* 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,  -  
Hans-Georg Beyer, FH Vorarlberg GmbH,   -  
Nikolaus Hansen, INRIA,  -  
Steffen Finck, University of Applied Sciences Vorarlberg,   -  
Raymond Ros, Université Paris Sud,   -  
Marc Schoenauer, INRIA,   -  
Darrell Whitley, Colorado State University,   -  

*Organizers*
Garnett Wilson,    -   
Simon Harding,    -   
W. B. Langdon,   -   
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, AI Research Group, Harvard University,    -   
William Rand, Center for Complexity in Business, University of Maryland,    -   
Rick Riolo, Center for the Study of Complex Systems, University of Michigan,    -   

*Organizers*
Nawwaf Kharma,   -   
William R. Buckley,    -   
Julian Miller,    -   
Kenneth Stanley,    -   
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, University of Nottingham,    -   
Will Browne, University of Reading,    -   
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,    -   

The Workshop focuses on the application of genetic and evolutionary computation (GEC) to problems in medicine and healthcare.
Subjects 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*
Stephen L. Smith, The University of York,    -   
Stefano Cagnoni, Universita' degli Studi di Parma,    -   

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, GE Research,    -   
Maarten Keijzer,    -   
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, 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, University of Southern Maine,   -   
Laurence D. Merkle, Rose-Hulman Institute of Technology,    -   
Frank W. Moore, University of Alaska Anchorage,    -