• noise-free functions and
• noisy functions

The participants can freely choose (between) both of them.

During the workshop the overall procedure will be critically reviewed, the algorithms will be presented by the participants, quantitative performance measurments of all submitted algorithms will be presented, categorized by early versus late performance and function properties like multi-modality, ill-conditioning, symmetry, ridge-solving, coarse- and fine-grain ruggedness, weak global structure, outlier noise…

Code of the benchmark functions and for the post-processing will be provided in early 2009.

Important Dates

• 03/23/2009 paper and data submission deadline
• 04/03/2009 decision notification
• 06/08/2009 workshop
• end of 2009 extended paper submission tentative deadline for an anticipated special issue of Evolutionary Computation Journal

Please visit the workshop web site at:
http://coco.gforge.inria.fr/doku.php?id=bbob-2009

*Organizers*

Anne Auger, INRIA,  -  

In 1997 he became a DFG Heisenberg Fellow and finally professor of CS in 2003. Since 2004 he is professor at the Vorarlberg University of Applied Sciences, Austria. He is author of the book "The Theory of Evolution Strategies" (Springer-Verlag, 2001) and author/coauthor of more than 100 papers. Dr. Beyer serves as an Associate Editor for IEEE Transactions on Evolutionary Computation and for Journal of Evolutionary Computation (MIT-Press). He served as guest editor for the Journals "Natural Computing" and "Genetic Programming and Evolvable Machines." He was initiator and co-organizer of the Dagstuhl-Seminar series on "Theory of Evolutionary Algorithms" (in 2000, 2002, and 2004). He served as Editor-in-Chief of GECCO-2005 and was/is program co-chair of GECCO-2000, -2003, -2004, -2009, and PPSN-2002 and -2006.

Hans-Georg Beyer, FH Vorarlberg GmbH,   -  

Nikolaus Hansen

Nikolaus Hansen is a research scientist at the Saclay MicrosoftResearch--INRIA joint center in France. Educated in medicine and mathematics, he received his Ph.D. in civil engineering in 1998 from the Technical University Berlin, working under Ingo Rechenberg. Since then he has been working in evolutionary computation and computational science at the Technical University Berlin and at ETH Zurich. His main research interests are learning and adaptation in evolutionary computation and the development of algorithms applicable in practice. His most well-known contribution to the field of evolutionary computation is the so-called Covariance Matrix Adaptation (CMA).

Nikolaus Hansen, INRIA,  -  

Steffen Finck obtained a MSc in Mechanical Engineering at the Rose-Hulman Institute of Technology in Terre Haute, USA (2004) and a Diploma in Aeronautical Engineering from the University of Stuttgart, Germany (2006). Since end of 2006 he is a PhD-student at the University of Applied Sciences Vorarlberg in Dornbirn, Austria. His PhD is concerned with direct search methods under the influence of noise.

Steffen Finck

Steffen Finck, University of Applied Sciences Vorarlberg,   -  

Raymond Ros

Raymond Ros graduated from the Ecole Superieure de Physique et de Chimie Industrielles (Paris, France) after having obtained a MSc in Computer Science from the Universite Paris-Sud in 2005. Since then, he has been preparing a PhD on the average-case analysis of machine learning and optimization algorithms under the supervision of Michele Sebag and Antoine Cornuejols.

Raymond Ros, Université Paris Sud,   -  

Marc Schoenauer is member of the Executive of SIGEVO, the ACM Special Interest Group for Evolutionary Computation. He has served in the IEEE Technical Committee on Evolutionary Computation from 1995 to 1999, and is member of the PPSN Steering Committee. He was the founding president (1995-2002) of Evolution Artificielle, the French Society for Evolutionary Computation. Marc Schoenauer is Editor in Chief of Evolutionary Computation Journal since 2002, is or has been Associate Editor of IEEE Transactions on Evolutionary Computation (1996-2004), of TCS-C (Theory of Natural Computing) (2001-2006), of Genetic Programming and Evolvable Machines (1999-), and of the Journal of Applied Soft Computing (2000-). He serves on the Program Committees of all major conferences in the field of Evolutionary Computation.

Marc Schoenauer, INRIA,   -  

---

Darrell Whitley

Darrell Whitley currently serves as Chair of the Executive Committee of the ACM Sig on Genetic and Evolutionary Computation (SIGEVO). He has previously served as Chair of the Governing Board of the International Society for Genetic Algorithms (1993-1997), and as Editor-in-Chief of Evolutionary Computation (1997-2002). Darrell Whitley, Colorado State University,   -

Everyone who has implemented an evolutionary algorithm or other computational intelligence approach using graphics processing units (GPUs), video game systems, mobile devices, cellular phones, etc. will want to submit to this workshop. Due to its speed, price, and availability, there is increasing interest in using mass consumer market commodity hardware for engineering and scientific applications. Mostly this has concentrated upon graphics hardware, particularly GPUs, due to their ability to offer teraflop performance on a desktop using a restricted form of parallel computing (known as “General Purpose computing on Graphics Processing Units”, or “GPGPU”). There is also increasing interest in using the computing power of game consoles such as Microsoft® Xbox, Sony® Playstation and the Cell processor, and portable entertainment and/or cellular phone mobile devices for research and applications.

Submissions are invited in (but not limited to) the following areas:

• Parallel genetic programming (GP) on GPU
• Parallel genetic algorithms (GA) on GPU
• Parallel evolutionary programming (EP) on GPU
• Associated or hybrid computational intelligence techniques on GPU

· Support Vector Machines
· Bayesian Networks
· Parallel search algorithms
· Data mining

• Differential Evolution on GPU
• Computational Biology or Bioinformatics on GPU
• Evolutionary computation on video game platforms
• Evolutionary computation on mobile devices

The workshop will be held in conjunction with the tutorial “Accelerating Evolutionary Computation with Graphics Processing Units,” and a GECCO 2009 competition on GPUs for Genetic and Evolutionary Computation has been organized with the prize of a state-of-the-art nVidia® graphics card.

Workshop website: http://www.cs.ucl.ac.uk/external/W.Langdon/cigpu/

*Organizers*

Garnett Wilson

Dr. Garnett Wilson was awarded his PhD in Computer Science from Dalhousie University, Canada in 2007. He has published in the areas of linear genetic programming, co-evolutionary algorithms, artificial developmental systems, and GPU programming for evolutionary computation. Dr. Wilson developed the first genetic programming, and general-purpose computing on graphics processing units (GPGPU), implementation on a commercial video game system (using the XBox 360). Industrial postdoctoral research involved production of proprietary machine learning algorithms for detection of financial fraud and anti-money laundering. He is currently a postdoctoral fellow with Memorial University of Newfoundland, where his research focuses on evolutionary computation applied to financial modelling and social network analysis.

Garnett Wilson,    -   

Simon Harding,    -   

W. B. Langdon

W. B. Langdon’s PhD ("Genetic Programming and Data Structures") was published in 1998 by Kluwer as the first volume in its GP series. He has worked in the electricity supply industry, for Logica and at University College London, The University of Birmingham, The CWI, Essex University and now King's College back in London. In recent years he has demonstrated research in GPGPU on nVidia cards as well as initiating CIGPU-2008.

W. B. Langdon,   -   

He has authored and co-authored over 80 papers and 1 book. His articles on these topics have been published in Management Science, Decision Support Systems, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Systems, Man, and Cybernetic, IEEE Intelligent Systems, IEEE Engineering in Medicine and Biology, Expert Systems with Applications, Journal of the American Society for Information Science and Technology, Fuzzy Sets and Systems, International Journal of Approximate Reasoning, etc. He received Lingnan University Research Excellence Award.
He has served in the organising and program committees of a number of international conferences including the Sixth IEEE International Conference Conference on Data Mining which was held in Hong Kong.
Dr. Wong and his colleagues started their work in CIGPU in 2003. The research has been recognised by the community of evolutionary computation and the results have been published in the journal IEEE Intelligent Systems, IEEE CEC 2005, and IEEE CEC 2006.

Man Leung Wong,    -   

Within the last decade, the use of computational intelligence techniques for solving challenging defense related problems has achieved widespread acceptance. The genesis of this interest lies in the fact that repeated attempts of using more traditional techniques have left many important problems unsolved, and in some cases, not addressed. Additionally, new problems have emerged that are difficult to tackle with conventional methods, since social, cultural and human behavioral factors tend to be at the heart of these new types of problems (e.g. within the broad areas of the global war on terrorism, homeland security, and force protection).
The purpose of the workshop is to introduce and discuss current and ongoing efforts in using computational intelligence techniques in attacking and solving defense-related problems, with a focus on genetic and evolutionary computation techniques. These include, but are not limited to the following:

• Genetic and evolutionary techniques in the design of military systems and sub-systems.
• Genetic and evolutionary techniques for logistics and scheduling of military operations.
• Genetic and evolutionary algorithms (GEAs) in strategic planning and tactical decision making.
• Multiobjective GEAs for examining tradeoffs in military, security, and counter-terrorism procedures.
• Automated discovery of tactics and procedures for site security, force protection, and consequence management.
• Genetics-based knowledge discovery and data mining of large databases used to recognize patterns of individual behavior.
• Co-evolutionary for simultaneous red-blue team strategic-tactical simulation and gaming.

*Organizers*

Laurence D. Merkle,    -   

Frank W. Moore, University of Alaska Anchorage,    -   

Genetic and evolutionary computation (EC) and multi-agent systems and simulation (MASS) both involve populations of agents. EC is a learning technique by which a population of individual agents adapt according to the selection pressures exerted by an environment; MASS seeks to understand how to coordinate the actions of a population of (possibly selfish) autonomous agents that share an environment so that some outcome is achieved. Both EC and MASS have top-down and bottom-up features: For example, some aspects of Multi-agent system engineering (e.g., mechanism design) are concerned with how top-down structure can constrain or influence individual decisions. Similarly, most work in EC is concerned with how to engineer selective pressures to drive the evolution of individual behavior towards some desired goal. Multi-agent simulation (sometimes called agent-based modeling, ABM) addresses the bottom-up issue of how collective behavior emerges from individual action. Likewise, the study of evolutionary dynamics (particularly in coevolution) within EC often considers how population-level phenomena emerge from individual-level interactions. Thus, at a high level, we may view EC and MASS as examining and utilizing analogous processes. It is therefore natural to consider how knowledge gained within EC may be relevant to MASS, and vice versa; indeed, applications and techniques from one field have often made use of technologies and algorithms from the other field. Studying EC and MASS in combination is warranted and has the potential to contribute to both fields.

Topics relevant to the proposed workshop include, but are not limited to:

• Multi-agent systems and agent-based models utilizing evolutionary computation
• Optimization of multi-agent systems and agent-based models using evolutionary computation
• Evolutionary computation models which rely not on explicit fitness functions but rather implicit fitness functions defined by the relationship to other individuals / agents
• Applications utilizing MASS and EC in combination
• Biological agent-based models (usually called individual-based models) involving evolution
• Evolution of cooperation and altruism
• Genotypic representation of complex phenotypic strategies of MASS
• Evolutionary learning within MASS (including Baldwinian learning and phenotypic plasticity)
• Emergence and feedbacks
• Open-ended strategy spaces and evolution
• Adaptive individuals within evolving Populations

*Organizers*

Sevan G. Ficici

Sevan G. Ficici is currently a Post-Doctoral Fellow in computer science at Harvard University; he obtained his Ph.D. from Brandeis University working under Jordan Pollack. Sevan has worked broadly in the field of multi-agent systems and learning for over a decade. His Ph.D. work focused on coevolutionary learning in multi-agent systems. At Harvard, Sevan is working with Avi Pfeffer to develop computational models of human behavior in multi-agent domains and construct computer agents that utilize these models to interact successfully with human participants. Sevan was chair of the coevolution track at GECCO 2006.

Sevan G. Ficici, AI Research Group, Harvard University,    -   

William Rand, Center for Complexity in Business, University of Maryland,    -   

Rick Riolo

Rick Riolo is an Associate Research Scientist and Director of the Computer Lab in the Center for the Study of Complex Systems at the University of Michigan. His research interests include evolutionary algorithms in theory and in models of complex adaptive systems, and agent-based modeling approaches to studying problems across a wide variety of complex systems, e.g., the spread of antibiotic resistance, the effects of phenotypic plasticity on ecological community dynamics, urban sprawl, and the effects of formal and informal institutions on the sustainability of common resource pools.

Rick Riolo, Center for the Study of Complex Systems, University of Michigan,    -   

Generative and Developmental Systems (GDS) is the study of artificial systems inspired by the process of development in nature, which is the central theme of this workshop. Its aim is to provide deep insights into the nature of biological and artificial development in order to encourage, enrich, and inspire thought and collaboration leading to the realization of complex developmental systems. The central theme includes (but is not limited to) the following issues (expressed as questions):

• What is development from both orthodox and dissenting perspectives?
• Why do we need computational development, in computing and engineering?
• How has/can development been/be concretely realized and fruitfully applied?
• For which kinds of applied problems is a developmental approach most useful?
• What abstract or bio-inspired mechanisms are necessary for an effective developmental approach?

The workshop encompasses two sessions, each covering an equal number of issues to be discussed and debated. Each session has three parts: (a) a brief presentation by the moderator and 2-3 invited debaters, followed by
(b) the discussion/debate itself, then (c) an open session, wherein all attendees are free to present questions and comments to the speakers. The whole workshop will be video-taped for future reference by anyone on the internet.

*Organizers*

Nawwaf Kharma

Nawwaf Kharma got his B.Eng. degree in Computer Engineering from The City University, London, receiving the Jarogate Prize for best overall academic performance. He got his PhD in Machine Learning form Imperial College, London. Since joining Concordia University in 2000, he has co-authored and authored a textbook on Character Recognition and a chapter on Evolvable Developmental Systems. He has published more than 25 peer-reviewed articles. He has two Canadian software copyrights. Kharma has received a Human-Competitive award from the Genetic and Evolutionary Computation Conference (GECCO) 2006 for his work on multiple ellipse detection (in microscopic images) using multi-population genetic algorithms. He is now leading a small team developing that work into a software prototype, in a 1-year project funded by NSERC’s Idea to Innovation (I2I) programme. He heads the Computational Intelligence Lab at Concordia University.

Nawwaf Kharma,   -   

William R. Buckley,    -   

Julian Miller

Biosketches: Julian Miller is a lecturer in the Department of Electronics at the University of York. His main research interests are genetic programming (GP), and computational development. He has published over 130 refereed papers on evolutionary computation, genetic programming, evolvable hardware, and computational development. He has been chair or co-chair of twelve conferences or workshops in genetic programming, computational development, evolvable hardware and evolutionary techniques.

Julian Miller,    -   

Kenneth Stanley,    -   

Garnett Wilson

Dr. Garnett Wilson was awarded his PhD in Computer Science from Dalhousie University, Canada in 2007. He has published in the areas of linear genetic programming, co-evolutionary algorithms, artificial developmental systems, and GPU programming for evolutionary computation. Dr. Wilson’s doctoral research involved the creation of a developmental genetic programming system called Probabilistic Adaptive Mapping Developmental Genetic Programming (PAM DGP). PAM DGP used co-evolution of genotype and genotype-phenotype mapping populations under statistical control to provide solutions to difficult problems using tailored function sets. Industrial postdoctoral research involved production of proprietary algorithms for detection of financial fraud and anti-money laundering.

Garnett Wilson,    -   

Since Learning Classifier Systems (LCSs) were introduced by Holland [1] as a way of applying evolutionary computation to machine learning problems, 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 a very active area of research, with newer approaches, in particular Wilson's accuracy-based XCS [2], receiving a great deal of attention. LCS are also benefiting from advances in reinforcement learning and other machine learning techniques.

This will be the twelfth edition of the workshop, which was initiated in 1992, held at the NASA Johnson Space Center in Houston, Texas. Since 1999 the workshop has been held yearly in conjunction with PPSN in 2000 and 2002 and with GECCO in 1999, 2001 and from 2003 to 2008.

Topics of interests include but are not limited to:

• Paradigms of LCS (Michigan, Pittsburgh, ...)
• Theoretical developments (behavior, scalability and learning bounds, ...)
• Representations (binary, real-valued, oblique, non-linear, fuzzy, ...)
• Types of target problems (single-step, multiple-step, regression/function approximation,...)
• System enhancements (competent operators, problem structure identification and linkage learning, ...)
• LCS for Cognitive Control (architectures, emergent behaviours, ...)
• Applications (data mining, medical domains, bioinformatics, ...)

---------------------------------------
[1] J. H. Holland and J. S. Reitman.
Cognitive systems based on adaptive algorithms.
In D. Hayes-Roth and F. Waterman, editors, Pattern-directed Inference
Systems, pages 313-329. Academic Press, New York, 1978.

[2] Steward W. Wilson.
Classifier fitness based on accuracy.
Evolutionary Computation, 3(2):149-175, 1995.
----------------------------------------

*Organizers*

Jaume Bacardit

Jaume Bacardit received his Ph.D. in 2004 from the Ramon Llull University in Barcelona, Spain. His thesis was focused on a class of machine learning techniques called Learning Classifier Systems, specially using the Pittsburgh approach of LCS. After graduating, he moved to the University of Nottingham, UK. First as a Postdoc, applying LCS to Bioinformatics domains, and currently as Lecturer in Bioinformatics, in a position jointly appointed between the Schools of Computer Science and Biosciences of the University of Nottingham, with the aim of increasing the collaborative research at the interface of both schools.

He has been in the program committee, among other conferences and workshops, of the Genetic and Evolutionary Computation Conference (GECCO) since 2005, the International Workshop on Learning Classifier Systems (IWLCS) since 2005, the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) since 2006 and the IEEE International Joint Conference on Neural Networks (IJCNN) since 2006, and has reviewed articles for the IEEE Transactions on System Man and Cybernetics Part B, IEEE Transactions on Evolutionary Computation, Evolutionary Computation Journal, Soft Computing, Expert Systems and the International Journal of AI Tools. Since 2002 he regularly has his contributions presented at GECCO and IWLCS. In 2001 he was in the organizing commitee of the 4th Catalan Conference on Artificial Intelligence (CCIA2001). He is co-editing a book in collaboration with Dr. Martin Butz and Dr. Ester Bernad-Mansilla containing extended versions of the papers presented at the 2006 and 2007 editions of the International Workshop on Learning Classifier Systems.

Jaume Bacardit, University of Nottingham,    -   

He has been involved with a wide range of EU projects. Currently, active in Fidis, Future of IDentity in the Information Society, FP6 NoE, a member of euCognition and EURON, EUropean RObotics research Network. His common research theme is developing systems capable of exploiting environmental feedback, which is core to both Cybernetics and Cognitive Robotics. He was an invited speaker at the Nokia Machine Consciousness workshop 2008 on artificial emotions for cognitive control of robotics. He presented an invited tutorial on Cognitive Robotics with Prof Kawamura (Vanderbilt University, USA) at Ro-man 2006. Conferences/Workshops Organisation has included PI of COGRIC: Cognitive Robotics and Control, EPSRC/NSF sponsored workshop 2006 that brought together internationally leading figures in order to discuss latest advancements and direct future research. Dr Browne also organised the 'Future Directions in Learning Classifier Systems' Workshop as part of PPSN 2004. He has been elected to serve on organising committee of International Workshop on Learning Classifier Systems for 2009 and 2010. Program Committees memberships /chairs include LCS and other GBML track at GECCO; the International Workshop on Learning Classifier Systems (IWLCS), Congress on Evolutionary Computation (CEC); Hybrid Intelligent Systems (HIS); Parallel Problem-Solving from Nature (PPSN) and Roman & Human Interactive Communication (RO-MAN). Journal reviewing has included IEEE Transactions on Evolutionary Computation, Journal of Soft Computing, Springer-Verlag, Journal of Engineering Manufacture, Journal of Pattern Analysis and Applications and IEEE Trans. Systems Man and Cybernetics. He has 25 refereed international publications between journal papers, conference papers and book chapters.

Will Browne, University of Reading,    -   

Jan Drugowitsch

Jan Drugowitsch received his PhD in computer science at the University of Bath, UK, in October 2007 under the supervision of Alwyn Barry. His thesis "Learning Classifier Systems from First Principles: A Probabilistic Reformulation of Learning Classifier Systems from the Perspective of Machine Learning" reformulates Learning Classifier Systems (LCSs) from the point-of-view of statistical machine learning and provides an approach to tackle their design an analysis from the first principles of a statistical description of their aim. He is now working with Alexandre Pouget at the Department of Brain & Cognitive Sciences at the University of Rochester, UK, on the neural basis of decision making, with particular emphasis on multimodal integration and reaction time.

He has been on the program committee for the Genetic and Evolutionary Computation Conference (GECCO) since 2005, the International Workshop on Learning Classifier Systems (IWLCS) since 2005, and has reviewed articles for the IEEE Transactions on Evolutionary Computation. Drugowitsch has written the book "Design and Analysis of Learning Classifier Systems: A Probabilistic Approach", where he outlines the approach developed in his thesis and provides extensions to it.


Jan Drugowitsch, University of Rochester,    -   

"No experiment is ever a complete failure; it can always serve as a bad example". From a failed study we cannot exactly learn what we intended to but we can always learn *something*. This shall be the mission statement of this workshop.

Failures frequently happen in experimental research as well as in theory. The question is what we can learn from a specific failure, and on a higher level, how to deal with these failures. The main emphasis is thus twofold: To inform about concrete failed approaches, and to review and develop methods of attaining progress beyond the failures.

Participants will present seemingly clever ideas and concepts that somehow did not work. These can e.g. be the beginning of the train of thoughts which already led to another successful approach, or it may be the presentation of a problem which is still unsolved.

It is intended that differences and similarities of the current scientific approaches of several researchers are discussed, as well as the possibilities of 'negative papers'. This relates to many issues in the philosophy of science and may contribute to a further development of experimental and theoretical methodologies in evolutionary computation (EC).

*Organizers*

Nicola Beume, Technische Universität Dortmund,    -   

Mike Preuss, Technische Universität Dortmund,    -   

MedGEC 2009 is the fifth GECCO Workshop on the application of genetic and evolutionary computation (GEC) to problems in medicine and healthcare.

A dedicated workshop at GECCO provides a much needed focus for medical related applications of EC, not only providing a clear definition of the state of the art, but also support to practitioners for whom GEC might not be their main area of expertise or experience.

The Workshop has two main aims:

(i) to provide delegates with examples of the current state of the art of applications of GEC to medicine.
(ii) to provide a forum in which researchers can discuss and exchange ideas, support and advise each other in theory and practice.

GECCO is widely regarded to be the most authoritative conference in GEC and, as such, offers the ideal venue for this important and growing community.

Subjects will include (but are not limited to) applications of GEC to:

• Medical imaging
• Medical signal processing
• Clinical diagnosis and therapy
• Data mining medical data and records
• Clinical expert systems
• Modeling and simulation of medical processes

*Organizers*

Steve and Stefano Cagnoni are co-founders and organizers of the MedGEC Workshop, which is now in its fifth year. They are also guest editors for a special issue of Genetic Programming and Evolvable Machines (Springer) on medical applications. Steve has some 75 refereed publications, is a Chartered Engineer and a member of the British Computer Society.

Stephen L. Smith, The University of York,    -   

Stefano Cagnoni, Universita' degli Studi di Parma,    -   

The purpose of this half day workshop is threefold: (1) to provide an opportunity for biomedical and computer scientists to present and discuss accomplishments as well as speculative futuristic ideas related to the application of evolutionary computation in support of patient care, (2) to organize an international community interested in on-going exploration of appropriate applications of computational intelligence in clinical medicine, and (3) to develop an internet-based community of practice dedicated to developing and sharing data, computer programs, knowledge and other resources related to computing in support of patient care.

In the workshop, six selected brief (10-15 minutes) informal presentations on real or imagined applications of evolutionary computation in support of patient care will be presented. Each presentation will be followed by a facilitated group dialogue focusing on the content of the presentation. A more general open dialogue will fill out the remaining time if there is any.

Subjects include (but are not limited to) applications of Genetic and Evolutionary Computation to:

• disease prevention, early detection, diagnoses and prognosis
• lifetime treatment planning and follow-up
• biomedical numeric, categorical, text, image & signal data mining
• knowledge extraction from electronic patient records
• low-cost screening devices
• cost reduction in any aspect of medicine
• continuous patient monitoring and alarm systems
• detecting untoward effects such as adverse drug reactions, drug-drug interactions, etc
• patient-management workflow optimization
• disease modeling & treatment selection
• survival prediction & other time-to-event modeling
• medical biometric technology & personal multimedia data processing
• building and using biomedical ontologies
• designing clinical research trials
• drug dose targeting & drug evaluation
• medical devices, patient monitoring & preventive treatment strategies
• subject recruitment for clinical research protocols
• translational research & comparative effectiveness
• genomics, proteomics, transcriptomics, metabolomics in relation to clinical practice
• pharmacology, pharmacokinetics, pharmacodynamics & pharmacogenomic

*Organizers*

Jim DeLeo

Jim DeLeo is a computer scientist at the National Institutes of Health Clinical Center in Bethesda Maryland, USA. He is chief of the NIH Clinical Center Scientific Computing Section and founder and chairman of the NIH Biomedical Computing Interest Group. He is dedicated to promoting the effective and practical use of modern intelligent computing methodology. Jim DeLeo,  National Institutes of Health -

Alexandru Floares is a neurologist and a computer scientist. He is the head and founder of the Artificial Intelligence Department of the Oncological Institute Cluj-Napoca, Transilvania, Romania, and the president and founder of SAIA - Solutions of Artificial Intelligence Applications - organization, Cluj-Napoca, Transilvania. His present research is more problem-oriented rather than methods-oriented, trying to identify important biomedical problems and to solve them with appropriate computational intelligence tools.

Alexandru Floares

Alexandru Floares,   Oncological Institute Cluj-Napoca -

Aaron Baughman, IBM Global Services   -    

Symbolic Modeling is used to designate the search for symbolic descriptions, usually in the language of mathematics, to describe and predict numerical data in diverse fields such as industry, economics, finance and science.

Symbolic modeling captures the field of symbolic regression: a genetic programming based search technique for finding symbolic formulae on numerical data in order to obtain an accurate and concise description of that data in symbolic, mathematical form. In the evolutionary computation field it also captures learning classifier systems, if and when they are applied to obtain specific interpretable results in the field of interest.

Symbolic modeling can be defined as a set of techniques (including, but not limited to symbolic regression and learning classifier systems) and representations that try to find a mathematical description and prediction in some numerical space. This can be contrasted with numerical modeling such as (generalized) linear regression, neural networks, kernel regression and support vector machines.

The key discriminator of producing symbolic results over numerical results is the ability to interpret and analyze the results, leading either to acceptance by field experts, or to heightened understanding of the theory in the field of application. Interpretation is key, and the workshop will focus heavily on this.

The workshop will focus on advances in using symbolic modeling for real world problems in industry, economics, finance and science. Papers are sought that contribute to the state of the art in symbolic modeling, either through innovative applications, theoretical work on issues of generalization, size and comprehensibility of the results produced, and algorithmic improvements to make the techniques faster, more reliable and generally better controlled.

*Organizers*

Steven Gustafson

Steven Gustafson is a computer scientist at the General Electric Global Research Center in Niskayuna, New York. As a member of the Computational Intelligence Lab, he develops and applies advanced AI and machine learning algorithms for complex problem solving. He received his PhD in computer science from the University of Nottingham, UK, where he was a research fellow in the Automated Scheduling, Optimisation and Planning Research Group. He received his BS and MS in computer science from Kansas State University, where he was a research assistant in the Knowledge Discovery in Databases Laboratory. Dr. Gustafson is a member of several program committees, a member of the editorial board of the Journal of Artificial Evolution and Applications, and a Technical Editor-in-Chief of the new journal Memetic Computing. In 2006, he received the IEEE Intelligent System's "AI's 10 to Watch" award.

Steven Gustafson, GE Research,    -   

Maarten Keijzer,    -   

Arthur Kordon

Arthur Kordon is a Data Mining & Modeling Leader in the Data Mining & Modeling Group in Corporate Work Process and Six Sigma Center, 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 based on computational intelligence for improved manufacturing and new product design, such as robust inferential sensors, automated operating discipline, and accelerated fundamental model building. 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, one book and 8 book chapters in the area of applied computational intelligence and advanced control. Dr. Kordon is a member of the Technical Committee on Evolutionary Computation of IEEE Computational Intelligence Society. 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.

Arthur Kordon,    -   

This full day workshop will take place on Wednesday July 8th 2009 and will involve presentations by approximately 12 selected graduate students conducting research in some aspect of evolutionary computation. Students will make 15-20 minute presentations to an audience that will include a 'mentor' panel of established researchers in evolutionary computation.
Presentations will be followed by a 10 minute question and discussion period led by the mentor panel.
The goal of this workshop is to assist students regarding their research: research methodology, goals, and plans. Students will also receive feedback on their presentation style. Other attendees will benefit by learning about current research, engaging in technical discussions and meeting researchers with related interests. Other students are encouraged to attend as a means of strengthening their own research.

The group of presenting students will be chosen with the intent of creating a diverse group of students working on a broad range of topic areas. You are an ideal candidate if your thesis topic has already been approved by your university and you have been working on your thesis or dissertation for between 6 and 18 months.

*Organizer*

Steven Gustafson

Steven Gustafson is a computer scientist at the General Electric Global Research Center in Niskayuna, New York. As a member of the Computational Intelligence Lab, he develops and applies advanced AI and machine learning algorithms for complex problem solving. He received his PhD in computer science from the University of Nottingham, UK, where he was a research fellow in the Automated Scheduling, Optimisation and Planning Research Group. He received his BS and MS in computer science from Kansas State University, where he was a research assistant in the Knowledge Discovery in Databases Laboratory. Dr. Gustafson is a member of several program committees, a member of the editorial board of the Journal of Artificial Evolution and Applications, and a Technical Editor-in-Chief of the new journal Memetic Computing. In 2006, he received the IEEE Intelligent System's "AI's 10 to Watch" award.

Steven Gustafson, GE Research,    -   

The seventh annual Undergraduate Student Workshop at a GECCO conference will occur on Wednesday, July 8th, 2009 as part of the GECCO-2009 conference in Montreal, QC, Canada. The workshop will provide an opportunity for undergraduate students to present their research in evolutionary computation.

Typically, presentations will describe senior-level research projects supervised by a faculty mentor; however, summer research projects or exceptional course projects may also be appropriate.
The workshop will be a half-day event, during which approximately eight undergraduate students will present their work to each other, to participating students' faculty mentors, and to GECCO participants interested in undergraduate research. Students should plan on 15-minute presentations, followed by five minutes of questions and discussion.

Students invited to the workshop will also participate in the conference poster session. Students will display posters summarizing their work, allowing the larger GECCO community to see what's being done by undergraduates in evolutionary computation. The poster session will also be a great opportunity for networking!

The goals of the Undergraduate Student Workshop are to:

• provide a forum allowing undergraduate students to put a capstone on their undergraduate research activities, by presenting their work at an international conference
• encourage teaching faculty to consider undergraduate research opportunities for their students in the EC field
• prepare undergraduate students for graduate work in EC areas
• encourage sharing and networking amongst teaching faculty with students participating in undergraduate research projects in EC
• provide networking opportunities for graduate school faculty and undergraduate students interested in pursuing advanced degrees, and
• encourage more emphasis on education at the GECCO conference

*Organizers*

Clare Bates Congdon

Clare Bates Congdon received her BA from Wesleyan University and MS and PhD from The University of Michigan, and has been teaching evolutionary computation and machine learning to undergraduates for over a dozen years. She is an advocate and mentor for undergraduate research, and has been bringing undergraduate students to GECCO and other conferences to present their evolutionary computation research since 2000. Her research (including that done with undergraduates) includes evolutionary computation as applied to areas such as bioinformatics, art, and robotics; her project "Machine Learning for Phylogenetics and Genomics" is funded by the NIH INBRE program.

Clare Bates Congdon, University of Southern Maine,   -   

Laurence D. Merkle, Rose-Hulman Institute of Technology,    -   

Frank W. Moore, University of Alaska Anchorage,    -