 2005 through the conference webpage at http://139.78.75.247/WCCI-Web_paper_submit.html. Please choose "S.Special Sessions, Sa: Multi-objective machine learning" as your main research topic -------------- next part -------------- An HTML attachment was scrubbed... URL: http://www.bio.net/bionet/mm/pop-bio/attachments/20060131/d6dfab67/attachment.html