General Information

Tutorial slides will be available on CD-ROM and distributed at the conference.

Tutorials will be presented on Saturday, July 7 and Sunday, July 8, 2007. View the tutorial schedule.

For more information about tutorials, contact Anikó Ekárt at

Call for tutorial proposals closed on 1 December 2006.
Notification of acceptance: 16 December 2006



Camera ready file preparation and submission instructions

Planned Free Tutorials


Introductory

Genetic Algorithms
more info
Erik Goodman
Genetic Programming
more info:
John Koza
Evolution Strategies              
Martin Schütz
Evolutionary Computation: A Unified Approach 
more info:
Kenneth De Jong
Ant Colony Optimization
more info:
Christian Blum
Learning Classifier Systems
more info:
Martin V. Butz
Probabilistic Model-Building GAs
more info:
Martin Pelikan
Grammatical evolution          
Conor Ryan
Coevolution                            
more info:
Edwin de Jong,
Kenneth Stanley,
Paul Wiegand
Particle Swarm Optimization
more info:
Andries Engelbrecht,
Xiaodong Li
Beowulf Clusters for Evolutionary Computation
Arun Khoshla,
Pramod Kumar Singh,
Diptanu Gon Chowdhary


Advanced

GA Theory
Jonathan Rowe
GP Theory
more info:
Riccardo Poli,
Bill Langdon
Representations for Evolutionary Algorithms 
more info
Franz Rothlauf
No Free Lunch
more info
Darrell Whitley
Bioinformatics
more info:
Jason Moore
Evolutionary Multiobjective Optimization
more info
Eckart Zitzler,
Kalyanmoy Deb
Industrial Evolutionary Computation
more info
Arthur Kordon,
Guido Smits,
Mark Kotanchek
Constraint Handling Techniques
Used with EAs  
more info
Carlos Coello Coello
Statistics for EC
Mark Wineberg
Coevolution
Sevan Ficici,
Anthony Bucci
Evolutionary Practical Optimisation   
more info
Kalyanmoy Deb
Computational Complexity and EC
more info:
Thomas Jansen,
Frank Neumann
Complex networks and Evolutionary Computation
more info:
J.J. Merelo,
Carlos Cotta
Particle Swarm Optimization for Fuzzy Models
Arun Khosla
Fitness Landscapes and Problem Hardness in Evolutionary Computation
more info:
Leonardo Vanneschi,
Sebastien Verel
An Information Perspective on Evolutionary Computation
more info:
Yossi Borenstein


Specialized Techniques and Applications

Experimental Research in EC
more info:
Mike Preuss,
Thomas Bartz- Beielstein
Symbolic Regression in GP
Maarten Keijzer
Evolutionary Neural Networks
more info:
Risto Miikkulainen
Quantum Computing
Lee Spector
Evolvable Hardware
more info
Lukas Sekanina
Artificial Development
Pauline Haddow,
Gunnar Tufte
Evolutionary Games
more info:
Marco Tomassini
Evolutionary Design
more info:
Ian Parmee

Evolutionary Multiobjective Combinatorial Optimization
more info:

Rajeev Kumar
Evolving Neural Network Ensembles
Xin Yao
Evolutionary Computer Vision
more info:
Gustavo Olague
Bio-inspired Telecommunications
more info:
Muddassar Farooq
Distributed Evolutionary Computation for Fun and Profit
more info:
J.J. Merelo,
J.L.J. Laredo

 



Introductory


Genetic Algorithms:

Description of Tutorial

The Introduction to Genetic Algorithms Tutorial is aimed at GECCO attendees with limited knowledge of genetic algorithms, and will start “at the beginning,” describing first a “classical” genetic algorithm in terms of the biological principles on which it is loosely based, then present some of the fundamental results that describe its performance. It will cover many variations on the classical model, some successful applications of genetic algorithms, and advances that are making genetic algorithms more useful.


Erik Goodman

Biosketch:

Erik Goodman is a Professor of Electrical and Computer Engineering and of Mechanical Engineering at Michigan State University. He studied genetic algorithms under John Holland at the University of Michigan, before they had been named. His use of a genetic algorithm in 1971 to solve for 40 coefficients of a highly nonlinear model of a bacterial cell was the first known GA application on a real-world problem, and ran for nearly a year on a dedicated computer. He has used genetic algorithms in his research ever since, including for parameterization of complex ecosystem models, for evolution of cooperative behavior in artificial life, for factory layout and scheduling, for protein folding and ligand docking, for design of shape and layup of composite structures, and for data mining and pattern classification.

Much of his recent research has been in development of new types of parallel genetic algorithms and in design methods for mechatronic systems using genetic programming. He is co-founder and VP Technology of Red Cedar Technology, which provides tools for automated engineering design based on evolutionary computation. He chaired ICGA-97 and GECCO-2001, chaired GECCO’s sponsoring organization, ISGEC, from 2001-2004, and is now chair of ACM SIGEVO, its successor.

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Genetic Programming:


John R. Koza

Biosketch:

John R. Koza received his Ph.D. in computer science from the University of Michigan in 1972, working under John Holland. From 1973 through 1987, he was co-founder, chairman, and CEO of Scientific Games Inc. where he co-invented the rub-off instant lottery ticket used by state lotteries. He has taught a course on genetic algorithms and genetic programming at Stanford University since 1988, where he is currently a Consulting Professor in the Biomedical Informatics Program in the Department of Medicine and in the Department of Electrical Engineering at Stanford University. He is Vice-Chair of SIGEVO, and has been on the Business Committee of GECCO since it started in 1999. He has written four books on genetic programming and one on presidential elections.

 

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Evolutionary Computation: A Unified Approach:

Description of Tutorial

The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to se the relationships between them, assess strengths and weaknesses, and make good choices for new application areas.

This tutorial is intended to give an overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and facilitates intelligent design choices. The use of this framework is then illustrated by showing how traditional EAs can be compared and contrasted with it, and how new EAs can be effectively designed using it.

Finally, the framework is used to identify some important open issues that need further research.


Kenneth A. De Jong

Biosketch:

Kenneth A. De Jong received his Ph.D. in computer science from the University of Michigan in 1975. He joined George Mason University in 1984 and is currently a Professor of Computer Science, head of the Evolutionary Computation laboratory, and the Associate Director of the Krasnow Institute. In addition, he serves as the Chief Scientist for the Navy Center for Applied Research in AI. His research interests include genetic algorithms, evolutionary computation, machine learning, and adaptive systems. He is currently involved in research projects involving the development of new evolutionary algorithm (EA) theory, the use of EAs as heuristics for NP-hard problems, and the application of EAs to the problem of learning task programs in domains such as robotics, diagnostics, navigation and game playing.

He is also interested in experience-based learning in which systems must improve their performance while actually performing the desired tasks in environments not directly their control or the control of a benevolent teacher. Support for these projects is provided by DARPA, ONR, and NRL. He is an active member of the Evolutionary Computation research community and has been involved in organizing many of the workshops and conferences in this area. He is the founding editor-in-chief of the journal Evolutionary Computation (MIT Press), and a member of the board of the ACM SIGEVO. He is the recipient of an IEEE Pioneer award in the field of Evolutionary Computation and a lifetime achievement award from the Evolutionary Programming Society.

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Ant Colony Optimization:

Description of Tutorial

Ant colony optimization (ACO) is a metaheuristic method that was first proposed in the early 90's by Marco Dorigo and colleagues. The inspiring source of ACO is the foraging behavior of real ants. When searching for food, ants initially explore the area surrounding their nest in a random manner. As soon as an ant finds a food source, it evaluates quantity and quality of the food and carries some of the found food to the nest. During the return trip, the ant deposits a chemical pheromone trail on the ground. The quantity of pheromone deposited, which may depend on the quantity and quality of the food, will guide other ants to the food source. The indirect communication between the ants via the pheromone trails allows them to find shortest paths between their nest and food sources. This functionality of real ant colonies is exploited in artificial ant colonies in order to solve discrete optimization problems.

The tutorial consists of two parts. The first part is concerned with the basics of ACO algorithms. We deal with questions such as the translation of the biological inspiration into a technical algorithm. The most successful ACO variants are presented. In the second part, some of the more advanced features of ACO algorithms are presented in the context of interesting applications. Finally, the tutorial concludes with examples of how to hybridize ACO algorithms with other optimization techniques.


Christian Blum

Biosketch:

Christian Blum received the Diploma (Master of Science) degree in mathematics in 1998 from the Universität Kaiserslautern (Germany), and the doctoral degree in applied sciences in 2004 from the Université Libre de Bruxelles (ULB, Belgium). From 1999 to 2000 he was with the Advanced Computation Laboratory (ACL) of the Imperial Cancer Research Fund (ICRF) in London (UK), and from 2000 to 2004 he was with IRIDIA at the Université Libre de Bruxelles (ULB, Belgium). He currently is a " Ramon y Cajal" research fellow at the ALBCOM research group of the Universitat Politècnica de Catalunya (UPC, Spain). Current subject of his research is the hybridization of metaheuristics with more classical artificial intelligence and operations research methods. He is co-founder of the International Workshops on Hybrid Metaheuristics (HM).

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Learning Classifier Systems

Description of Tutorial

When Learning Classifier Systems (LCSs) were introduced by John H. Holland in the 1970s, the intention was the design of a highly adaptive cognitive system. Since then, LCSs came a long way. Interest strongly decreased in the late 80s and early 90s due the complex interactions of several learning mechanisms. However, since the introduction of the accuracy-based XCS classifier system by Stewart W. Wilson in 1995 and the modular analysis of several LCSs thereafter, interest re-gained momentum. Current research has shown that LCSs can effectively solve data-mining problems, reinforcement learning problems, other predictive problems, and cognitive control problems. Hereby, it was shown that performance is machine learning competitive, but learning is taking place online and is often more flexible and highly adaptive. Moreover, system knowledge can be easily extracted.The Learning Classifier System tutorial provides a gentle introduction to LCSs and their general functioning. It then surveys the current theoretical understanding of the systems and their proper application to various problem domains. Finally, we provide a suite of current successful LCS applications and discuss the most promising areas for future applications.


Martin V. Butz

Biosketch:

Martin V. Butz received his Diploma in computer science from the University of Wuerzburg, Germany in 2001 with the thesis topic: “Anticipatory Learning Classifier Systems”. He then moved on to the University of Illinois at Urbana-Champaign for his PhD studies under the supervision of David E. Goldberg. Butz finished his PhD in computer science on “Rule-based evolutionary online learning systems: Learning Bounds, Classification, and Prediction” in October, 2004. The thesis puts forward a modular, facet-wise system analysis for Learning Classifier Systems (LCSs) and analyzes and enhances the XCS classifier system.

Since October 2004, Butz is working back at the University of Würzburg at the Department of Cognitive Psychology III on the interdisciplinary cognitive systems project “MindRACES: From reactive to anticipatory cognitive embodied systems”.
Butz is the co-founder of the “Anticipatory Behavior in Adaptive Learning Systems (ABiALS)” workshop series and is currently also co-organizing the “International Workshop on Learning Classifier Systems (IWLCS 2007)”. Moreover, Butz has published and co-edited four books on learning classifier systems and anticipatory systems. Currently, Butz is focussing on the design of highly flexible and adaptive cognitive system architectures, which are inspired by studies in cognitive psychology, evolutionary biology, and behavioral neuroscience.

http://www.psychologie.uni-wuerzburg.de/i3pages/butz

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Probabilistic Model-Building Algorithms (PMBGAs)

Description of Tutorial

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. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs).

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.

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.


Martin Pelikan

Biosketch:

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 in St. Louis. In 2006 Pelikan founded the Missouri Estimation of Distribution Algorithms Laboratory (MEDAL). Prior to joining the University of Missouri, he worked at the Illinois Genetic Algorithms Laboratory (IlliGAL), the German National Center for Information Technology in Sankt Augustin, the Slovak University of Technology in Bratislava, and the Swiss Federal Institute of Technology (ETH) in Zurich.

Pelikan has 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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Coevolution 


Kenneth O. Stanley

Biosketch:

Kenneth O. Stanley is an Assistant Professor in the School of Computer Science at the University of Central Florida. His research focuses on artificially evolving complex solutions to difficult real-world tasks. He graduated with a B.S.E. in Computer Science Engineering and a minor in Cognitive Science from the University of Pennsylvania in 1997. He received an M.S. in Computer Science in 1999, and a Ph.D. in 2004 at the University of Texas at Austin. He has won best paper awards for his work on NEAT (at the 2002 Genetic and Evolutionary Computation Conference) and for his work on NERO (at the IEEE 2005 Symposium on Computational Intelligence and Games), and also won the Independent Games Festival Student Showcase Award (at the 2006 Game Developers conference) for NERO.

He has published papers in JAIR, Evolutionary Computation, IEEE Transactions on Evolutionary Computation, and Artificial Life journals.

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Particle Swarm Optimization:

Description of Tutorial

Since the first publication of Particle Swarm Optimization (PSO) in 1995, the number of research papers on PSO, and the number of researcher in PSO, have exploded. Many variations of the PSO have been developed to improve its performance, studies have been done to understand the dynamics of particles, and adaptations have been developed to apply the PSO to different optimization problem types.

This tutorial is planned in two parts, one introductory, and one addressing more advanced topics. The introductory part will have as its objective to provide the attendee with an overview of PSO and its basic variations. A significant problem with the standard PSO will be illustrated, and a few results from studies of particle trajectories will be presented. The advanced part of the tutorial will consider PSO models that are specifically designed for multimodal optimization, multiobjective optimization, optimization in dynamic environments, and constraint handling.

In more detail, the tutorial will cover the following topics, in the order listed below:

Introduction:

1. Basic PSO: The philosophy of PSO will be discussed, and the basic (original) PSO algorithms will be explained and illustrated. The need for social network structures will be discussed, as well as the importance of PSO control parameters, basic variations (velocity clamping, inertia, constriction). An overview of performance criteria will be given.
2. Particle Trajectories: Illustrations of particle trajectories and the influence of parameter choices on trajectories will be given. Heuristics for the selection of control parameter values will be given.
3. PSO problem: A problem with the PSO that causes premature convergence will be discussed, and solutions proposed.

Advanced:

4. Multimodal optimization: speciation and niching techniques used in PSO will be described. Illustration of a speciation-based PSO will be provided, as well as analysis of its performance and some research issues.
5. Multiobjective optimization: an overview of existing multiobjective PSO models will be provided. In addition, a multiobjective PSO will be shown to have a fast convergence and nice spread of solutions across the Pareto optimal front.
6. Optimization in dynamic environments: The rationale of PSO being suitable for optimization in a dynamic environment will be given. Recent developments on this topic will be presented, with illustrations of simulation runs of a PSO model on the Moving Peaks benchmark test functions.
7. Constraint handling: a brief overview of existing constraint handling techniques adopted in PSO will be provided.


Andries Engelbrecht

Biosketches:

Andries Engelbrecht
is a Full Professor in Computer Science at the Department of Computer Science, University of Pretoria. He manages a research group of 40 Masters and PhD students, most of whom do research in swarm intelligence. He has recently authored a book, “Fundamentals of Computational Swarm Intelligence”, published by Wiley. He is also the author of a book, “Computational Intelligence: An Introduction”, also published by Wiley. He has presented tutorials on PSO and Coevolutionary methods for evolving game agents at IEEE CEC 2005 and IEEE CIG 2005 respectively. He is co-presenter of a tutorial on PSO and DE at IEEE CEC 2007. He has published approximately 100 papers in the last decade, serves as a reviewer for a number of conferences and journals, and is an associate-editor of IEEE TEC, and serves on the editorial board of two other journals. He served as a member of a large number of conference program committees, and is in the organizing committee of several conferences.


Dr Xiaodong Li is a Senior Lecturer in the School of Computer Science and IT, RMIT University, Melbourne, Australia. His research interest includes Artificial Intelligence, Evolutionary Computation, Artificial Neutral Networks, Swarm Intelligence, Multiobjective Optimization, Optimization in Dynamic Environments, and their applications to real-world problems. Dr. Li is an IEEE member, and a member of SIGEVO. He serves as a technical committee member of Working Group on Swarm Intelligence, IEEE Computational Intelligence Society. He was the organizing chair for special session on Swarm Intelligence in CEC'03, CEC'04, and CEC'06. He has recently presented a tutorial on Particle Swarm Optimization at SEAL’06. He is the publicity chair and a member of the steering committee for the IEEE Swarm Intelligence Symposium 2007 (SIS'07). He is the general and programme chair for SEAL’08, which will be held in Melbourne, Australia in 2008.


Xiaodong Li

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Advanced


Genetic Programming Theory

Description of Tutorial

GP is a complex adaptive system with zillions of degrees of freedom. Experimental studies require the experimenter to choose which problems, parameter settings and descriptors to use. Plotting the wrong data increases the confusion about GP's behaviour, rather than clarify it.

We treat GP as a search process and explain its behaviour by considering the search space, in terms of its size, its limiting fitness distributions and also the halting probability. Then we shall use modern schema theory to characterise GP search. We finish with lessons and implications.

Knowledge of genetic programming will be assumed.


Riccardo Poli

Biosketches:

Riccardo Poli is a professor in the Department of Computer Science at Essex. His main research interests include genetic programming (GP) and the theory of evolutionary algorithms. In July 2003 Prof.Poli was elected a Fellow of The International Society for Genetic and Evolutionary Computation (ISGEC) ``... in recognition of sustained and significant contributions to the field and the community''. He has published over 180 refereed papers on evolutionary algorithms (particularly genetic programming), neural networks and image/signal processing. With W.B.Langdon, he has co-authored the book Foundations of Genetic Programming . He has been co-founder and co-chair of EuroGP, the European Conference on Genetic Programming for 1998, 1999, 2000 and 2003.

Prof.Poli was the chair of the GP theme at the Genetic and Evolutionary Computation Conference (GECCO) 2002 (the largest conference in the field) and was co-chair of the prestigious Foundations of Genetic Algorithms (FOGA) Workshop in 2002.
He has been (the first non-US) general chair of GECCO in 2004, and served as a member of the business committee for GECCO 2005.
He is technical chair of the International workshop on Ant Colony Optimisation and Swarm Intelligence (ANTS 2006) and competition chair for GECCO 2006. He is an associate editor of ``Evolutionary Computation'' (MIT Press), ``Genetic Programming and Evolvable Machines'' (Springer) and the ``International Journal of Computational Intelligence Research'' (IJCIR). Prof. Poli has been programme committee member of over 50 international events. He has presented invited tutorials on GP at 10 international conferences. He is a member of the EPSRC Peer Review College and has attracted, as principal Investigator or co-investigator, funding for over GBP 1.8M from EPSRC, DERA, Leverhulme Trust, Royal Society, and others.
W. B. Langdon is a senior research fellow in computer systems engineering at Essex University. He worked on distributed real time databases for control and monitoring of power stations at the Central Electricity Research Laboratories. He then joined Logica to work on distributed control of gas pipelines and later on computer and telecommunications networks. After returning to academe to gain a PhD in genetic programming (sponsored by National Grid plc.), he has worked at the University of Birmingham, the CWI, UCL and, most recently, in Essex University.


W. B. Langdon

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Representations for Evolutionary Algorithms:

Description of Tutorial

Successful and efficient use of evolutionary algorithms (EAs) depends on the choice of the problem representation - that is, the genotype and the mapping from genotype to phenotype - and on the choice of search operators that are applied to this representation. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given representation.

In EA practice one can distinguish two complementary approaches. The first approach uses indirect representations where a solution is encoded in a standard data structure, such as strings, vectors, or discrete permutations, and standard off-the-shelf search operators are applied to these genotypes.

To evaluate the solution, the genotype needs to be mapped to the phenotype space. The proper choice of this genotype-phenotype mapping is important for the performance of the EA search process. The second approach, the direct representation, encodes solutions to the problem in its most 'natural' space and designs search operators to operate on this representation.

Research in the last few years has identified a number of key concepts to analyse the influence of representation-operator combinations on EA performance.

These concepts are

• locality,
• redundancy, and
• bias.

Locality is a result of the interplay between the search operator and the genotype-phenotype mapping. Representations are redundant if the number of phenotypes exceeds the number of possible genotypes. Furthermore, redundant representations can lead to biased encodings if some phenotypes are on average represented by a larger number of genotypes. Finally, a bias need not be the result of the representation but can also be caused by the search operator.

The tutorial gives a brief overview about existing guidelines for representation design, illustrates the different aspects of representations, gives a brief overview of theoretical models describing the different aspects, and illustrates the relevance of the aspects with practical examples.

It is expected that the participants have a basic understanding of EA principles.



Franz Rothlauf

Biosketch:

Franz Rothlauf (M'98) received a Diploma in Electrical Engineering from the University of Erlangen, Germany, and a Ph.D. in Information Systems from the University of Bayreuth, Germany, in 1997, and 2001, respectively.

He is currently Professor at the Department of Information Systems, Mainz University, Germany. He has published more than 45 technical papers in the context of evolutionary computation, co-edited several conference proceedings, and is author of the book "Representations for Genetic and Evolutionary Algorithms" which is published in a second edition in 2006.

His main research interests are problem representations for heuristic search approaches especially evolutionary algorithms. For several years he was a visiting researcher at the Illinois Genetic Algorithm Laboratory (IlliGAL). He is a member of the Editorial Board of Evolutionary Computation Journal and Information Sciences.
He has been organizer of several workshops on representations issues, chair of EvoWorkshops in 2005 and 2006, co-organizer of the European workshop series on "Evolutionary Computation in Communications, Networks, and Connected Systems", topic chair for IEEE CEC 2005, and co-chair of the program commitee of the GA track at GECCO 2006.

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No Free Lunch:

Description of Tutorial

The "No Free Lunch" theorem is now more than 10 years old; it asserts that all possible search algorithms have the same expected behavior over all possible discrete search problems. But there is more than this to No Free Lunch (NFL). NFL also holds over finite sets of functions, some of which are compressible and some of which are not. A focused form of NFL also holds for very small finite groups of parameter optimization problems encoded using Binary and Gray Code representations. Some observations that hold when NFL is applied over all possible discrete functions are not true when NFL holds over a finite set.

Variants of local search are capable of beating random enumeration (and side stepping NFL) over large classes of problems of bounded complexity. The talk will also explore what NFL tells us about how to construct, compare and evaluate search algorithms.



Darrell Whitley

Biosketch:

Darrell Whitley is Professor and Chair of Computer Science at Colorado State University. He was the Chair of the Governing Board of the International Society for Genetic Algorithms from 1993 to 1997. He was editor-in-chief of the journal Evolutionary Computation from 1997 to 2003.


Bioinformatics:

Description of Tutorial

Bioinformatics is an interdisciplinary field of study that blends computer science and statistics with the biological sciences. Important objectives of bioinformatics include the storage, management and retrieval of high-dimensional biological data and the detection, characterization, and interpretation of patterns in such data. The goal of this tutorial is to provide an entry-level introduction to bioinformatics for those hoping to apply genetic and evolutionary computation to solving complex biological problems. Specific examples of how methods such as genetic programming have been applied in bioinformatics will be presented.



Jason H. Moore

Biosketch:

Jason H. Moore, Ph.D.: Dr. Moore received his M.S. in Statistics and his Ph.D. in Human Genetics from the University of Michigan. He then served as Assistant Professor of Molecular Physiology and Biophysics (1999-2003) and Associate Professor of Molecular Physiology and Biophysics with tenure (2003-2004) at Vanderbilt University. While at Vanderbilt, Dr. Moore held an endowed position as an Ingram Asscociate Professor of Cancer Research. He also served as Director of the Bioinformatics Core and Co-Founder and Co-Director of the Vanderbilt Advanced Computing Center for Research and Education (ACCRE).

In 2004, Dr. Moore accepted a position as the Frank Lane Research Scholar in Computational Genetics, Associate Professor of Genetics, and Adjunct Associate Professor of Community and Family Medicine, and Director of Bioinformatics at Dartmouth Medical School. He also holds adjunct positions in the Department of Biological Sciences at Dartmouth College, the Department of Computer Science at the University of New Hampshire, and the Department of Computer Science at the University of Vermont. Dr. Moore serves as Director of the Bioinformatics Shared Resource for the Norris-Cotton Cancer Center and Founder and Director of The Discovery Resource, a 300-processor parallel computer cooperatively operated for the Dartmouth community. His research has been communicated in more than 130 scientific publications.

 


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Evolutionary Multiobjective Optimization:

Description of Tutorial

Many real-world search and optimization problems are naturally posed as non-linear programming problems having multiple conflicting objectives.

Due to lack of suitable solution techniques, such problems are usually artificially converted into a single-objective problem and solved. The difficulty arises because multi-objective optimization problems give rise to a set of Pareto-optimal solutions, each corresponding to a certain trade-off among the objectives. It then becomes important to find not just one Pareto-optimal solution but as many of them as possible.

Classical methods are found to be not efficient because they require repetitive applications to find multiple Pareto-optimal solutions and in some occasions repetitive applications do not guarantee finding distinct Pareto-optimal solutions. The population approach of evolutionary algorithms (EAs) allows an efficient way to find multiple Pareto-optimal solutions simultaneously in a single simulation run.


In this tutorial, we shall contrast the differences in philosophies between classical and evolutionary multi-objective methodologies and provide adequate fundamentals needed to understand and use both methodologies in practice.

Particularly, major state-of-the-art evolutionary multi-objective optimization (EMO) methodologies will be presented and various related issues such as performance assessment and preference articulation will be discussed. Thereafter, three main application areas of EMO will be discussed with adequate case studies from practice -- (i) applications showing better decision-making abilities through EMO, (ii) applications exploiting the multitude of trade-off solutions of EMO in extracting useful information in a problem, and (iii) applications showing better problem-solving abilities in various other tasks (such as, reducing bloating, solving single-objective constraint handling, and others).

Clearly, EAs have a niche in solving multi-objective optimization problems compared to classical methods. This is why EMO methodologies are getting a growing attention in the recent past. Since this is a comparatively new field of research, in this tutorial, a number of future challenges in the research and application of multi-objective optimization will also be discussed.

This tutorial is aimed for both novices and users of EMO. Those without any knowledge in EMO will have adequate ideas of the procedures and their importance in computing and problem-solving tasks. Those who have been practicing EMO will also have enough ideas and materials for future research, know state-of-the-art results and techniques, and make a comparative evaluation of their research.



Eckart Zitzler

Biosketches:

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 three international conferences on evolutionary multi-criterion optimization (EMO 2001, EMO 2003, and EMO 2005)


Kalyanmoy Deb is currently a Professor of Mechanical Engineering at Indian Institute of Technology Kanpur, India and is the director of Kanpur Genetic Algorithms Laboratory (KanGAL). He is the recipient of the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences for the year 2005. He has also received the `Thomson Citation Laureate Award' from Thompson Scientific for having highest number of citations in Computer Science during the past ten years in India. He is a fellow of Indian National Academy of Engineering (INAE), Indian National Academy of Sciences, and International Society of Genetic and Evolutionary Computation (ISGEC). He has received Fredrick Wilhelm Bessel Research award from Alexander von Humboldt Foundation in 2003.


Kalyanmoy Deb

His main research interests are in the area of computational optimization, modeling and design, and evolutionary algorithms. He has written two text books on optimization and more than 180 international journal and conference research papers. He has pioneered and a leader in the field of evolutionary multi-objective optimization. He is associate editor of two major international journals and an editorial board members of five major journals. More information about his research can be found from http://www.iitk.ac.in/kangal/deb.htm.

Addresses:
Eckart Zitzler

Computer Engineering (TIK), ETH Zurich
Gloriastr. 35, 8092 Zurich, Switzerland
Phone:+41-1-6327066 Fax:+41-1-6321035
http://www.tik.ee.ethz.ch/~zitzler/

Kalyanmoy Deb

Professor Phone : +91 512 2597205 (O)
Department of Mechanical Engineering +91 512 2598310 (H)
Indian Institute of Technology, Kanpur Fax : +91 512 2597205, 2590007
Kanpur, Pin 208 016, INDIA
http://www.iitk.ac.in/kangal/deb.htm


Industrial Evolutionary Computation

Description of Tutorial

The tutorial gives a systematic view, based on the experience from The Dow Chemical Company, of the key issues for applying Evolutionary Computation (EC) in industrial problems. The competitive advantages and the benefits of using EC in industry are defined as well as the criteria for successful practical applications. The implementation issues of several computational intelligence techniques, such as analytic neural networks, support vector machines, Pareto-front genetic programming, and particle swarm optimizers are discussed.

An integrated methodology, which uses the discussed methods for variable selection, data compression, and robust empirical model building is proposed and illustrated with successful industrial applications in the area of process monitoring, optimization, and new product design. The open technical and non-technical issues of applied EC are addressed and selected areas of future research are recommended.


Arthur Kordon

Biosketches:

Arthur Kordon
is a Research&Development Leader in the Modeling Group in Engineering& Process Sciences Department of Core R&D, The Dow Chemical Company in Freeport, Texas, USA. He is an internationally recognized expert in applying computational intelligence technologies in industry. Dr. Kordon has successfully introduced several novel technologies for improved manufacturing and new product design, such as robust inferential sensors, automated operating discipline, accelerated fundamental model building, etc. His research interests include application issues of computational intelligence, robust empirical modeling, intelligent process monitoring and control, and data mining. He has published more than 60 papers and 7 book chapters in the area of applied computational intelligence and advanced control.

Dr. Kordon holds a Master of Science degree in Electrical Engineering from the Technical University of Varna, Bulgaria and a Ph.D. degree in Electrical Engineering from the Technical University of Sofia, Bulgaria.

Guido Smits is a Research Leader in the Engineering Sciences within the Core R&D Department in The Dow Chemical Company. His main area of expertise is in industrial applications of computational intelligence techniques like neural nets, genetic algorithms, genetic programming, support vector machines, and modeling in general. He has more than 50 technical papers and currently holds 15 patents. He has a Ph.D. in Mathematics and Physical Sciences from the University of Leiden, The Netherlands and holds degrees in Organic Chemistry (University of Antwerp, Belgium), Informatics (University of Leuven, Belgium), Polymer Physics and Chemistry (University of Eindhoven, The Netherlands), and Knowledge Technology (University of Ghent, Belgium). He lives in Wijnegem, Belgium.


Guido Smits


Constraint Handling Techniques Used with EAs:

Description of Tutorial

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 also discussed (as time allows).


Carlos Coello Coello

Biosketch:

Carlos Artemio Coello Coello received a BSc in Civil Engineering from the Universidad Autonoma de Chiapas in Mexico in 1991 (graduating Summa Cum Laude). Then, he was awarded a scholarship from the Mexican government to pursue graduate studies in Computer Science at Tulane University (in the USA). He received a MSc and a PhD in Computer Science in 1993 and 1996, respectively.
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 full professor at CINVESTAV-IPN in Mexico City, Mexico.

He has published over 170 papers in international peer-reviewed journals and conferences. He has also co-authored the book "Evolutionary Algorithms for Solving Multi-Objective Problems" (Kluwer Academic Publishers, 2002) and has co-edited the book "Applications of Multi-Objective Evolutionary Algorithms (World Scientific, 2004).
He has delivered invited talks, keynote speeches and tutorials at international conferences held in Spain, USA, Canada, Switzerland, UK, Chile, Colombia, Brazil, Argentina, Uruguay and Mexico.
Dr. Coello has served as a technical reviewer for over 40 international journals and for more than 40 international conferences and actually serves as associate editor of the journals "IEEE Transactions on Evolutionary Computation" and "Evolutionary Computation" and as a member of the editorial boards of the journals "Soft Computing", "Engineering Optimization", "Journal of Heuristics", "Computational Optimization and Applications" and the "International Journal of Computational Intelligence Research". He is member of the Mexican Academy of Science, Senior Member of the IEEE, and member of Sigma Xi, The Scientific Research Society. He is also a member of the Council of Authors of the "International Society for Genetic and Evolutionary Computation". His current research interests are: evolutionary multiobjective optimization, constraint-handling techniques for evolutionary algorithms and evolvable hardware.

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Evolutionary Practical Optimization:

Description of Tutorial

Classical optimization methods are often too restrictive to be applied to practical problem solving due to a number of limitations in their working principles: convexity requirement, continuity of search space, unimodality, single-objectiveness etc. Unfortunately, most practical optimization problems have all but the above properties. Evolutionary algorithms belong to a class of stochastic optimization algorithms which, although do not always guarantee finding the optimal solution, often are the only viable choices to solve those problems to near-optimality. In this tutorial, we shall discuss a number of properties and operators which provide EAs the necessary platform to solve such complex optimization problems. Every aspect will be contrasted with competitive classical methods and will be illustrated with interesting case studies.

Evolutionary optimization has a bright future beyond optimization in terms of their abilities in deciphering innovative principles for being optimal - a matter which will be well-illustrated with a number of interesting practical case studies.

Contents:
1. Practical optimization problems and their characteristics
1.1 Large dimension (variables and constraints)
1.2 Non-linear constraints
1.3 Discontinuities, non-differentiability, discreteness of search space
1.4 Multi-modalities (multiple optima, local and global)
1.5 Multi-objectivity (multiple conflicting objectives, trade-off solutions)
1.6 Uncertainties in variables and objectives (robust design, reliability-based design, handling noise)
1.7 Large computational time for evaluation (need for approximate eval. using RSM, Kriging, neural nets etc.)
1.8 Imprecise description of variables and objectives (need for fuzzy logic and rough set descriptions)
1.9 Dynamic optimization problems in which optimization problem changes with time (iteration counter)

2. Classical optimization principles and their shortcomings for handling practical problems

3. Efficient evolutionary algorithms for practical optimization (Modifications to a basic EA will be discussed to handle each issue of item 1. Reasons for their success will be given. Moreover, to illustrate at least one case study for each case will be discussed to drive the point home)

4. Need for hybrid EA-Classical methods

5. Beyond optimization: Unveiling innovation and innovative principles for being optimum (EAs allow finding multiple optimal solutions in one simulation. These solutions can be analyzed for commonality principles which often give rise to innovative principles about solving the problem which were not known before and not possible to achieve by other means. A number of interesting case studies will be discussed to illustrate the innovative principles which can be deciphered by this process. This task has a tremendous application in engineering design activities.)

Who should attend? All those who are interested in practical optimization and practical problem solving will be interested in this tutorial. Besides getting a feel for the differences between evolutionary optimization and their classical counterparts, participants will get a feel that evolutionary optimization provides a more (w)holistic optimization which can not only be used to just find the optimum or near-optimum solution, but to look beyond and gain a plethora of important insights about solving the problem at hand.


Kalyanmoy Deb

Biosketch:

Kalyanmoy Deb
is currently a Professor of Mechanical Engineering at Indian Institute of Technology Kanpur, India and is the director of Kanpur Genetic Algorithms Laboratory (KanGAL). He is the recipient of the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences for the year 2005. He has also received the `Thomson Citation Laureate Award' from Thompson Scientific for having highest number of citations in Computer Science during the past ten years in India. He is a fellow of Indian National Academy of Engineering (INAE), Indian National Academy of Sciences, and International Society of Genetic and Evolutionary Computation (ISGEC). He has received Fredrick Wilhelm Bessel Research award from Alexander von Humboldt Foundation in 2003.

His main research interests are in the area of computational optimization, modeling and design, and evolutionary algorithms. He has written two text books on optimization and more than 180 international journal and conference research papers. He has pioneered and a leader in the field of evolutionary multi-objective optimization. He is associate editor of two major international journals and an editorial board members of five major journals. More information about his research can be found from http://www.iitk.ac.in/kangal/deb.htm.

Kalyanmoy Deb
Professor of Mechanical Engineering
Department of Mechanical Engineering
Indian Institute of Technology Kanpur
Kanpur, PIN 208016, India
Email:
Web: http://www.iitk.ac.in/kangal/deb.htm

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Computational Complexity and Evolutionary Computation:

Description of Tutorial

Evolutionary algorithms and other nature-inspired search heuristics like ant colony optimization have been shown to be very successful when dealing with real-world applications or problems from combinatorial optimization. In recent years, analyses has shown that these general randomized search heuristics can be analyzed like "ordinary" randomized algorithms and that such analyses of the expected optimization time yield deeper insights in the functioning of evolutionary algorithms in the context of approximation and optimization. This is an important research area where a lot of interesting questions are still open.

The tutorial enables attendees to analyze the computational complexity of evolutionary algorithms and other search heuristics in a rigorous way. An overview of the tools and methods developed within the last 15 years is given and practical examples of the application of these analytical methods are presented.


Thomas Jansen

Biosketches:

Thomas Jansen,
born 1969, studied Computer Science at the University of Dortmund, Germany. He received his diploma (1996) and Ph.D. (2000) there. From September 2001 to August 2002 he stayed as a Post-Doc at Kenneth De Jong's EClab at the George Mason University in Fairfax, VA. Since September 2002 he holds a position as Juniorprofessor for Computational Intelligence at the University of Dortmund. He is an associate editor of Evolutionary Computation (MIT Press). His research is centered around the theoretical analysis of evolutionary algorithms.


Frank Neumann studied Technical Computer Science in Dortmund and Kiel, Germany. He received his diploma (2002) and Ph.D. (2006) from the Christian-Albrechts-University of Kiel. Since November 2006 he is a Post-Doc in the research group "Algorithms and Complexity" (headed by Kurt Mehlhorn) of the Max-Planck-Institute for Computer Science in Saarbrücken, Germany. A central topic in his work are theoretical aspects of randomized search heuristics in particular for problems from combinatorial optimization. He is the co-chair of a newly founded track on "Formal Theory" at GECCO 2007 which aims to be a new forum for strong theoretical work dealing with evolutionary computation methods.


Frank Neumann

 

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Complex networks and evolutionary computation

Description of Tutorial

This tutorial will try to explain the main concepts in complex network analysis. Networks (i.e. graphs) that have been created by, or constructed from, natural phenomena, present some striking characteristics and similarities that make them worthwhile researching. The initial interest on on graphs dates back to the fifties, with work carried out by Eötvös and partners, but were revived at the end of the nineties by researches such as Mark Newman, Watts and Strogatz and Barabási by describing scale-free networks and small-world networks. These concepts have been put to work in many disciplines, including evolutionary computation. The tutorial will be illustrated with examples drawn from the EC co-authorship networks, and how complex network analysis has been used in several evolutionary computation applications.



Juan Julián Merelo

Biosketches:

Juan Julián Merelo is Associate Professor at the University of Granada. His main research interests are distributed evolutionary computation and complex systems. He's been coorganizer or organizer of the last 4 PPSN conferences, and participated in program committees of all major EC conferences.

 


Carlos Cotta
is Associate Professor at the University of Málaga, He received the MSc and PhD degrees in 1994 and 1998, respectively, in Computer Science from the University of Málaga (Spain). His research interests are primarily in evolutionary algorithms, especially memetic algorihtms, and cross-paradigm hybridization. He is also interested in combinatorial optimization, and in bioinformatics.


Carlos Cotta

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Fitness Landscapes and Problem Hardness in Evolutionary Computation:

Description of Tutorial

The performance of searching agents, or metaheuristics, like evolutionary algorithms (genetics algorithms, genetic programming, etc.) or local search algorithms (simulated annealing, tabu search, etc.) depend on some properties of the search space structure. One concept that allows us to analyse the search space is the fitness landscape; in its most general definition, a fitness landscape takes into account both the optimization problem and some characteristics of the search algorithm used to solve it. This tutorial presents some definitions of fitness landscape. Subsequently, the concept of landscape geometry will be introduced and two of the most common landscape geometries (multimodal and neutral) and the dynamics of the metaheuristics on those landscapes will be discussed. After that, the binding between fitness landscapes and problem difficulty will be discussed and a set of measures that characterize the difficulty of a metaheuristic in searching solutions in a fitness landscape are analysed.
Among those measures, particular relevance will be given to Fitness Distance Correlation (FDC), Negative Slope Coefficient (NSC), a set of measures bound to the concept of Neutrality and some distance metrics and/or similarity measures that are consistent with the most commonly used genetic operators. Finally, some open questions about fitness landscapes are discussed.


Leonardo Vanneschi

Biosketches:

Leonardo Vanneschi

Born in Florence (Italy) on October 3rd, 1970.
• Laurea (maitrise) “summa cum laude” in Computer Science at the University of Pisa, achieved on February 23rd, 1996.
• PhD in Computer Science at the University of Lausanne (Switzerland), achieved on September 06th, 2004.
• Current Position: Permanent Researcher by the Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.) of the Science Faculty of the University of Milano-Bicocca (Italy).

Main scientific contributions:

Problem Hardness in Genetic Programming (GP). I have proposed some new hardness indicators for GP and other measures to characterize fitness landscapes.
Parallel and Distributed GP. I have studied how the island GP model enables to maintain a higher diversity in the whole GP system, thus producing better quality solutions for many applications.
Other Techniques to Improve GP Performance. The techniques that I have studied are: (1) reducing the number of test cases to evaluate fitness by means of statistics, (2) using populations which dynamically adapt their size according to some events happening during the evolution, (3) coevolving GP terminal symbols by means of Genetic Algorithms (GAs).
Traffic Control Applications using Machine Learning. Using image sequences taken by a fixed camera, I have realized a system to recognize, classify and track the vehicles traveling along a street. Techniques used are Neural Networks, GAs and IPAN Tracker. The system has been embedded with a television camera system built by the Comerson Srl and it is presently in use in some city crossroads and motorways.
Genetic Algorithms for the Chemical Formulation Problem. This problem is very important to build new and performing chemical compounds and drugs. I have formalized it as an optimization problem and solved it in efficient way by means of Evolutionary Algorithms.

Web page: http://personal.disco.unimib.it/Vanneschi/


Sébastien Verel


• Born in Lisieux (France) on October 29th, 1976.
• PhD in Computer Science at the University of Nice Sophia-Antipolis (France), “Study and exploitation of neutral networks in fitness landscapes for hard optimization”, achieved in December 12th, 2005.
• Current position: Permanent Researcher since 2006 by computer science departement of the Science Faculty of Nice Sophia-Antipolis (France
).



Sébastien Verel

Research Interests:
• Theory of evolutionary computation
  study and measure of hardness of fitness landscapes specialy neutral fitness landscapes.
– Proposition of fitness/fitness correlation to study dynamic and hardness,
– Analysis of complex neutral fitness landscapes in GA and GP,
– Proposition of metaheuristic for neutral fitness landscapes.

• Cellular Genetic Algorithm:
  evolutionary algorithms where the population is structured by a grid or a graph.
– Proposition of new selection selection method in Cellular GA,
– Analysis of diversity and convergence time in Cellular GA.

• Complex Systems:
  where some ”global” properties of the system comes from a large number of ”local” interactions.
– Study of the majority problem in cellular automata,
– Design of new GA for majority problem.

Web page:
http://www.i3s.unice.fr/~verel

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An Information Perspective on Evolutionary Computation

Description of Tutorial


"Information is the tennis ball of communication…" this sentence concluded Keith Devlin's talk trying to answer the question: "Does information Really Exist? ". Whether information exists or not, the discussion about information surely does.

Even though the term information has a formal definition (i.e., Kolmogorov complexity, Shannon's information theory), in the EC community (and, as it seems, in many other fields), the term information is used, more often than not, in an informal way (e.g., the problem contains no information and hence it cannot be solved efficiently).

This tutorial focuses mainly on Kolmogorov's notion of information – that is, the information content of a binary string is the length of the shortest program that can produce this string and halt – but more importantly, it concentrates on the applicability of this notion to Optimisation problems and Black-Box algorithms. For example, we will discuss how informal observations of the kind, "ONEMAX contains good information", "NIAH does not contain any" connects with the formal definition.

The tutorial covers the following major issues: decomposition of a fitness-function, the entropy of a fitness-function as a bound on the expected performance, Kolmogorov complexity (KC) and its relation to Shannon information theory, KC and problem hardness, the relation between KC and other (applicable) predictive measures to problem difficulty (e.g., auto-correlation, ruggedness) and KC vs. the no-free-lunch theorems.


Borenstein Yossi

Biosketch:

Borenstein Yossi is completing his PhD in Computer Science (Natural and Evolutionary Computation Group) at the University of Essex. He is Harold-Hyam Wingate, and AJA scholar. His research is also supported by the ORS award scheme and the University of Essex. After spending a few years working both in the industry and as a freelance lecturer he turned his effort to research. His PhD thesis, Information Landscapes explores the connection between information theory and optimisation problems in the black box scenario. During the PhD he published 11 papers in international conferences, and organized a workshop in PPSN of which extracted papers will be published in a special issue of Evolutionary Computation Journal. In addition to that he is a reviewer for IEEE TEC, ECJ, GECCO and IEEE FOCI.

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Specialized Techniques and Applications


Experimental Research in EC

Description of Tutorial

The Future of Experimental Research

We present a comprehensive, effective and very efficient methodology for the design and experimental analysis of search heuristics such as evolutionary algorithms, differential evolution, pattern search or even classical methods such as the Nelder-Mead simplex algorithm.

Our approach extends the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems.
The benefit of combining modern and classical statistical methods is demonstrated. Optimization practitioners receive valuable hints for choosing an adequate heuristic for their optimization problems -- theoreticians receive guidelines for testing results systematically on real problem instances.

We demonstrate how SPO improves the performance of many search heuristics significantly. However, this performance gain is not available for free.
Therefore, costs of this tuning process are discussed.

Several examples from theory and practice are used to illustrate typical pitfalls in experimentation. Software tools implementing procedures described in this tutorial are freely available.


Mike Preuss

Biosketches:

Mike Preuss is Research Associate at the Computer Science Department, University of Dortmund, Germany (since 2000), where he also received his diploma degree in 1998.
His current research interests focus on the field of evolutionary algorithms and include structured populations, niching, multiobjective optimization, and real-world applications. Additionally, he has published about information distribution in peer-to-peer systems, experimental methodology and parameter tuning, simple EA hybrids, EAs for classification problems, and basic models for EA behavior on multimodal test problems.



Thomas Bartz-Beielstein Dr. Bartz-Beielstein is Professor for Applied Mathematics a Cologne University of Applied Sciences. He studied Mathematics, Computer Science and Pedagogics and worked as a researcher at the collaborative research center “Computational Intelligence” at the University Dortmund where he also received his PhD. Thomas is experienced in consulting to industry and teaching university courses. His interests include optimization, modeling, simulation, statistical analysis, and experimental design of complex real-world problems.


Thomas Bartz-Beielstein

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Evolutionary Neural Networks

Description of Tutorial

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.


Risto Miikkulainen

Biosketch:

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 200 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.

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Evolvable Hardware

Description of Tutorial

In this tutorial, fundamental concepts of evolutionary circuit design and evolvable hardware will be introduced. In particular, we will deal with evolutionary algorithms and reconfigurable devices utilized for hardware evolution as well as with their interactions in typical applications of evolvable hardware. Traditionally, evolvable hardware is interpreted from the perspective of electrical engineering and computer engineering. However, existing results in the field of evolvable hardware have impacts on theoretical computer science. Hence the goal of this tutorial is also to present a non-traditional view on evolvable hardware reflecting these theoretical issues.


Lukas Sekanina

Biosketch:

Lukas Sekanina (MSc - 1999, PhD - 2002) received all his degrees from Brno University of Technology, Czech Republic. He was awarded the Fulbright scholarship and worked on the evolutionary circuit design with NASA Jet Propulsion Laboratory in Pasadena in 2004. He was a visiting lecturer with Pennsylvania State University and visiting researcher with Department of Informatics, University of Oslo in 2001. Lukas Sekanina is author of the monograph Evolvable Components (Springer Verlag, 2004). He co-authored more than 50 papers mainly on evolvable hardware. He has served as a program committee member of several conferences (including CEC, ICES, AHS, DDECS, EvoHOT, EuroGP, WEAH and GECCO) and received several awards for his work.

Research interests: theory, design and hardware implementation of bio-inspired computational systems. Currently he is an associate professor and deputy head of the Department of Computers Systems at the Faculty of Information Technology, Brno University of Technology. Member of IEEE. For more information, see http://www.fit.vutbr.cz/~sekanina

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Evolutionary Games:

Description of Tutorial

1- Introduction to Standard Non-cooperative Game Theory

• Intelligent and rational agents and elements of decision theory:
complete and
incomplete information. Bayesian decision theory.
• Decision under conflicting situations. Basic terminology of game theory.
• Extended and normal forms of a game. Equivalence. Two-person games.
Examples.
• Dominance and Nash equilibrium concepts. Pure and randomized strategies.
Examples.
• Limitations of the Nash equilibrium concept. Iterated games.

2- Introduction to evolutionary game theory

• Evolutionary processes. Evolutionary population games.
• The concept of evolutionary stable strategies. Examples.
• The replicator dynamics
• Replicator dynamics analysis of some simple two-person, symmetric games
• Evolutionary games in spatially structured populations
• Graph characterization of non-mixing populations
• Evolutionary games on graphs
• Evolutionary games on social networks: equilibrium states and stability. Examples.

3- Discussion and conclusions


Marco Tomassini

Biosketch:

Marco Tomassini is a professor of Computer Science at the Information Systems Department of the University of Lausanne, Switzerland. He graduated in physical and chemical sciences in Mendoza, Argentina, and got a Doctor's degree in theoretical chemistry from the University of Perugia, Italy, working on computer simulations of condensed matter systems. His current research interests are centered around the application of biological ideas to artificial systems. He is active in evolutionary computation, especially spatially structured systems, genetic programming, and the structure of program search spaces. He is also interested in machine learning, evolutionary games, and the dynamical properties of networked complex systems. He has been Program Chairman of several international events and has published many scientific papers and several authored and edited books in these fields.


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Evolutionary Design:

Description of Tutorial

The tutorial explores and describes the integration of evolutionary search, exploration and optimization across a wide spectrum of design activities and thus investigates the manner in which evolutionary computation can be utilized to generate design concepts and achieve meaningful designs in addition to more standard evolutionary optimization processes. The support of innovation and creativity during preliminary design and novel EC strategies and problem representations that best handle design complexity and support people-centred aspects are also presented. Although the focus is upon development and integration of appropriate evolutionary computing strategies in engineering design the transfer of the technologies into other domains such as drug design and discovery and software design will also be included. Generic design aspects across several such domains that can particularly benefit from EC application / integration and illustrate EC potential across these areas will be presented.

The tutorial will therefore include the following aspects:
• The integration of both deterministic design approaches and computational intelligence techniques with evolutionary design environments
• The extraction and processing of evolutionary design data and its visualization within appropriate designer interfaces
• Human-centred aspects and interactive evolutionary design systems
• Evolutionary search and exploration across uncertain / poorly-defined design environments
• Supporting innovative and creative design
• Development and integration of subjective fitness measures
• Qualitative and quantitative multi-objective design satisfaction
• Search and optimization within heavily constrained design domains
• Reducing computational expense during detailed design, analysis and optimisation
• Evolutionary design systems involving high-performance and distributed computing, problem-solving environments and grid-based application


Ian Parmee

Biosketch:

Formerly Director of the EPSRC Engineering Design Centre at the University of Plymouth, Ian Parmee established the Advanced Computation in Design and Decision-making Lab (www.ad-comtech.co.uk/ACDDM_Group.htm) at Bristol, UWE in 2001. The research within the Lab focuses upon the integration of people-centred, evolutionary and other computational intelligence technologies with complex design and decision-making processes. From an engineering perspective his work has extended into mechanical, aerospace, electronic, power system and marine engineering design.

From this basis his work now encompasses financial systems, software design and drug design. Enabling computational technologies such as high performance, distributed computing and Grid technology; data-mining, analysis, processing and visualisation and aspects relating to human-computer interaction also play a major role. Experience across these various domains has resulted in an understanding of generic issues, degrees of complexity and the difficulties facing both researchers and practitioners when operating within them. Ian has published extensively across engineering and evolutionary computing domains and has presented plenary talks at leading conferences in both areas. His own book ‘Evolutionary and Adaptive Computing in Engineering Design’ was published by Springer-Verlag in 2001. He has also organised and chaired the biennial international conference ‘Adaptive Computing in Design and Manufacture’ since 1994. The seventh event took place in Bristol, UK in April 2006 (www.ad-comtech.co.uk/ACDM06).
He is also the Scientific Director of ACT (www.ad-comtech.co.uk) which specialises in a range of computational intelligence strategies to provide search, optimisation and modelling capabilities across complex industrial and commercial problem domains. These areas of activity are underpinned by seventeen years experience of application of these technologies (especially evolutionary computation) in close collaboration with industry.

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Evolutionary Multiobjective Combinatorial Optimization:

Description of Tutorial

Discrete/combinatorial optimization has been an interesting and challenging area to researchers belonging to development of algorithms and OR techniques for real-world applications. Most such problems abstracted/taken from graph theory are NP-complete in single objective, and NP-hard for multiple objectives. There are many applications of such problems in communication topology and VLSI design.

For most such problems, there do not exist good heuristics/algorithms, therefore, black-box optimization techniques, e.g., EAs are usually preferred for getting effective solutions. In this tutorial, we will do a quick review of some of the standard combinatorial optimization problems, and then include the techniques and design of operators to effectively solve EMCO problems taken from real-world applications. This would be followed by some case-studies taken from standard problems. This interdisciplinary tutorial provides a practical foundation for:

• an introduction to combinatorial problems and their multiobjective versions,
• problem challenges and solution through EMO techniques,
• need of designing specialized genetic operators, representation and techniques for embedding of (limited) problem specific knowledge,
• few case studies taken form well known EMCO, and comparison of EMO results with the existing heuristics/approximation algorithms, and
• improvement of the EMO solutions through hybridization using local search and others.

Key design considerations to designing operators/representations so as to effectively explore the search space will be looked into. In general, the tutorial is aimed at quickly laying a foundation that can be used as the basis for further study, research and development in this emerging area.


Rajeev Kumar

Biosketch:

Rajeev Kumar is an Associate Professor of Computer Science & Engineering (CSE) at Indian Institute of Technology (IIT), Kharagpur. He received his Ph.D. from University of Sheffield, and M.Tech. from University of Roorkee (now, IIT - Roorkee) both in Computer Science & Engineering. He had been a faculty of CSE at IIT Kanpur and BITS Pilani. He had worked for National Semiconductors, Germany and DRDO, India. His main research interests include evolutionary algorithms & multiobjective combinatorial optimization, embedded & multimedia systems, and programming language & software engineering. He has published over 100 research articles in lead journals and conferences. He is a member of ACM, senior member of IEEE and a fellow of IETE. More info about him can be found at http://www.facweb.iitkgp.ernet.in/~rkumar/.

Email:

Further Information: http://www.facweb.iitkgp.ernet.in/~rkumar/emco.html


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Evolutionary Computer Vision:

Description of Tutorial

The term Evolutionary Computer Vision is a recent research subject in genetic and evolutionary algorithms. This tutorial aims to provide a survey about the main topics that have been developed in the literature. The goals are to introduce the genetic and evolutionary community to the research activities of endowing a machine with visual abilities, applying the paradigm of evolutionary algorithms. The tutorial will be focused in giving examples of real working systems and how the paradigm could be implemented to solve problems such as: recognition, feature extraction, 3D measurement, and segmentation.

The tutorial addresses all the above mentioned issues and is articulated in three parts. The introduction summarizes the new idea of evolving structures that solves computer vision problems in terms of optimization using the framework of evolutionary computation. The second part is focused in details to successfully implement an Evolutionary Algorithm to solve computer vision problems. In particular, it illustrates the crucial role of encoding the solution, the definition of the fitness function, and how the structures are evolved by the system. Examples are provided to illustrate aspects related to multimodal fitness functions, synthesis of feature detectors, and problem decomposition. Finally, a discussion about the methodological issues addressed in evolutionary computer vision should give the participants a clear picture that a successful application requires a balance between computer vision and evolutionary algorithms knowledge.

Scientists, Engineers, Mathematicians, Programmers, and Technical Managers will all benefit by learning about how to evaluate this new computational research field.


Gustavo Olague

Biosketch:

Gustavo Olague is a research scientist at the CICESE Research Center working within the Computer Science Department of the Applied Physics Division. He hold a Bachelor's degree (Honors) in Electronics Engineering and a Master's degree in Computer Science from the Instituto Tecnológico de Chihuahua, Mexico. He received the Ph.D. (Diplôme de Doctorat en Imagerie, Vision et Robotique) in Computer Graphics, Vision and Robotics from Institut National Polytechnique de Grenoble, France, working at the INRIA Rhône Alpes within the MOVI Research team.   He is presently a Professor of Computer Science at Centro de Investigación Científica y de Educación Superior de Ensenada, B.C.

Olague's research focuses on the principles of computational intelligence applied to close-range photogrammetry and computer vision. He is a member of the EvoNET, RSPSoc, ASPRS, SIGEVO, IEEE, IEEE Computer Society, and is listed in Who's Who in Science and Engineering. Dr. Olague is recipient of the "2003 First Honorable Mention for the Talbert Abrams Award", offered by the American Society for Photogrammetry and Remote Sensing. He has been awarded the Bronze Medal at the Human Competitive Awards for his work on synthesis of interest points in 2006.

email:

Further information: http://cienciascomp.cicese.mx/evovision/

Expected enrollment: 30 people

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Bio-inspired Telecommunications:

Description of Tutorial

The rapid advances in computing and transmission technologies are giving im petusto the large-scale deployment of interconnected systems for communication and transport of data, voice, video and resources. The global Internet, and cellular, satellite, and Wi-Fi networks, as well as power and logistic networks, just to mention a few remarkable examples, are at the same time ubiquitous and at the very heart of the functioning and success of modern societies. On theother hand, all these networks are increasingly heterogeneous, complex, and dynamic, such as they present a number of challenging issues concerning their analysis and design, management and control, robustness and security. Biological systems show a number of properties, such as self-organization, adaptivity, scalability, robustness, autonomy, locality of interactions, distribution, which are highly desirable to deal with the growing complexity of current and future networks.

Therefore, in recent years a growing number of effective solutions for problems related to networked systems have been proposed by taking inspiration from the observation of natural systems and processes such as natural selection, insect societies, immune systems, cultural systems, collective behaviors of groups of animals/cells, etc.

The aim of the tutorial is to provide the cutting edge research on Bio/Nature inspired approaches to network-related problems. The tutorial will also focus on protocol engineering in order to introduce different frameworks that have been developed to realize such Bio/Nature inspired protocols inside the network stack of the Linux kernel. The presentation will also introduce acomprehensive performance evaluation framework that is crucial for getting an insight into the behavior of a Bio/Nature inspired routing algorithm over wide operational landscape of a real network. The designers of the routing protocols can use it to verify their Linux model by comparing important performance values obtained from Linux model with the simulation model. The presentation will also introduce a novel testing framework for MANETs to compare and verify the results of MANET routing protocol in real world MANETs. Last but not least tutorial will also introduce security threats that a designer has to be aware of while deploying such protocols in real world fixed and MANETS. We believe that the tutorial will be instrumental in highlighting the potential of Bio/Nature inspired protocolsin real world networks.

The tutorial is intended for telecommunication managers, protocol developers, network engineers, network software developers and optimization researchers who want to work in non-linear real time dynamic problems.


Muddassar Farooq

Biosketch:

Muddassar Farooq is currently working as an Assistant Professor at National University of Sciences and Technology (NUST), Pakistan. He has worked as a research associate at The University of Dortmund (Germany) from Oct 2001 to March 2006. His research interests include application of Bio/Nature inspired principles to telecommunication networks and developing Nature inspired engineering solutions to real world problems without the need of additional hardware or software resources. He was the project Manager for BeeAdHoc, which deals with designing a secure, scalable and energy efficient routing protocol for MANETs (Mobile Ad-Hoc Networks).

He recently completed the BeeHive project that dealt with designing a robust, fault-tolerant and efficient protocol for fixed telecommunication networks. His paper on BeeHive won the best paper award at ANTS 2004 conference. He is a member of ACM and IEEE. He is the workshop chair for EvoCoMNet 2007 to be held in Valencia Spain. He also acted as the guest editor for a for a special issue on Nature Inspired Applied Systems (NIAS) of Elsevier Journal of System Architecture (Volume 52, Numbers 8-9) August/Sep tember 2006.

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Distributed Evolutionary Computation for Fun and Profit

Description of Tutorial

Distributed evolutionary computation is almost a given nowadays. Computer networks are composed of heterogeneous nodes linked by different bandwidth and latencies. Even off-the-shelf laptops include dual-core microprocessors, which, to be taken full advantage of, must be programmed using distributed computing techniques. Moreover, current processors such as the latest by Intel and AMD include hyperthreading architectures that allow several threads to run concurrently. All these things have to be taken into account to fully leverage the current computational environment.

In general, programming in this environment presents several challenges:

• Achieve as many participants as possible in a distributed computing experiment.
• Distribute the experiment in the best possible way.
• Scale with the addition of new processing elements.
• Take advantage of all participants in the experiment despite their capability and connection quality

In general, that means that the usual, plain-vanilla generational-cum-island/farming model is not adequate for this kind of environment. Even if some improvement can be obtained for several processors, even in a single processor better performance can be achieved through an adaptation of the algorithm to the architecture.

In this tutorial, we will first present the state of the art in distributed evolutionary computation, presenting what are the issues in _normal_ parallel/distributed EC, and how they have been classically solved. Then we will present an overview of _modern_ distributed computing, including P2P computing and more esoteric topics such as sneaky/parasitic/volunteer computation.

We will present and, if possible, let the audience run examples using systems such as DREAM, a P2P system designed with evolutionary computation (but not only) in mind, and DCOR (Distributed Computation on Rails, available from http://rubyforge.org/projects/dconrails/, a sneaky distributed evolutionary computation system based on Ruby on Rails). Then, we will try to address the challenges of modern evolutionary computation as written above, and how different authors have approached them.

Finally, we will present open issues in distributed evolutionary computation.


Juan Julián Merelo

Biosketches:

Juan Julián Merelo is Associate Professor at the University of Granada. His main research interests are distributed evolutionary computation and complex systems. He's been coorganizer or organizer of the last 4 PPSN conferences, and participated in program committees of all major EC conferences.

 

Juan Luis Jiménez Laredo is a researcher and PhD student at the University of Granada. He's currently working on adaptive distributed evolutionary computation system, and has also participated in the HPC program and as an Erasmus student at the University of Ulm, in Germany.


Juan Luis Jiménez

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