Workshops and Tutorials Schedule (PDF)

Introductory Tutorials

 Genetic Algorithms Erik Goodman Genetic Programming John Koza Evolution Strategies Thomas Baeck A Unified Approach to EC Ken DeJong Evolvable Hardware I Tetsuya Higuchi Linear GP Wolfgang Banzhaf Ant Colony Optimization Christian Blum Particle Swarm Intelligence Russell Eberhart Learning Classifier Systems     more info Tim Kovacs Probabilistic Model-Building GAs more info Martin Pelikan

Advanced Tutorials

 No Free Lunch (NFL) Darrell Whitley Genetic Algorithm Theory Jonathan Rowe Bioinformatics James A. Foster Taxonomy and Coarse Graining in EC more info Chris Stephens Multiobjective Optimization with EC  more info Eckart Zitzler Computational Complexity and EC Ingo Wegener Evolvable Hardware II Adrian Stoica Representations Franz Rothlauf Building on Biological Evolution Ingo Rechenberg Principled Efficiency Enhancement  more info Kumara Sastry Generalized Hill Climbing Algorithms    more info Sheldon H. Jacobson Statistics for EC Steffan Christensen, Mark Wineberg

Tutorials on Specialized Techniques and Applications

 Symbolic Regression in Genetic Programming more info Maarten Keijzer Grammatical Evolution Conor Ryan Quantum Computing Lee Spector Evolutionary Robotics Dario Floreano Evolutionary Music Al Biles Evolution and Resiliency Terry Soule Evolutionary Design Ian Parmee Interactive Evolution Hideyuki Takagi Optimization of Dynamic Environments Juergen Branke Spatially Structured EAs Marco Tomassini Industrial Evolutionary Computation A. Kordon, G. Smits, M. Kotanchek In Vitro Molecular Evolution     more info Byoung-Tak Zhang Evolving Neural Networks    more info Risto Mikkulainen Experimental Research in EC Mike Preuss, Thomas Bartz-Beielstein Fitness Approximation in EC     more info Yaochu Jin, Khaled Rasheed Constraint-handling Techniques used with EAs more info Carlos Coello-Coello The XCS Learning Classifier System: From Theory to Application     more info Martin Butz Experiences Implementing a GA-Based Optimizer in an Aerospace Engineering Application    more info Thomas Dickens Fitness Landscapes and Problem Difficulty    more info Jean-Paul Watson

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Introductory Tutorials

Learning Classifier Systems

Description:
Since the first Learning Classifier System (LCS) was introduced by Holland and Reitman in 1978, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining to automated innovation to on-line control. Classifier systems are currently enjoying a renaissance, with newer approaches, in particular Wilson's accuracy-based XCS, receiving a great deal of attention. LCS are also benefiting from advances in the field of Reinforcement Learning, and there is a trend toward developing connections between the two areas. The tutorial begins with an introduction to the basics of classifier systems, reviews more advanced techniques such as niche genetic algorithms and macroclassifiers, and introduces XCS and contrasts it with earlier systems.
 Speaker Bio: Tim Kovacs, Lecturer in Machine Learning, Department of Computer Science, University of Bristol, www.cs.bris.ac.uk/~kovacs/, holds a BA in Psychology, an MSc in Computer Science and a PhD in Machine Learning. His main interests are in intelligence, adaptive behaviour and machine learning, with an emphasis on evolutionary and other stochastic optimisation methods (e.g. genetic algorithms and ant colony optimisation) and reinforcement learning. Dr. Kovacs has published 20 papers in the last 4 years in areas including learning classifier systems theory, methodological issues in machine learning, the complexity of learning, game playing and the behaviour of social insects.
His PhD thesis won a British Computer Society / Conference of Professors and Heads of Computing Distinguished Dissertation Award and was published by Springer-Verlag. He compiled and maintains the on-line Learning Classifier Systems bibilography. He is an associate editor of the IEEE Transactions on Evolutionary Computation, has presented several tutorials at major international conferences and was an invited speaker at Benelearn 2004. He won a GECCO-2004 best paper award. He is a co-organiser of the Foundations of Learning Classifier Systems workshop at PPSN 2004 and of the inaugural 2004 Bristol/Bath regional workshop on Mathematics, Computation and Biology.

Probabilistic Model-Building GAs
Description: Probabilistic model-building algorithms (PMBGAs) replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. Replacing traditional crossover and mutation operators by building and sampling a probabilistic model of promising solutions enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimization enables the design of optimization techniques that can automatically adapt to the given problem.

There are many successful applications of PMBGAs, for example, Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and scheduling. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs). The tutorial Probabilistic Model-Building GAs will provide a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.

Speaker Bio:
Martin Pelikan received Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2002. Since 2003, he has been an assistant professor at the Dept. of Mathematics and Computer Science at the University of Missouri at St. Louis. Prior to joining the University of Missouri, he worked at the Illinois Genetic Algorithms Laboratory (IlliGAL), the German National Center for Information Technology at Sankt Augustin, the Slovak University of Technology at Bratislava, and the Swiss Federal Institute of Technology (ETH) at Zurich.

Pelikan worked as a researcher in genetic and evolutionary computation since 1995. His most important contributions to genetic and evolutionary computation are the Bayesian optimization algorithm (BOA), the hierarchical BOA (hBOA), the scalability theory for BOA and hBOA, and the efficiency enhancement techniques for BOA and hBOA. His current research focuses on extending BOA and hBOA to other problem domains, applying genetic and evolutionary algorithms to real-world problems with the focus on physics and machine learning, and designing new efficiency enhancement techniques for BOA and other evolutionary algorithms.

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Advanced Tutorials

Taxonomy and Coarse Graining in EC
Description: The theory of genetic dynamics, in both population biology and evolutionary computation, is difficult. In the latter it is also relatively undeveloped,there existing many different types of evolutionary algorithm, such as geneticalgorithms, genetic programming, evolutionary strategies etc. whose theoreticalrelationships are not very clear. Even for a particular type of algorithm, suchas genetic algorithms, the theoretical underpinnings have traditionally consisted of apparently antagonistic elements such as, on one side, Holland's Schema theorem, the associated Building Block Hypothesis, and on the other side, exact, microscopic models such as the Vose model. Besides offering a conceptual, qualitative understanding theory should also make quantitative predictions. In this aspect "engineering-type", approximate models have had more success than their more mathematically rigorous, exact counterparts. However, there are still no known systematic, general approximation schemes for describing the dynamics of an evolutionary algorithm.

Theoretical understanding is greatly facilitated by an adequate taxonomy - a practical example being the periodic table in chemistry. In this tutorial I showhow recent developments in exact, coarse-grained (schema-based) formulations of genetic dynamics point the way to a more adequate taxonomy of evolutionary algorithms.In particular, I will show how genetic algorithms and genetic programming can be"unified" in this way, leading to exact schema theorems for both, a deeper, more rigorousunderstanding of Holland's Schema theorem and the Building Block hypothesis, an extension of these to genetic programming, and a reconciliation of them with microscopic formulations such as the Vose model. We will see how the different formulations are simply coordinate transformations of one another in the space of representations. I will also show how tools and concepts from theoretical physics, such as therenormalization group, can further the theoretical understanding of genetic dynamicsand also offer potential systematic approximation schemes for describing them.
 Speaker Bio: Chris Stephens is Professor at the Institute for Nuclear Sciences of the Universidad Nacional Autonoma de Mexico. After receiving his undergraduate degree at The Queen's College, Oxford he completed his graduate work at the University of Maryland in the area of theoretical physics. He then had several postdoctoral positions, including the University of Utrecht, where he worked with Gerard 't Hooft, the 1999 Nobel Laureate in Physics and a Marie Curie Fellowship at the Dublin Institute for Advanced Studies. He has had visiting positions at various leading academic institutions, including the Weizmann Institute, the
Joint Institute for Nuclear Research, Dubna, the University of Birmingham and others. He is a founding partner of Adaptive Technologies Inc. a research company dedicated to the production of agent-based technologies for dynamical optimization in finance and industry. He is author or co-author of over 80 publications and his work has been cited over 800 times. He has given over 120 invited lectures in more than 20 countries. Among the academic honours he has received is the Jorge Lomnitz Prize of the Mexican Academy of Sciences. He is also a member of the editorial board of Genetic Programming and Evolvable Hardware.

His research interests are very broad, having published in a wide array of international journals - ranging from Classical and Quantum Gravity to the Journal of Molecular Evolution. An overiding theme, however, has been the Renormalization Group - a general methodology for solving complex, non-linear problems with many degrees of freedom via coarse graining - and, more recently, applying it to the area of genetic dynamics. His principal contribution in Evolutionary Computation has been to show how exact coarse-grained formulations lead to a unification and reconciliation of many previously antagonistic theoretical elements, such as Holland's Schema theorem and the Vose model.

Multi-objective Evolutionary Optimization

Description:
Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. This tutorial gives an overview of evolutionary multiobjective optimization with the focus on methods and theory. On the one hand, basic principles of multiobjective optimization are presented, and various algorithmic aspects such as fitness assignment and environmental selection are discussed in the light of state-of-the-art techniques. On the other hand, the tutorial covers several theoretical issues such as performance assessment and running-time analysis.
 Speaker Bio: Eckart Zitzler received degrees from University of Dortmund in Germany (diploma in computer science) and ETH Zurich in Switzerland (doctor of technical sciences). Since 2003, he has been Assistant Professor for Systems Optimization at the Computer Engineering and Networks Laboratory at the Department of Information Technology and Electrical Engineering of ETH Zurich, Switzerland. His research focuses on bio-inspired computation, multiobjective optimization, computational biology, and computer engineering applications.
Dr. Zitzler was General Co-Chairman of the first two international conferences on evolutionary multi-criterion optimization (EMO 2001 and EMO 2003), held in Zurich, Switzerland, and Faro, Portugal, respectively.

Principled Efficiency Enhancement

Description:
A key challenge in genetic and evolutionary computation (GEC) research is the design of competent genetic algorithms (GAs). By competent we mean GAs that can solve hard problems, quickly, reliably, and accurately, and much progress has been made along these lines (see for example, Goldberg, D. E. (2001). Design of Innovation). In essence, competent GA design takes problems that were intractable with first generation GAs and renders them tractable, oftentimes requiring only a subquadratic number of fitness evaluations. However, for large-scale problems, the task of computing even a subquadratic number of function evaluations can be daunting.
This is especially the case if the fitness evaluation is a complex simulation, model, or computation. This places a premium on a variety of efficiency enhancement techniques. While competence leads us from intractability to {\em tractability\/}, efficiency enhancement takes us from tractability to practicality.

In this tutorial, we will consider a four part decomposition of efficiency-enhancement techniques: (1) Parallelization, (2) Hyrbridization, (3) Time Continuation, and (4) Evaluation Relaxation.
We will develop a principled design methodology for different methodologies in each of efficiency-enhancement technique categories using facetwise modeling and dimensional arguments. The principled design methodology not only enables us to predict the maximum speed-up and scalability of each of the efficiency-enhancement methods, but also yields practical guidelines of using them in real-world problems.

Speaker Bio:
Kumara Sastry is a graduate student of Systems and Entrepreneurial Engineering at the Univeristy of Illinois and a Member of the Illinois Genetic Algorithms Laboratory. He has been actively consulting on genetic and evolutionary algorithms to industry, including a leading Israeli wireless company and a Fortune 100 company. His masters thesis on efficiency enhancement techniques was awarded the William A. Chittenden award for best graduate thesis in the Department of General Engineering. His research interests include efficiency enhancment of genetic agorithms, estimation of distribution algorithms, scalability of genetic and evolutionary computation, facetwise analysis of evolutionary algorithms, and multi-scale modeling in science and engineering.

Generalized Hill Climbing Algorithms: Theory and Practice

Description:
Generalized hill climbing (GHC) algorithms have been introduced as a unifying framework for addressing intractable discrete optimization problems. GHC algorithms provide a well-defined structure for classifying and studying a large body of stochastic and deterministic search strategies. Simulated annealing, threshold accepting, and tabu search, among others, can all be formulated as particular GHC algorithms. This tutorial reviews the GHC algorithm structure, and shows how many common search strategies can be described using the GHC algorithm framework. The advantages and disadvantages of the GHC framework are presented. Convergence and performance results for GHC algorithms are also discussed. Opportunities for future research with GHC algorithms are presented.
 Speaker Bio: Sheldon H. Jacobson is a Professor, Willett Faculty Scholar, and Director of the Simulation and Optimization Laboratory in the Department of Mechanical and Industrial Engineering at the University of Illinois. He has a B.Sc. and M.Sc. (both in Mathematics) from McGill University, and a M.S. and Ph.D. (both in Operations Research and Industrial Engineering) from Cornell University. His theoretical research interests include the analysis and design of heuristics for intractable discrete optimization problems. His applied research interests address problems in the manufacturing, aviation security, and health-care industries.
In 1998, he received the Application Award from the Institute of Industrial Engineers Operations Research Division. In 2002, he was named an Associate in the Center for Advanced Study at the University of Illinois, and was awarded the Aviation Security Research Award by Aviation Security International, the International Air Transport Association, and the Airports Council International. In 2003, he received the Best Paper Award in IIE Transactions Focused Issue on Operations Engineering and was named a Guggenheim Fellow by the John Simon Guggenheim Memorial Foundation. His research has been published in a wide spectrum of journals, and he has received research funding from several government agencies and industrial partners, including the National Science Foundation and the Air Force Office of Scientific Research.

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Tutorials on Specialized Techniques and Applications

Symbolic Regression in GP

Description:
The automated induction of mathematical descriptions of data using genetic programming is commonly referred to as symbolic regression. The main benefit of this approach is the perceived usefulness of the explicit mathematical expressions that are induced. This tutorial will concentrate on issues and techniques that arise when trying to evolve such explicit symbolic descriptions on data. The tutorial will concentrate on appropriateness of fitness measures; size control; interpretability; optimization of constants; robustness and generalization error; issues arising through finite floating point precision. Finally the Bayesian framework of induction will be presented in the context of symbolic regression.
 Speaker Bio: Graduating in 1995 in Cognitive Artificial Intelligence, Maarten Keijzer proceeded to work as a programmer/data mining consultant at Cap Gemini Adaptive Systems on the Predictive Data Mining software called OMEGA. Here much development was directed at the induction of explicit symbolic expressions on data ('symbolic regression'). In 1998 Maarten Keijzer started to pursue a Ph.D. at the Danish Technical University in conjunction with the Danish Hydraulic Institute under the supervision of Lars Kai Hansen and Vladan Babovic.The Ph.D. was concluded in 2001 with the thesis "Genetic Programming for Scientific Discovery".
From 2001 onwards he worked at the Free University of Amsterdam, as a senior researcher at KiQ Ltd, (again on the toolset called OMEGA), and as a senior researcher at WL | Delft Hydraulics. Maarten Keijzer's research interests have centred around genetic programming with emphasis to symbolic regression from as early as 1994.

In Vitro Molecular Evolution

Description: Unbeknown to most of the evolutionary computation researchers, biologists and biochemists have long been using the principle of genetic and evolutionary algorithms to design biomolecules with novel enzymatic activities. This approach, generally known as in vitro selection or directed evolution, starts with a library of candidate molecules and uses biochemical variation and selection techniques to derive fitter target molecules. On the other hand, biomolecules such as DNA and RNA provide interesting alternative materials for building evolutionary computers and other evolvable machines. In vitro molecular experiments allow us to play with up to the Avogadro number (6 x 10^23) of individuals in a single population. The existing biochemical techniques, such as polymerase chain reaction, gel electrophoresis, and fluorochromatography, provide massively parallel operators for assembly, replication, variation, and selection of "molecular genetic programs." In addition to solving computational problems in a more efficient way, the use of biomolecules in genetic and evolutionary computation opens up new applications in biomedical research, pharmaceutical industries, and nanotechnology. This tutorial aims (1) to review recent results on directed evolution from life sciences and biotechnology and (2) to provide the EC community with new research issues as to the theory, methodology, technology, and applications inspired by the in vitro molecular evolution approach. We will discuss the challenges and opportunities we face as EC researchers. The tutorial assumes an introductory level of knowledge in genetic and evolutionary computation, but does not require backgrounds in molecular biology or chemistry. EC researchers interested in bio- and nano-technologies would find this tutorial most exciting.
 Speaker Bio: Byoung-Tak Zhang is currently Associate Professor of the School of Computer Science and Engineering and the Graduate Programs in Bioinformatics and Cognitive Science at Seoul National University (SNU), Korea, and directs the Biointelligence Laboratory and the Center for Bioinformation Technology (CBIT). He received his Ph.D. in Computer Science from University of Bonn, Germany in 1992 and his BS and MS degrees in Computer Science and Engineering from SNU in 1986 and 1988, respectively.Prior to joining SNU, he had been a research associate at German National Research Center for Information Technology (GMD) from 1992-1995.
He has been a visiting professor at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) from August 2003 to August 2004. Byoung-Tak Zhang serves as an associate editor of IEEE Transactions on Evolutionary Computation, Advances in Natural Computation, and Genomics & Informatics, and on the editorial board of Genetic Programming and Evolvable Machines and Applied Soft Computing. His research interests include probabilistic models of learning and evolution, biomolecular/DNA computing, molecular learning/evolvable machines.

Evolutionary Neural Networks

Description:
Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: the state can be disambiguated through recurrency, and novel situations handled through pattern matching. In this tutorial, we will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining traditional neural network learning algorithms with evolutionary methods, and (3) applications of neuroevolution to game playing, robot control, resource optimization, and cognitive science.
 Speaker Bio: Risto Miikkulainen is a Professor of Computer Sciences at the University of Texas at Austin. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His recent research focuses on methods for evolving neural networks and applying these methods to game playing, robotics, and intelligent control. He is an author of over 150 articles on neuroevolution, connectionist natural language processing, and the computational neuroscience of the visual cortex. He is an editor of the Machine Learning Journal and Journal of Cognitive Systems Research.

Fitness Approximation in Evolutionary Computation

Description:
Evolutionary algorithms need a large number of fitness evaluations to get an acceptable solution. This poses huge difficulties in employing EAs to solve a wider range of real-world problems because fitness evaluations is often very expensive. This tutorial provides an overview of various methods for reducing expensive fitness evaluations in evolutionary computation. The tutorial covers the following major issues: * Motivations for fitness approximation in evolutionary computation * General methods for fitness approximation, such as problem approximation, functional approximation, fitness inheritance and imitation * Various frameworks for using fitness approximations, including the multi-population approaches, informed crossover and mutation, generation-based and individual-based evolution control * A short introduction to different approximate models and learning methods * Real-world applications of fitness approximation
 Speaker Bio: Yaochu Jin received the Ph.D. degree from Zhejiang University, Hangzhou, China, and the Dr.-Ing. degree from Ruhr-Universitaet Bochum, Bochum, Germany. He was an Associate Professor with the Electrical Engineering Department, Zhejiang University, a Visiting Scholar and a Researcher with the Institut fuer Neuroinformatik, Ruhr-Universitaet Bochum, and a Postdoctoral Associate with the Industrial Engineering Department, the State University of New Jersey, Piscataway. He joined the Honda R&D Europe,Offenbach, Germany in 1999. Currently, he is a Principal Scientist with the Honda Research Institute Europe.
His main research interests are fuzzy systems, neural networks and evolutionary computation with application to systems control and design optimization. He is the author of the book "Advanced Fuzzy Systems Design" (Heidelberg, Germany:Springer, 2003), and the editor of the book "Knowledge Incorporation in Evolutionary Computation" (Berlin, Germany:Springer, 2004). Dr. Jin is an Associate Editor of the IEEE Transactions on Control Systems Technology and the IEEE Transactions on Systems, Man, and Cybernetics, Part C. He is a Guest Editor of four journal special issues. He serves as the Chair of the Working Group on "Evolutionary Computation in Dynamic and Uncertain Environments" within the Evolutionary Computation Technical Committee of the IEEE Computational Intelligence Society. He is the Program Chair of the Second International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'05), and the Co-chair of the First and Second European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments. He is a Senior Member of IEEE, and a member of Council of Authors of ISGEC.

 Speaker Bio: Dr. Khaled Rasheed is an Assistant Professor at the Computer Science department, University of Georgia (USA). He received his Ph.D. from Rutgers University in January 1998 on his work on adapting genetic algorithms for problems in engineering design. His research interests include artificial intelligence, genetic algorithms, design optimization, computational biology, and machine learning. He has served on the program committees of several conferences including the Genetic and Evolutionary Computation
Conference and the International Conference on Machine Learning and as reviewer for several journals including IEEE transactions on Evolutionary Computation, the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, Machine Learning Journal and Artificial Intelligence in Engineering Design and Manufacturing.

Constraint-Handling Techniques used with Evolutionary Algorithms

Description: When used for global optimization, Evolutionary Algorithms (EAs) can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear) into their fitness function. Although the use of penalty functions (very popular with mathematical programming techniques) may seem an obvious choice, this sort of approach requires a careful fine tuning of the penalty factors to be used. Otherwise, an EA may be unable to reach the feasible region (if the penalty is too low) or may reach quickly the feasible region but being unable to locate solutions that lie in the boundary with the infeasible region (if the penalty is too severe). This has motivated the development of a number of approaches to incorporate constraints into the fitness function of an EA. This tutorial will cover the main proposals in current use, including novel approaches such as the use of tournament rules based on feasibility, multiobjective optimization concepts, hybrids with mathematical programming techniques (e.g., Lagrange multipliers), cultural algorithms, and artificial immune systems, among others. Other topics such as the importance of maintaining diversity, current benchmarks and the use of alternative search engines (e.g., particle swarm optimization, differential evolution, evolution strategies, etc.) will be briefly discussed (as time allows).
 Speaker Bio: Carlos Coello-Coello. Carlos Artemio Coello Coello received a BSc in Civil Engineering from the Universidad Autónoma de Chiapas in Mexico in 1991. Then, he was awarded a scholarship from the Mexican government to pursue graduate studies in Computer Science at Tulane University. He received a MSc and a PhD in Computer Science in 1993 and 1996, respectively. His PhD thesis was one of the first in the field now called evolutionary multiobjective optimization. Dr. Coello has been a Senior Research Fellow in the Plymouth Engineering Design Centre (in England) and a Visiting Professor at DePauw University (in the USA).

He is currently associate professor at CINVESTAV-IPN in Mexico City, Mexico. He has published over 130 papers in international peer-reviewed journals and conferences and one book on evolutionary multiobjective optimization which is part of the Genetic Algorithms and Evolutionary Com- putation Series edited by David E. Goldberg. He has also served as a technical reviewer for a number of journals and international conferences and actually serves as associate editor of the "IEEE Transactions on Evolutionary Computation" and as a member of the editorial board of "Engineering Optimization". He is member of the Mexican Academy of Science, Senior Member of the IEEE, and member of Sigma Xi, The Scientific Research Society. His current research interests are: evolutionary multiobjective optimization, constraint-handling techniques for evolutionary algorithms and evolvable hardware.

The XCS Learning Classifier System: From Theory to Application

Description: Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago originally calling them cognitive systems. The XCS classifier system maybe its currently most successful and most promising representative. As all LCSs, XCS combines the strength of reinforcement learning with the generalization and search capabilities of genetic algorithms resulting in a flexible, online-learning and generalizing predictive learning system. This tutorial focuses on the questions how and when XCS works and, derived from these questions, how XCS can be designed and enhanced to solve diverse online reinforcement, control, or general predictive problems. Particularly, a facetwise approach is proposed that partitions the learning biases of the system and analyzes the components separately respecting their possible interactions. The insights gained directly lead to a comprehensive application manual for XCS that outlines its (representation- and task-dependent) successful design and application to the problem at hand. Due to the simple, facetwise approach, the successful creation of more competent, truly cognitive systems appears to be within our grasp.

 Speaker Bio: Martin Volker Butz was born in Würzburg, Germany on August 4, 1975. Butz commenced his undergraduate studies in computer science with a minor in psychology at the Bayerische Julius-Maximilians Universität Würzburg in fall 1995. During his studies he spent one year at the Illinois Genetic Algorithms Laboratory (IlliGAL) as a visiting scholar. He graduated from the Bayerische Julius-Maximilians Universität Würzburg with honors in August, 2001. Since then, he has been a research assistant at the department of cognitive psychology at the Bayerische Julius-Maximilians Universität Würzburg.
In January, 2002, Butz joined the University of Illinois at Urbana-Champaign for his graduate studies. He completed his Ph.D. degree in Illinois in October, 2004. Since then, he is working on a European project on cognitive science at the department of cognitive psychology at the Bayerische Julius-Maximilians Universität Würzburg. His PhD thesis is called Rule-based Evolutionary Online Learning Systems: Learning Bounds, Classification, and Prediction''. The thesis proposes a general approach to classifier system analysis and analyzes the XCS classifier system in detail by the means of the approach. Results confirm the theoretical learning bounds as well as the wide applicability of the enhanced system.

Experiences Implementing a GA-Based Optimizer in an Aerospace Engineering Application

Description:
The Aero Grid and Paneling System (AGPS), is a Boeing-developed 3D geometry system which is a general-purpose tool for creating, interrogating, and visualizing 3D geometry. Boeing uses AGPS in their Engineering Analysis work throughout the company, for commercial, military, and space systems, and also offers AGPS to external customers. One key feature of AGPS is that is was designed as a geometry programming language, with a customizable GUI, allowing for custom applications to be easily written and tailored for domain-specific tasks. This also allows engineering processes to be highly automated. I recently added a Genetic-Algorithm-based parametric-optimizer to AGPS, with the goal of providing our users with a simple, easy-to-use capability to find geometric solutions. This tutorial presents my experiences, lessons learned, and design trade-offs with this effort, with the goal of providing others with a roadmap about issues involved and hopefully some helpful guidance in implementing an optimizer in their own applications, and providing a view from the user's perspective.
 Speaker Bio: Thomas Dickens Tom Dickens has been a Boeing engineer for the past 20 years, specializing in software architecture for aerodynamics research at Boeing Commercial. He became a Boeing Associate Technical Fellow in 2001, and has more than 30 papers published in various fields--including a paper at GECCO 2004. With a BS-EET and an MS-CE, Tom has designed and built both software systems and embedded hardware systems for Boeing, and is currently working projects in direct support of designing Boeing's new 7E7.
In the evenings, Tom has taught college classes for the past 15 years at Henry Cogswell College, the University of Phoenix, and the University of Washington.

Fitness Landscapes and Problem Difficulty

Description: The effectiveness of any metaheuristic, e.g., genetic algorithms or simulated annealing, is dictated by the degree to which the algorithm is able to exploit the structure of the underlying search space or fitness landscape. This tutorial will begin with a formal definition of the concept of a fitness landscape, followed by an overview of the various structural features present in the fitness landscapes of many well-known NP-hard combinatorial optimization problems. Following the introductory material, I will discuss the relationship between fitness landscape structure and algorithm run-time behavior, explain why certain fitness landscape features often cause problems for various metaheuristics, and illustrate how metaheuristics can be designed to exploit the presence of specific fitness landscape features. The tutorial concludes with a brief survey of open, fundamental research questions in the area of metaheuristic analysis and design.
 Speaker Bio: Dr. Jean-Paul Watson is currently a researcher at Sandia National Laboratories in Albuquerque, New Mexico. His primary activities include the design and analysis of metaheuristics and the development of metaheuristics for difficult optimization problems originating in both military and homeland security applications. He holds a Ph.D. in Computer Science from Colorado State University and has published numerous articles and papers on metaheuristics, with an emphasis on fitness landscape structure, genetic algorithms, and tabu search.

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