Important Deadlines

Submission of workshop papers:
Wednesday March 26
Notification of authors:
Wednesday, April 2
Camera ready files due:
Friday, April 18
Author registration:
Monday, April 21

See file format instructions here

Tutorials and workshops will be presented on Saturday, July 12 and Sunday, July 13, 2007.
View the tutorial schedule.

Please send inquiries regarding workshops by e-mail to the workshop chair Marc Ebner, Universität Würzburg, Germany -

Call for workshop proposals closed on 1 November, 2007


SoftGEC 2008
(2 hours)
2nd GECCO Workshop on Open-Source Software for Applied Genetic and Evolutionary Computation (SoftGEC)
Jason H. Moore (Dartmouth Medical School),
[ summary | details ]

DACI 2008
(Full Day)
2nd Defense Applications of Computational Intelligence Workshop at GECCO 2008
Laurence D.Merkle (Rose-Hulman Inst. of Tech.),
Frank W. Moore (University of Alaska Anchorage),
[ summary | details ]

(Half Day)
6th Undergraduate Student Workshop at GECCO 2008
Laurence D.Merkle(Rose-Hulman Inst. of Tech.),
Frank W. Moore (University of Alaska Anchorage),
Clare Bates Congdon (University of Southern Maine),
[ summary | details ]

MedGEC 2008
(Half Day)
4th Workshop on Medical Applications of Genetic and Evolutionary Computation at GECCO 2008
Stephen L. Smith (The University of York),
Stefano Cagnoni (Universita'degli Studi di Parma),
[ summary | details ]

LCS Workshop 2008
(Full Day)
11th International Workshop on Learning Classifier Systems
Jaume Bacardit (University of Nottingham),
Ester Bernado-Mansilla (Ramon Llull University),
Martin V. Butz (University of Würzburg,
[ summary | details ]

ARC-FEC Workshop 2008
(Half Day)
Advanced Research Challenges in Financial Evolutionary Computing
Christopher D. Clack (University College London),
[ summary | details ]

Optimization by Building and Using Probabilistic Models (OBUPM-2008)
(Half Day)
Mark Hauschild (University of Missouri in St. Louis),
Martin Pelikan (University of Missouri in St. Louis),
Kumara Sastry (University of Illinois at Urbana-Champaign),
[ summary | details ]

Graduate Student Workshop 2008
(Full Day)
Steven Gustafson (GE GLobal Research),
[ summary | details ]

EAIDO 2008
Workshop on Evolutionary Algorithms for Industrial Design Optimisation
Partha S. Dutta (Rolls-Royce plc),
Aniko Ekart (Aston University),

ECoMASS 2008
(Half Day)
2nd Evolutionary Computation and Multi-Agent Systems and Simulation Workshop
William Rand (Northwestern University),
Sevan G. Ficici (Harvard University),
Rick Riolo (University of Michigan),
[ summary | details ]


SoftGEC 2008

The field of Genetic and Evolutionary Computation (GEC) is undergoing a transition from a largely theoretical discipline to a more applied discipline as more and more people discover the power and utility of GEC methods. Freely-available, open-source, and user-friendly software for the application of GEC methods to real-world problems is more important than ever as GEC algorithms mature. However, there are few examples of software for applied GEC that are free, open-source, platform-independent, and user-friendly.

The second annual workshop on Open-Source Software for Applied Genetic and Evolutionary Computation (SoftGEC), organized in connection with the GECCO 2008 in Atlanta, is intended to explore and critically evaluate the development, evaluation, distribution, and support of GEC software. The availability of software is very important if GEC is going to find its way into applied computer science in fields such as engineering, economics, and bioinformatics. This is partly because the development, evaluation, distribution, and support of user-friendly software is both time consuming and expensive. This workshop will critically explore barriers to the development and dissemination of GEC software and will critically evaluate solutions.

Jason H. Moore

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

He also holds adjunct positions in the Department of Computer Science at the University of New Hampshire and the Department of Computer Science at the University of Vermont!. Dr. Moore serves as Director of Bioinformatics at Dartmouth Medical School and Founder and Director of The DISCOVERY resource, a 330-processor parallel computer cooperatively operated for the Dartmouth community. His research has been communicated in more than 150 scientific publications. He serves as Co-Editor-in-Chief of a new journal called BioData Mining.

Defense Applications of Computational Intelligence

The Defense Applications of Computational Intelligence (DACI) Workshop will be held on Sunday, July 13, 2008 as part of the 2008 Genetic and Evolutionary Computation Conference (GECCO-2008). This full-day workshop will 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-evolution for simultaneous red-blue team strategic-tactical simulation and gaming.

* Other computational intelligence techniques for applications in the areas listed above.

Eight-page papers are due March 26. For additional information, please visit the workshop web site at:

Frank Moore

Frank Moore - has taught computer science, computer engineering, and electrical engineering courses at the undergraduate and graduate level for the past 12 years. In addition, he has over six years of industry experience developing software for a wide variety of military projects, including the Integrated Test Bed, Avionics Integration Support Technology, Crew-Centered Cockpit Design, and Approach Procedures Expert System projects. His recent research at the Air Force Research Laboratory has used evolutionary computation to optimize transforms that outperform wavelets for image compression and reconstruction under quantization. He has received over $300,000 in research funding, including three Visiting Faculty Research Program awards from the Air Force, and has published over 50 journal articles, conference papers, and technical reports.

Undergraduate Student Workshop

The sixth annual Undergraduate Student Workshop will occur on Saturday, July 12, 2008. This half-day workshop will provide an excellent opportunity for undergraduate students to present a senior research project, summer project, or exceptional course project involving evolutionary computation, and receive valuable feedback from an international panel of experts. 4-page papers are due March 26; for additional details, please see

Frank Moore

Frank Moore - has taught computer science, computer engineering, and electrical engineering courses at the undergraduate and graduate level for the past 12 years. In addition, he has over six years of industry experience developing software for a wide variety of military projects, including the Integrated Test Bed, Avionics Integration Support Technology, Crew-Centered Cockpit Design, and Approach Procedures Expert System projects. His recent research at the Air Force Research Laboratory has used evolutionary computation to optimize transforms that outperform wavelets for image compression and reconstruction under quantization. He has received over $300,000 in research funding, including three Visiting Faculty Research Program awards from the Air Force, and has published over 50 journal articles, conference papers, and technical reports.


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


MedGEC 2008 -Medical Applications of Genetic and Evolutionary Computation

MedGEC 2008 is the fourth 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.


Stephen Smith

Stephen 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 fourth year. They are also guest editors of the recent December special issue of Genetic Programming and Evolvable Machines (Springer) on medical applications.

Steve has some 65 refereed publications, is a Chartered Engineer and a member of the British Computer Society.

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) in 2006 and 2007. He has been chairman of EvoIASP since 1999.

Stefano Cagnoni

In 2001 and 2002 he was General Chair of EvoWorkshops, the European joint event of which EvoIASP is a component.
He has been chairman and organiser of GSICE, the Italian Workshop on Evolutionary Computation, in 2005 and 2006, and organizer of SECEVITA, the Italian Summer School on Evolutionary Computation and Artificial life in 2007. He was chairman of EC2AI, the workshop on Evolutionary Computation which was held at ECAI, the European Conference on Artificial Intelligence, in 2006. He has co-chaired, in 2005, 2006 and 2007, MedGEC, workshop on medical applications of evolutionary computation held concurrently with GECCO. He has co-edited three special issues dedicated to "Genetic and Evolutionary Computation for Image Analysis and Signal Processing" in: EURASIP Journal of Applied Signal Processing (2003), Pattern Recognition Letters (2006), and Evolutionary Computation (in press); as well as a special issue of "Intelligenza Artificiale", the journal of the Italian Association for Artificial Intelligence, dedicated to " Evolutionary Computation" (2007).

His main basic research interests are in the field of Soft Computing, with particular regard to Evolutionary Computation, and in computer vision. As concerns applied research, the main research topics are the application of the above-mentioned techniques to problems of pattern recognition and robotics. He has published over 60 papers in recognized international journals and conferences.

Learning Classifier Systems (LCSs)

Since Learning Classifier Systems (LCSs) were introduced by Holland 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, receiving a great deal of attention. LCS are also benefiting from advances in reinforcement learning and other machine learning techniques.

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, ...)
* Applications (data mining, medical domains, bioinformatics, ...)


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.


Ester Bernad-Mansilla.- Dr. Bernad-Mansilla is an associate professor at the Computer Engineering Department of Enginyeria i Arquitectura La Salle, Ramon Llull University, located at Barcelona, Spain. She is the director of the Research Group in Intelligent Systems. Dr. Bernad-Mansilla's research interests are focused on the study of genetic-based machine learning and related areas: machine learning, data mining, pattern recognition, etc. She has participated in several projects on machine learning applied to medical diagnosis, and specifically has studied the application of rule-based evolutionary learning systems to such real-world problems.

Ester Bernad-Mansilla

Bernad-Mansilla is on the program committee of the Genetic and Evolutionary Computation Conference (GECCO 2003-2007) and the International Workshop on Learning Classifier Systems (IWLCS 2005-2007), as well as other relevant conferences on artificial intelligence, pattern recognition, machine learning and neural networks (e.g., ICPR, IJCNN, IEEE MCDM). She has co-organized the 10th International Workshop on Learning Classifier Systems (IWLCS'2007) held at the 2007 GECCO Conference, as well as the Fourth Catalan Conference on Artificial Intelligence (CCIA 2001). Bernadnsilla has co-edited two books on Learning Classifier Systems and Data Mining.

Martin V. Butz

Martin V. Butz received his PhD in computer science at the University of Illinois at Urbana-Champaign in October 2004 under the supervision of David E. Goldberg. His thesis "Rule-based evolutionary online learning systems: Learning Bounds, Classification, and Prediction" puts forward a modular, facet-wise system analysis for Learning Classifier Systems (LCSs) and analyzes and enhances the XCS classifier system. Until September 2007, Butz was working at the University of Wrzburg at the Department of Cognitive Psychology III on the interdisciplinary cognitive systems project "MindRACES: From reactive to anticipatory cognitive embodied systems". In October 2007 he founded his own cognitive systems laboratory: "Cognitive Bodyspaces: Learning and Behavior" (COBOSLAB), funded by the German research foundation under the Emmy Noether framework.

Butz is the co-founder of the "Anticipatory Behavior in Adaptive Learning Systems (ABiALS)" workshop series and is currently also co-organizing the "International Workshop on Learning Classifier Systems" (IWLCS) series. Butz has published and co-edited four books on learning classifier systems and anticipatory systems. Currently, he is focusing on the design of highly flexible and adaptive cognitive system architectures, based on recent research insights gained in cognitive psychology, behavioral neuroscience, evolutionary biology, and adaptive behavior.

ARC-FEC 2008
Advanced Research Challenges in Financial Evolutionary Computing

In recent years there has been a marked increase in the application of Evolutionary Computation (EC) to problems in finance. A brief survey of the research papers in GECCO'07 and CEC'07 provides roughly 90 authors publishing in these two conferences alone. Other popular venues for publishing such research include for example the Conference on Computational Intelligence in Economics and Finance (CIEF), the European Workshop on Evolutionary Computation in Finance and Economics (EvoFIN), and the Workshop on Economic Heterogeneous Interacting Agents (WEHIA). EC has been extensively applied to forecast financial time series, price options, speed up risk calculations, simulate financial markets, improve stock selection models, and so on. Problems in finance are often highly irregular, complex, noisy and volatile, and EC has been proven to operate well in such environments with very few a-priori requirements. Multi-objective EC techniques also fit well with the multiple objectives of investment managers who must constantly juggle such factors as return on investment, risk, liquidity, and even ethical constraints.

The GECCO Workshop on Advanced Research Challenges in Financial Evolutionary Computing (ARC-FEC) explores recent advances, speculative ideas and innovative research in Financial Evolutionary Computing (FEC). The workshop aims to bring together experienced and practising researchers to provide a lively forum in which they can share experience, identify issues, and start to map out the challenges and research directions for the future.

Accepted papers will be presented orally at the workshop and distributed in the GECCO 2008 Workshop Proceedings to all GECCO attendees. Authors are encouraged to contribute to, and will gain early access to, resources on the workshop web site - this will include a bibliography of research publications in FEC, and links to active researchers and groups in FEC.

For more details please see:


Christopher D. Clack

Christopher D. Clack is Director of Financial Computing at UCL. He founded UCL's Virtual Trading Floor and has attracted funds exceeding £ 2.1 million in the last three years. He is Coordinator of the PROFIT European Network in Financial Computing, which includes UCL, Deutsche Bank, Reuters, and the universities of Athens, Moscow, Prague, and Sofia. He was conference chair at Trade Tech Architecture 2008, is a regular panel member at both industry and academic conferences and workshops, and is also presenting a tutorial on Financial Evolutionary Computing at GECCO 2008. His research team focuses on applying Genetic Computation and multi-agent systems to real-world problems in finance, and his work on GP robustness in highly volatile markets won a Best Paper award at GECCO 2007.

He has twenty years' experience of consulting in Financial Computing, from settlement systems to portfolio optimization, and has established very close partnerships with Credit Suisse, Goldman Sachs, Merrill Lynch, Morgan Stanley, Reuters, and the London Stock Exchange.This provides unrivalled exposure to the most pressing technology problems in finance, coupled with invaluable access to real data.

Optimization by Building and Using Probabilistic Models (OBUPM-2008)

Genetic and evolutionary algorithms (GEAs) evolve a population of candidate solutions using two main operators: (1) selection and (2) variation. However, fixed, problem independent variation operators often fail to effectively exploit important features of high quality solutions obtained by selection to create novel, high-quality solutions. One way to make variation operators more effective is to replace traditional variation operators by the following two steps:

1. Estimate the distribution of the selected solutions on the basis of an adequate probabilistic model.

2. Generate a new population of candidate solutions by sampling from the distribution estimated.

Algorithms based on this principle are often called probabilistic model-building genetic algorithms (PMBGAs), estimation of distribution algorithms (EDAs) or iterated density estimation algorithms (IDEAs). The purpose of this workshop is to discuss recent advances in PMBGAs, theoretical and empirical results, applications of PMBGAs, and promising directions of future PMBGA research.

For additional information please visit the workshop website at:


Mark Hauschild

Mark Hauschild is a graduate student in the department of Computer Science at the University of Missouri in St. Louis and a research assistant at the Missouri Estimation of Distribution Algorithms Laboratory (MEDAL). His research interests include efficiency enhancement of estimation of distribution algorithms (with a particular emphasis on exploiting prior knowledge), machine learning, scalability of evolutionary computation, probabilistic model building genetic programming and applying estimation of distribution algorithms to solve real-world problems.


Martin Pelikan.- received Ph.D. from the Department of Computer Science at the University of Illinois at Urbana-Champaign in 2002. He joined the Department of Mathematics and Computer Science at the University of Missouri in St. Louis in 2003. Currently, he is an assistant professor of computer science and the director of the Missouri Estimation of Distribution Algorithms Laboratory (MEDAL). Pelikan's research focuses on genetic and evolutionary computation and machine learning. He worked at the Slovak University of Technology in Bratislava, the German National Center for Information Technology in Sankt Augustin, the Illinois Genetic Algorithms Laboratory (IlliGAL) at the University of Illinois at Urbana-Champaign, and the Swiss Federal Institute of Technology (ETH) in Zurich.

Martin Pelikan

Pelikan's most important contributions to genetic and evolutionary computation are the Bayesian optimization algorithm (BOA), the hierarchical BOA (hBOA), the scalability theory for BOA and hBOA, and efficiency enhancement techniques.

Kumara Sastry

Kumara Sastry received his Ph.D. in Systems and Entrepreneurial Engineering from the department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign in 2007. He was a member of the Illinois Genetic Algorithms Laboratory and the Materials Computation Center at the University of Illinois. He has been actively consulting on genetic and evolutionary algorithms to industry, including help startup a new web 2.0 company. His research interests include efficiency enhancement of genetic algorithms, estimation of distribution algorithms, scalability of genetic and evolutionary computation, facetwise analysis of evolutionary algorithms, and multi-scale modeling in science and engineering. He is currently an Engineer in the Lithography Modeling Group at Intel Corp.

Graduate Student Workshop :

This full day workshop will involve presentations by approximately 12 selected students pursing 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.

Students will also be invited present their work as a poster at the Tuesday evening Poster Session.

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.

Submissions should follow the general format of GECCO submission (see author guidelines on the main GECCO page). In addition, submissions should be accompanied by a brief cover letter including the student's current enrollment status (undergraduate, M.S. student, or Ph.D. student) and information regarding the extent of their research to date (e.g. number of months on the project, whether they've completed a proposal defense, or some similar indication of progress). Accepted papers will be included with the other workshop papers on the GECCO workshops CD. Awards will be presented for best work and best presentation.

Presenters should plan to present both their current research results and their future research goals and plans. Keeping in mind that the goal is to receive advice and suggestions on both the current status of their research and on their planed future research directions.


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.

ECoMASS 2008:

Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS) 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 adapts 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 individual decisions. Similarly, most work in EC is concerned with how to engineer selective pressures to drive the evolution of individuals towards some desired goal. Multi-agent simulation (also called agent-based modeling) addresses the bottom-up issue of how collective behavior emerges from individual action. Likewise, the study of evolutionary dynamics 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 analogous processes. It is therefore natural to consider how 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.

Example Topics: 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 the 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.


William Rand

William Rand.- Bill 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.

Sevan G. Ficici .- He 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

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.

 Genetic and Evolutionary Computation Conference (GECCO-2008)
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