In 2009, GECCO will be held WEDNESDAY to SUNDAY, not the traditional Saturday through Wednesday. However, WORKSHOPS and TUTORIALS will still take place on the first two days of the conference. See schedule.

Call for GECCO 2008 Workshop Proposals   closed on November 10th, 2007


Important Dates

March 25, Wednesday Workshop paper submissions deadline
April 3, Friday Workshop paper decision notifications sent
April 17, Friday Camera ready deadline for accepted workshop papers
April 27, Monday Registration deadline for presenting authors


OVERVIEW:

Call for Workshop Papers

Workshop organizers invite submissions. Refer to the details link for each individual workshop for submission details.

Accepted workshop papers will be published in the GECCO 2009 Companion Material and be included with the proceedings on a CD, and also in the ACM Digital Library. Accepted papers must be submitted to the publisher use the ACM template.

Maximum pages for final, camera-ready files:

Workshop papers: 8 page limit
Student Workshop Papers: 4 page limit

Templates and final file preparation instructions:
http://www.sheridanprinting.com/typedept/gecco1.htm.


2009 Workshops

Thirteen workshops are in the schedule this year, including Graduate and Undergraduate Student Workshops.

1 Automated Heuristic Design: Crossing the Chasm for Search Methods
Gabriela Ochoa, University of Nottingham,   -   
Ender Ozcan, University of Nottingham,    -   
Marc Schoenauer, INRIA,    -    
[ summary | details ]

2 Black Box Optimization Benchmarking (BBOB)
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,   -  
[ summary | details ]

3 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU)
Garnett Wilson,    -   
Simon Harding,    -   
W. B. Langdon,   -   
Man Leung Wong,    -   
[ summary | details ]

4 Defense Applications of Computational Intelligence Workshop (DACI)
Laurence D. Merkle,    -   
Frank W. Moore, University of Alaska Anchorage,    -   
[ summary | details ]

5 Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)
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,    -   
[ summary | details ]

6 Generative & Developmental Systems Workshop (GDS)
Nawwaf Kharma,   -   
William R. Buckley,    -   
Julian Miller,    -   
Kenneth Stanley,    -   
Garnett Wilson,    -   
[ summary | details ]

7 Learning Classifier Systems (IWLCS)
Jaume Bacardit, University of Nottingham,    -   
Will Browne, University of Reading,    -   
Jan Drugowitsch, University of Rochester,    -   
[ summary | details ]

8 Learning from Failures in Evolutionary Computation (LFFEC)
Nicola Beume, Technische Universität Dortmund,    -   
Mike Preuss, Technische Universität Dortmund,    -   
[ summary | details ]

9 Medical Applications of Genetic and Evolutionary Computation (MedGEC)
Stephen L. Smith, The University of York,    -   
Stefano Cagnoni, Universita' degli Studi di Parma,    -   
[ summary | details ]

10 Support of Patient Care Workshop (SPC)
Jim DeLeo,  National Institutes of Health -
Alexandru Floares,   Oncological Institute Cluj-Napoca -
Aaron Baughman, IBM Global Services   -    
[ summary | details ]

11 Symbolic Regression and Modeling Workshop (SRM)
Steven Gustafson, GE Research,    -   
Maarten Keijzer,    -   
Arthur Kordon,    -   
[ summary | details ]

12 Graduate Student Workshop (GSW)
Steven Gustafson, GE Research,    -   
[ summary | details ]

13 Undergraduate Student Workshop (UGSW)
Clare Bates Congdon, University of Southern Maine,   -   
Laurence D. Merkle, Rose-Hulman Institute of Technology,    -   
Frank W. Moore, University of Alaska Anchorage,    -   
[ summary | details ]

>
1 Automated Heuristic Design: Crossing the Chasm for Search Methods

Despite the success of heuristic search methods, including evolutionary algorithms, in solving difficult real-world optimization problems, their application to newly encountered problems, or even new instances of known problems remains problematic - even for experienced researchers of the field not to mention newcomers, or scientists and engineers from other areas. Theory and/or practical tools are still missing to make them "crossing the chasm" (from Geoffrey A. Moore book - 1991, revised 1999 - "Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers"). The difficulties faced by (meta-)heuristics users arise mainly from the significant range of algorithm and/or parameter choices involved when using this type of approaches, and the lack of guidance as to how to proceed for selecting them. Moreover, state-of-the-art approaches for real-world problems tend to represent bespoke problem-specific methods which are expensive to develop and maintain.

This workshop aims at bringing together researchers from different sub-fields of computer science, artificial intelligence and operations research that have recognized the need for developing automated systems to replace the role of a human expert in the design and tuning of search heuristics, and who are, therefore, interested in developing more generally applicable methodologies. Our call is mainly directed to real-world hard combinatorial optimization where adaptive and automated robust approaches are still lacking.

Relevant topics include, but are not limited to:

• Hyper-heuristics
• Algorithm selection and portfolios
• Parameter setting - parameter control - parameter tuning
• Off-line techniques (DOE, DACE, Racing, SPO, ...)
• Adaptive and self-tuning algorithms
• Reactive search
• Optimization of meta-parameters
• Design of class-specific heuristics (e.g. using Genetic programming)
• Hybrid approaches
• Foundational studies (heuristic understanding)

The authors of accepted abstracts will have the opportunity to write a full paper based on their abstract and submit it for the special issue of a well-known journal.

*Organizers*


Gabriela Ochoa

Gabriela Ochoa received her PhD in Computer Science and Artificial Intelligence from the University of Sussex, UK, in 2001. From 2001 to 2006 she worked as a full time lecturer initially and later as an associate professor in the Department of Computer Science at the University Simon Bolivar in Caracas, Venezuela. Dr. Ochoa has been involved with inter-disciplinary research, and foundations and applications of evolutionary algorithms. She joined the Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer Science at the University of Nottingham, UK, in 2006, as a senior research fellow in the project Next Generation Decision Support: Automating the Heuristic Design Process, a far reaching initiative that aims at developing automated systems to intelligently select and evolve search heuristics for supporting human decision making in industry, biotechnology, and medicine.

Dr. Ochoa is a member of several program committees, and proposed and co-organized the first workshop on hyper-heuristics as a part of the PPSN X (2008) conference in Dortmund.

Gabriela Ochoa, University of Nottingham,   -   



Ender Ozcan is a Senior Research Fellow with the Automated Scheduling, Optimisation and Planning research group in the School of Computer Science at the University of Nottingham, UK. He received his PhD from the Department of Computer and Information Science at Syracuse University, NY, USA in 1998. He worked as a full time lecturer in the Department of Computer Engineering at Yeditepe University, Istanbul, Turkey from 1998-2007. He established and leaded the ARTIficial Intelligence research group from 2002. He also served as the Deputy Head of the Department from 2004-2007.


Ender Ozcan

Ender Ozcan has been working as a researcher in the field of metaheuristics focusing on evolutionary algorithms (memetic algorithms, PSO), hyper-heuristics, and their applications to the real-world and theoretical problems. He has been a member of the program committees in major international conferences and refereeing for reputable journals. He served as an expert evaluator for the National Technology Development Foundation of Turkey until 2007. He was a co-chair of the hyper-heuristic seminars at Istanbul Technical University, Turkey (2007) and the workshop on hyper-heuristics within the PPSN'2008 conference in Dortmund, Germany. He is the guest co-editor of the special issue for the Journal of Heuristics on hyper-heuristics that will appear in 2009. Ender Ozcan is an executive committee member of the LANCS initiative, which is one of the largest Science and Innovation Rewards given by EPSRC (Engineering and Physical Sciences Research Council, UK). He is an associate editor of the International Journal of Metaheuristic Computing, a forthcoming journal in 2010.

Ender Ozcan, University of Nottingham,    -   



Marc Schoenauer

Marc Schoenauer is "Directeur de Recherche" with INRIA. He graduated at Ecole Normale Superieure in Paris, and obtained a PhD in Numerical Analysis at Paris 6 University in 1980. From 1980 to 2001, he has been with CNRS, working at the Applied Maths Laboratory at Ecole Polytechnique. He then joined INRIA, and later founded the TAO team in September 2003 together with Michele Sebag. Marc Schoenauer has been working in the field of Evolutionary Computation since the early 90s, is author of more than 120 papers in journals and major conferences of that field. He is or has been advisor of 25 PhD students. He has also been part-time Associate Professor at Ecole Polytechnique from 1990 to 2004.

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,    -    

2 Black Box Optimization Benchmarking (BBOB)
Quantifying and comparing performance of optimization algorithms is one important aspect of research in search and optimization. However, this task turns out to be tedious and difficult to realize — at least if one is willing to accomplish it in a scientifically decent and rigorous way.
The BBOB 2009 workshop for real-parameter optimization will furnish most of this tedious task for its participants: (1) choice and implementation of a well-motivated benchmark function testbed, (2) design of an experimental set-up, (3) generation of data output for (4) post-processing and presentation of the results in graphs and tables.
What remains to be done for the participants is to allocate CPU-time, run their favorite (not necessarily brand-new) black-box real-parameter optimizer in different dimensionalities a few hundreds of times and finally start the post-processing procedure. Two testbeds are provided,

• 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

Anne Auger received her diploma in mathematics from the University of Paris VI, France, in 2001. She also obtained the French highest diploma for teaching mathematics, "Agregation de mathematiques". She received the doctoral degree from the university Paris VI in 2004. Afterwards, she worked for two years (2004-2006) as a postdoctoral researcher at ETH (in Zurich) in the Computational Laboratory (CoLab). Since October 2006, she holds a permanent research position at INRIA (French National Research Institute in Computer Science and Applied Mathematics). Her research interests are stochastic continuous optimization, including theoretical analyses of randomized search heuristics. She published more than fifteen articles at top conferences and journals in this area. She organized (and will organize) the biannual Dagstuhl seminar "Theory of Evolutionary Computation" in 2008 (and 2010).

Anne Auger, INRIA,  -  



Hans-Georg Beyer received the Diploma degree in Theoretical Electrical Engineering from the Ilmenau Technical University, Germany, in 1982 and the Ph.D. in physics from the Bauhaus-University Weimar, Germany, in 1989. He finished his Habilitation thesis at the University of Dortmund, Germany, in computer science (CS) in 1997. From 1982 to 1984, he worked as an R&D Engineer in the Reliability Physics Department, VEB Gleichrichterwerk, Stahnsdorf, Germany. From 1984 to 1989, he was Research and Teaching Assistant and later on Postdoc at the Physics Department and the CS Department, Bauhaus-University Weimar. From 1990 to 1992, he worked as a Senior Researcher in the Electromagnetic Fields Theory Group at the Darmstadt University of Technology, Germany. From 1993 to 2004 he was with the CS Department of the University of Dortmund.


Hans-Georg Beyer

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 "Directeur de Recherche" with INRIA. He graduated at Ecole Normale Superieure in Paris, and obtained a PhD in Numerical Analysis at Paris 6 University in 1980. From 1980 to 2001, he has been with CNRS, working at the Applied Maths Laboratory at Ecole Polytechnique. He then joined INRIA, and later founded the TAO team in September 2003 together with Michele Sebag. Marc Schoenauer has been working in the field of Evolutionary Computation since the early 90s, is author of more than 120 papers in journals and major conferences of that field. He is or has been advisor of 25 PhD students. He has also been part-time Associate Professor at Ecole Polytechnique from 1990 to 2004.


Marc Schoenauer

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,   -  

 

3 Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU)

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 was awarded a PhD in Electronic Engineering from the University of York, UK in 2006. He has published widely in computational intelligence, unconventional computing, genetic programming and artificial developmental systems. He is currently a researcher at Memorial University, Canada.
Dr Harding previously co-organised CIGPU 2008. Later this year, he will be delivering an invited tutorial on genetic programming on GPUs at the Fifth Latin-American Summer School on Computational Intelligence, in Chile. He has several publications on GPU programming, including the first paper describing general purpose genetic programming on GPUs. Dr. Harding also administers the gpgpgpu.com web page.


Simon Harding

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,   -   



Man Leung Wong is an associate professor at the Department of Information Systems of Lingnan University, Tuen Mun, Hong Kong. Before joining the university, he worked as an assistant professor at the Department of Systems Engineering and Engineering Management, the Chinese University of Hong Kong and the Department of Computing Science, Hong Kong Baptist University. He worked as a research engineer at the Hypercom Asia Ltd. in 1997. His research interests are evolutionary computation, data mining, machine learning, knowledge acquisition, and approximate reasoning.


Man Leung Wong

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,    -   

4 Defense Applications of Computational Intelligence Workshop (DACI)

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*

Larry Merkle teaches computer science, mathematics, and computer engineering courses and advises senior thesis students at Rose- Hulman Institute of Technology. He served as an active duty officer in the United States Air Force from 1988 through 2002, and continued to serve as a reservist through 2007. He became involved in evolutionary computation in 1991, and has been involved in its application to a number of problems of interest to the military, including design of materials with nonlinear optical properties, design of high-power microwave sources, modeling of biochemical processes in molecular computing applications, and enhancing the effectiveness of compilers for polymorphous computing architectures. During the summer of 2004, he held a Visiting Professor position with the Air Force Research Laboratory where he studied evolvable hardware.


Laurence D. Merkle,    -   




Frank Moore is an Associate Professor of Computer Science at the University of Alaska Anchorage. He has taught computer science, computer engineering, and electrical engineering courses at the undergraduate and graduate level since 1997. In addition, he has over six years of industry experience developing software for a wide variety of military research and development projects. His recent research at the Air Force Research Laboratory has used evolutionary computation to optimize transforms that outperform wavelets for signal compression and reconstruction. He has received three Visiting Faculty Research Program awards, and has published over 50 journal articles, conference papers, and technical reports.


Frank Moore


Frank W. Moore, University of Alaska Anchorage,    -   

5 Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)

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 is currently a Post-Doctoral Fellow at Northwestern Institute on Complex Systems at Northwestern University; he obtained his Ph.D. from the University of Michigan working under Rick Riolo and John Holland. His dissertation focused on the use of GAs in dynamic environments, and at the same time he helped develop a large-scale model of residential land-use decisions in Southeastern Michigan to model the effects of suburban sprawl. At Northwestern, Bill is co-authoring a textbook with Uri Wilensky that is the a hands-on introduction to agent-based modeling.


William Rand


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,    -   

6 Generative & Developmental Systems Workshop (GDS)

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.


Dr. Miller chaired of the Evolvable Hardware tracks at the Genetic and Evolutionary Computation Conference in 2002-2003 and the Genetic Programming track chair in 2008. He was co-chair the Generative and Developmental Systems(GDS) track in 2007 and is track co-chair of GDS in 2009. He is an associate editor of the journals IEEE Transactions on Evolutionary Computation, and Genetic Programming and Evolvable Machines. He is an editorial board member of the journals Evolutionary Computation and Unconventional Computing. He has given 35 invited talks at conferences, universities, research institutions and commercial companies.

Julian Miller,    -   




Kenneth O. Stanley is an assistant professor in the School of Electrical Engineering and Computer Science at the University of Central Florida. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT) and HyperNEAT algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, and interactive evolution. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, and HyperNEAT. He is the chair of the IEEE Task Force on Computational Intelligence and Video Games, and has chaired the Generative and Developmental Systems track at GECCO for the last three years.


Kenneth O. Stanley


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.

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,    -   

7 Learning Classifier Systems (IWLCS)

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,    -   



Will Browne received the B. Eng. (Hons.) degree in Mechanical Engineering from the University of Bath, UK, in 1993 and the M. Sc. in Energy (Distinction) from the University of Wales, Cardiff in 1994. From 1994 to 1998 he was associated with British Steel and the University of Wales, Cardiff, through the Engineering Doctorate scheme, South Wales. His thesis regarded the industrial development of a Learning Classifier System for the Data Mining of quality control within a Steel Mill. From 1998 to 2001 he worked as a Post Doctoral Research Associate in the Control and Instrumentation Research Group, University of Leicester, UK. In October 2001 he was appointed to a Lectureship in the Cybernetic Intelligence Research Group (CIRG), University of Reading.


Will Browne


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,    -   

8 Learning from Failures in Evolutionary Computation (LFFEC)

"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

Nicola Beume is a Research Associate at the Department of Computer Science, TU Dortmund University since her Diploma degree in computer science there from (2006). Her research is focused on the design and the theoretical and empirical analysis of multi-objective evolutionary computation since 2004. She also designs and applies methods of computational intelligence to real-world applications, currently in the area of games.

Nicola Beume, Technische Universität Dortmund,    -   



Mike Preuss is Research Associate at the Department of Computer Science, TU Dortmund University, Germany (since 2000), where he also received his Diploma degree in 1998. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective niching and the experimental methodology for (non-deterministic) optimization algorithms. He is currently working on the adaptability and applicability of computational intelligence techniques for various engineering domains and computer games.


Mike Preuss

Mike Preuss, Technische Universität Dortmund,    -   

9 Medical Applications of Genetic and Evolutionary Computation (MedGEC)

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*


Stephen L. Smith

Stephen L. Smith received a BSc in Computer Science and then an MSc and PhD in Electronic Engineering from the University of Kent, UK. He is currently a senior lecturer in the Department of Electronics at the University of York, UK.

Steve’s main research interests are in developing novel representations of evolutionary algorithms particularly with application to problems in medicine. His work is currently centered on the diagnosis of neurological dysfunction and analysis of mammograms. Steve was program chair for the Euromicro Workshop on Medical Informatics, program chair and local organizer for the Sixth International Workshop on Information Processing in Cells and Tissues (IPCAT) and guest editor for the subsequent special issue of BioSystems journal.

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 has been with the Dipartimento di Ingegneria dell'Informazione of the Universita' degli Studi di Parma since 1997, where he is currently Associate Professor. He graduated in Electronic Engineering at the University of Florence in 1988 where he has been a PhD student until 1993 and a post-doc until 1997. In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology.

He is Editor-in-chief of the "Journal of Artificial Evolution and Applications". He has been member of the Managing Board and secretary of the Italian Association for Artificial Intelligence (AI*IA) from 2006 to 2007. He has been chairman of EvoIASP since 1999.


Stefano Cagnoni


Stefano Cagnoni, Universita' degli Studi di Parma,    -   

10 Support of Patient Care Workshop (SPC)

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

Aaron Baughman is a computer scientist and software engineer at International Business Machines (IBM), USA. He initiated and co-chaired the first IBM Academy of Technology conference on biometric analytics at IBM Research and founded IBM's virtual biometric community. He has several pending patents in the area of medical analytics and finished observations at the National Institutes of Health and the McKnight Brain Institute.

Aaron Baughman, IBM Global Services   -    

11 Symbolic Regression and Modeling Workshop (SRM)

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,    -   

12 Graduate Student Workshop (GSW)

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,    -   

13 Undergraduate Student Workshop (UGSW)

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,   -   



Larry Merkle teaches computer science, mathematics, and computer engineering courses and advises senior thesis students at Rose- Hulman Institute of Technology. He served as an active duty officer in the United States Air Force from 1988 through 2002, and continued to serve as a reservist through 2007. He became involved in evolutionary computation in 1991, and has been involved in its application to a number of problems of interest to the military, including design of materials with nonlinear optical properties, design of high-power microwave sources, modeling of biochemical processes in molecular computing applications, and enhancing the effectiveness of compilers for polymorphous computing architectures. During the summer of 2004, he held a Visiting Professor position with the Air Force Research Laboratory where he studied evolvable hardware.


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



Frank Moore is an Associate Professor of Computer Science at the University of Alaska Anchorage. He has taught computer science, computer engineering, and electrical engineering courses at the undergraduate and graduate level since 1997. In addition, he has over six years of industry experience developing software for a wide variety of military research and development projects. His recent research at the Air Force Research Laboratory has used evolutionary computation to optimize transforms that outperform wavelets for signal compression and reconstruction. He has received three Visiting Faculty Research Program awards, and has published over 50 journal articles, conference papers, and technical reports.


Frank Moore


Frank W. Moore, University of Alaska Anchorage,    -   

 

 

 
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