Friday, March 23, 2007: Workshop Papers submission deadline
Friday, March 30, 2007: Accepted Workshop Paper authors notified
Wednesday, April 11:
Camera-ready files submission deadline
Wednesday, April 11, 2007: Conference registration deadline for authors presenting workshop papers

Proceedings of the workshops will be published on CD-ROM, and distributed at the conference.

Workshops will be held on Saturday, July 7 and Sunday, July 8, 2007.
View the workshops schedule.

Please send inquiries regarding workshops by e-mail to the workshop chair Tina Yu -    .
Call for workshop proposals closed on 15 November 2006.

Camera ready file preparation and submission instructions


Particle Swarms: The Second Decade
Riccardo Poli -  ,   Jim Kennedy - ,  Tim Blackwell - and Alex Freitas -
Duration: Half Day
[ summary | details ]

Open-Source Software for Applied Genetic and Evolutionary Computation (SoftGEC)
Jason H. Moore -  
[ summary | details ]

Optimization by Building and Using Probabilistic Models
Kumara Sastry -  , Martin Pelikan - 
Duration:Half Day
[ summary | details ]

Graduate Student Workshop
Anikó Ekárt -  
Duration:Full Day

[ summary | details ]

Undergraduate Student Workshop
Laurence Merkle - ,   Clare Bates Congdon -    & Frank Moore -
Duration: Half Day

[ summary | details ]

Evolutionary Algorithms for Dynamic Optimization Problems
Peter A.N. Bosman -   and   Jürgen Branke -
Duration: Half Day
[ summary | details ]

Parallel Bioinspired Algorithms
Francisco Fernández -   and  Erick Cantú-Paz -
Duration: Half Day
[ summary | details ]

Learning Classifier Systems
Jaume Bacardit - ,   Ester Bernadó-Mansilla - ,   Martin V. Butz -
Duration: Full Day
[ summary | details ]

Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS)
Bill Rand - ,    Sevan G. Ficici -
Duration: Half Day

[ summary | details ]

Petroleum Applications of Evolutionary Computation
Alexandre Castellini   -   , Charles Guthrie - , David Wilkinson - , Burak Yeten - , Tina Yu -
Duration: Half Day
[ summary | details ]

Defense Applications of Computational Intelligence
Frank Moore - ,   Laurence D. Merkle - ,    Stephen C. Upton -
Duration: Full Day
[ summary | details ]

Evolution of Natural and Artificial Systems - Metaphors and Analogies in Single and Multi-Objective Problems
Ami Moshaiov   -   , Steven Hecht Orzack, Joshua Knowles
Duration: Half Day

[ summary | details ]

Medical Applications of Genetic and Evolutionary Computation
Stephen L. Smith   -  , Stefano Cagnoni  - 
Duration: Half-day

[ summary | details ]

FX-SBSE - Foundations and cross cutting issues in Search Based Software Engineering
Mark Harman    -  , John Clark   -  , Xin Yao   -  , Joachim Wegener   -   , Christine McCulloch   -  , Tanja Vos   -
Duration: Half-day

[ summary | details ]

User-centric Evolutionary Computation
Iam Parmee   -  
Duration: Half-day

[ summary | details ]

FD-ET: Future Directions in Evolutionary Testing
Mark Harman   -  , John Clark   -  , Xin Yao   -  , Joachim Wegener   -  , Christine McCulloch   -   , Tanja Vos   -  
Duration: Half-day
[ summary | details ]


Particle Swarms: The Second Decade:

The particle swarm is a remarkable optimiser that has evolved in the last decade since it was proposed. It is little understood, yet it has a very simple formulation.

The workshop will focus on recent advances in PSO, both in terms of the understanding and the applications of the algorithm.

The aim is to attract papers on particularly innovative research, speculative ideas, and novel applications that could act as seeds for PSO research in its second decade.

The workshop will run for a half day and it will be divided into two sessions. Session 1 (3 hours, with 15 minute break) will be paper presentations (15-20 minute presentation followed by 5-10 minute discussion, each). Session 2 will be a panel discussion which will integrate the ideas presented during the workshop and will try to sketch the way ahead. The discussion will particularly focus on novel application areas, new PSO paradigms, and advances in theory.

Authors of selected papers presented at the workshops will be invited to submit an extended version of their manuscripts for inclusion in a special issue on particle swarms of the new journal in the field. The special issue has been approved by the editor-in-chief (Prof Stefano Cagnoni, University of Parma).


Riccardo Poli

Riccardo Poli:

Is a professor in the Department of Computer Science at Essex. His main research interests include genetic programming (GP), particle swarm optimisation, and the theory of evolutionary algorithms. In July 2003 Prof. Poli was elected a Fellow of The International Society for Genetic and Evolutionary Computation (ISGEC) " ... in recognition of sustained and significant contributions to the field and the community". He has published approximately 190 refereed papers on evolutionary algorithms, neural networks and image/signal processing. With W. B. Langdon, he wrote Foundations of Genetic Programming published by Springer in 2002.

He has been co-founder and co-chair of EuroGP for 1998, 1999, 2000 and 2003.

Prof. Poli was the chair of the GP track at the Genetic and Evolutionary Computation Conference (GECCO) 2002 and 2007 and was co-chair of the Foundations of Genetic Algorithms (FOGA) Workshop in 2002.
He was the general chair of GECCO in 2004 and served as a member of the business committee for GECCO 2005.
He was technical chair of the International workshop on Ant Colony Optimisation and Swarm Intelligence (ANTS 2006) and competition chair for GECCO 2006. He is an associate editor of Evolutionary Computation (MIT Press), Genetic Programming and Evolvable Machines (Springer), the International Journal of Computational Intelligence Research (IJCIR) and editoral board member of the Swarm Intelligence journal. Prof. Poli has been a programme committee member for over 50 international events.

He has presented invited tutorials at 10 international conferences. He is a member of the EPSRC Peer Review College and has attracted funding of over GBP 1.8M from EPSRC, DERA, Leverhulme Trust, Royal Society, etc.

James Kennedy:

Is a social psychologist who has been working with the particle swarm algorithm it since 1994. He received his Ph.D. in 1992 from the University of North Carolina, and works for the US Department of Labor in Washington, DC. He has published dozens of articles and chapters on particle swarms and related topics, in both computer-science and social-science journals and Proceedings. The Morgan Kaufmann volume, "Swarm Intelligence," by Kennedy and Russell C. Eberhart, is now in its third printing.

James Kennedy

Tim Blackwell

Tim Blackwell:

Is a lecturer in Computing at Goldsmiths College, University of London. He has degrees in physics, theoretical physics and computer science and has researched and published in a wide range of subjects including quantum field theory, condensed matter theory, psychology, computer music, digital art and swarm intelligence. He is well known for the application of swarms to improvised music, and his Swarm Music system has been the subject of numerous articles, radio programmes and TV documentaries worldwide. Recently he has headed, with the composer Michael Young, the EPSRC Live Algorithms for Music research network, an interdisciplinary network of 100+ musicians, engineers and computer and cognitive scientists. The aim of the network is to research autonomous computer music applications, and the network holds regular conferences, workshops and concerts.

His work in swarm optimization has been focused on dynamic problems. Using an atom analogy, he introduced 'charge' into particle swarm optimization (PSO) as a diversity increasing mechanism, and used classical and quantum models of particle motion to adapt PSO for dynamic optimization. In a collaboration with Juergen Branke (Germany), he devised a multi-swarm technique and used an "exclusion principle" to achieve an excellent optimization algorithm for dynamic, multi-modal, landscapes. He is Principal Investigator (Goldsmiths) for the EPSRC Extended Particle Swarms project. His recent work in this field is concerned with the distribution of particle positions within PSO, and he has developed, with Toby Richer (Australia) a Levy swarm. This work is inspired by studies of foraging animals which reveal power-law walk during searching periods, as modeled by the Levi distribution. He is currently working with the artist Janis Jefferies and exhibits and performs digital art internationally. This body of work concerns texture across the senses. As part of this research programme he has developed "Woven Sound" a technique for mapping sound to textile patterns. He uses principles of stigmergy, swarm animations and sound granulation to render woven sound back into sound in real-time performance. Recently he has been elected to the committee of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour and is a member of IEEE.

Alex A. Freitas:

Received his Ph.D. in Computer Science from the University of Essex, UK, 1997. He was a Visiting Lecturer at the Federal Center for Technological Education in Parana (CEFET-PR), Brazil, from 1997 to 1998; and an Associate Professor at the Pontifical Catholic University in Parana (PUC-PR), Brazil, from 1999 to 2002. In 2002 he moved to the University of Kent, UK, where he is currently a Senior Lecturer in Computer Science.

His publications include two research-oriented books on data mining the second of which is specialized on data mining with evolutionary algorithms and over 100 refereed research papers published in journals, books, conference or workshop proceedings.

Alex A. Freitas

Most of these papers are about data mining and/or bio-inspired algorithms. He is a member of the editorial board of three international journals, namely Intelligent Data Analysis, the International Journal on Data Warehousing and Mining, and the International Journal of Computational Intelligence and Applications. At present his main research interests are data mining and knowledge discovery, bio-inspired algorithms and bioinformatics. He is a member of IEEE, BCS-SGAI (The British Computer Society's Specialist Group on Artificial Intelligence), ACM-SIGKDD (ACM-F¢s Special Interest Group on Knowledge Discovery and Data Mining)-A and AAAI (American Association for Artificial Intelligence).

to top

Open-Source Software for Applied Genetic and Evolutionary Computation (SoftGEC)

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 first annual workshop on Open-Source Software for Applied Genetic and Evolutionary Computation (SoftGEC), organized in connection with the GECCO 2007 in London, 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. For more information, visit


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 Biological Sciences at Dartmouth College, the Department of Computer Science at the University of New Hampshire, and the Department of Computer Science at the University of Vermont. Dr. Moore serves as Director of the Bioinformatics Shared Resource for the Norris-Cotton Cancer Center and Founder and Director of The Discovery Resource, a 300-processor parallel computer cooperatively operated for the Dartmouth community. His research has been communicated in more than 120 scientific publications.

to top

Optimization by Building and Using Probabilistic Models:

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.


Kumara Sastry

Kumara Sastry:

Kumara Sastry is a graduate student in the department of Industrial and Enterprise Systems Engineering at the Univeristy of Illinois and a Member of the Illinois Genetic Algorithms Laboratory. 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 enhancment of genetic agorithms, estimation of distribution algorithms, scalability of genetic and evolutionary computation, facetwise analysis of evolutionary algorithms, and multi-scale modeling in science and engineering.

Martin Pelikan:

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.

to top

Graduate Student Workshop:

Submissions should be made electronically to ANIKO's EMAIL ADDRESS at with the subject: GECCO 2007 Graduate Student Workshop Submission

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.

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.

to top

Undergraduate Student Workshop:

Goals of the workshop include:

providing a forum allowing undergraduate students to put a "capstone" on their undergraduate research activities, through presentation of their work at an international conference;
encouraging teaching faculty to think about undergraduate research opportunities for their students in the evolutionary computation field;
preparing standout undergraduate students for graduate studies in the evolutionary computation field;
encouraging more focus on education amongst GECCO participants;
recruiting opportunities for faculty at advanced degree granting institutions; and
sharing and networking amongst teaching faculty with students participating in undergraduate research.


Laurence D. Merkle:

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 continues to serve as a reservist. 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. He has published over 50 conference papers and journal articles.

Clare Bates Congdon:

Clare received her BA from Wesleyan University and MS and PhD from The University of Michigan. 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. She is currently a Research Scientist in the Computer Science Department at Colby College. 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

Frank W. Moore

Frank W. Moore:

Frank has taught computer science, computer engineering, and electrical engineering courses at the undergraduate and graduate level for the past 11 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 three Visiting Faculty Research Program awards and over $200,000 in research funding from the Air Force, and has published over 50 journal articles, conference papers, and technical reports.


to top

Evolutionary Algorithms for Dynamic Optimization Problems

Many real-world optimization problems are dynamic. New jobs are to be added to a schedule, the quality of the raw material may be changing, new orders have to be included into the routing of a fleet of vehicles, etc. In such cases, when the problem changes over the course of the optimization, the purpose of the optimization algorithm changes from finding an optimal solution to being able to continuously track the movement of the optimum through time. Since in a sense natural evolution is a process of continuous adaptation, it seems straightforward to consider evolutionary algorithms as appropriate candidates for dynamic optimization problems.

And indeed, several attempts have been made to modify evolutionary algorithms, to tune them for optimization in a changing environment. It was observed in all these studies, that the dynamic environment requires the evolutionary algorithm to maintain sufficient diversity for a continuous adaptation to the changes of the landscape. The following basic strategies for modifying the evolutionary algorithm can be identified:

identify the occurrence of a change in the environment and then deliberately increase diversity in the population e.g. by means of increased mutation
try to avoid convergence all the time, e.g. by including new random individuals in the population in every generation
supply the EA with a memory, e.g. by using diploidy or an explicit memory, so that the EA can recall useful information from past generations.

More recent developments in the area include the use of anticipation, the role of flexibility, and multi-criteria aspects.

The goal of this workshop is to foster interest in the important subject of evolutionary algorithms for dynamic optimization problems, get together the researchers working on this topic, and to discuss recent trends in the area. The workshop will feature a series of selected presentations.

More info:


Peter A.N. Bosman

Peter A.N. Bosman:

Peter is a postdoctoral research fellow in the computational intelligence and multi-agent games theme at the Centrum voor Wiskunde en Informatica (CWI) (Centre for Mathematics and Computer Science) located in Amsterdam, the Netherlands. He finished his Ph.D. at the Utrecht University on the design and application of estimation-of-distribution algorithms in 2003. He has since then been an active researcher in the field of evolutionary computation. His current research position is mainly focused on optimization using evolutionary algorithms and (multi-)agent technology in logistics, a key application area of dynamic optimization algorithms.


Jürgen Branke:

Jürgen is research associate at the Institute for Applied Computer Science and Formal Description Methods (AIFB), University of Karlsruhe, Germany. He has been active in the area of nature-inspired optimization since 1994, and is a leading expert on optimization in the presence of uncertainties, including noisy or dynamically changing environments. Further research interests include complex system optimization, multi-objective optimization, robustness of solutions, parallelization, and agent-based modeling. Dr. Branke has written the first book on evolutionary optimization in dynamic environments in 2000 and has published over 80 peer-reviewed papers in international journals, conferences and workshops. Besides, he has applied nature-inspired optimization techniques to several real-world problems as part of industry projects.

Jürgen Branke


to top

Parallel Bioinspired Algorithms:

The application of Bioinspired Algorithm to solving difficult problems has shown that they need high computation power and communications technology. During the last few years researchers have tried to embody parallelism into this kind of algorithms, so that a very interesting research area has emerged and matured. The combination of Bioispired algorithms and Parallel systems has allowed researchers to develop new algorithms, and also solve hard problems.

The topics of interest for this workshop include (but are not limited to):

Parallel Evolutionary Algorithms (PEAs), including but not limited to Genetic Algorithms, Genetic Programming, Evolutionary Programming, and Evolution Strategies.
Other biologically inspired parallel algorithms: Ant Colonies, Immune Systems, Artificial Life, Cultural Algorithms.
P2P and Grid implementation of PEAs.
Fault tolerant implementations of PEAs.
Analytical modeling of PEAs.
Performance evaluation of PEAs.
Improvement in system performance through optimization and tuning.
Case studies showing the role of PEAs when solving hard real-life problems.


Francisco Fernández de Vega

Francisco Fernández de Vega:

Received his Ph.D. in Computer Science from the University of Extremadura, Spain, 2001. Currently, he is an associate professor of computer science and CIO of the University of Extremadura. He is also the director of the GEA (Artificial Evolution Group).

He has published over 90 referred papers. His research interests include Bioinspired Algorithms (Genetic Programming, Cellular Automata, Epidemic Algorithms) and Cluster and Grid Computing.

He is part of the steering committee of the Spanish conference on Evolutionary Algorithms (MAEB), and has presented invited tutorials at several international conferences (including CEC and PPSN).

He was co-chair of the 1st Workshop on Parallel Bioinspired Algorithms, that was held jointly with the IEEE International Conference on Parallel Processing 2005 in Oslo.

Erick Cantú-Paz:

Erick Cantú-Paz received a Ph.D. from the Department of Computer Science at the University of Illinois at Urbana-Champaign in 1999. He worked as a computer scientist at Lawrence Livermore National Laboratory and currently works in sponsored search relevance at Yahoo!, Inc. Cantú-Paz's research focuses on data mining, machine learning and efficiency improvements for evolutionary algorithms. He is mainly interested in large-scale machine learning from labeled and unlabeled data, learning from nonstationary sources, anomaly detection, feature subset selection, and applying his research to real-world applications. In evolutionary computation, his research interests focus on efficiency of evolutionary algorithms in general.

Erick Cantú-Paz

to top

Learning Classifier Systems

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:

Jaume 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. He is now working in the University of Nottingham, UK applying LCS to bioinformatics domains in a project called "Robust Prediction with Explanatory Power for Protein Structure and Related Prediction Problems", funded by the UK Engineering and Physical Sciences Research Council.


Ester Bernadó-Mansilla:

Ester is an associate professor at the Computer Engineering Depatment of Enginyeria i Arquitectura La Salle, Ramon Llull University, Barcelona, Spain. Her research interests are focused on the study of genetic-based machine learning and related aeas: machine learning, data mining, pattern recognition, etc. She is an expert of the applicability of Learning Classifier Systems to real-world problems, specifically XCS. She has designed a supervised a version of XCS, called UCS, which learns successfully and eficiently in supervised learning problems. She has participated in several projects on machine learning applied to medical diagnosis. She is currently participating in the KEEL (Knowledge Extraction with Evolutionary Learning) project, which is developed jointly by five teams of different Spanish universities and is supported by the Spanish Ministry of Science and Education.

Ester Bernadó-Mansilla


Martin V. Butz

Martin V. Butz:

Dr. Butz's areas of specialization include computer science and cognitive psychology. Butz's focus lies on artificial intelligence and machine learning, particularly on neural network architectures, evolutionary algorithms, and learning classifier systems. Butz is a specialist of the most renowned learning classifier system to-date: XCS, which he analyzed experimentally and analytically during his PhD studies. System improvements lead to successful applications in datamining, reinforcement learning, function approximation, and adaptive behavior.

to top

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


Bill Rand

Bill 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. Bill developed his first agent-based model (ABM) with an evolutionary component in 1996 at Michigan State University, and has maintained a research interest in ABM and GA ever since. 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 working with Uri Wilensky to develop and refine the methods and techniques of ABM, including the incorporation of evolutionary computation tools into ABM.

Sevan G. Ficici:

Sevan 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 as well as co-presenter, with Anthony Bucci, of the Introductory Tutorial on Coevolution at GECCO 2006; Sevan and Anthony will be presenting the Advanced Tutorial on Coevolution at GECCO 2007.

Sevan G. Ficici

to top

Petroleum Applications of Evolutionary Computation

This workshop is intended to encourage communication among EC researchers, petroleum engineers and scientists to better understand the current and ongoing efforts in using evolutionary computation techniques in solving petroleum-related problems. Such interaction can help advance new development of evolutionary computation techniques for petroleum-related applications.

more info:

Alexandre Castellini

Alexandre Castellini :

Alexandre received his MSc in Fluid Mechanics from Toulouse, France and his MSc in Petroleum Engineering from Stanford University. He has been working for Chevron Energy Technology Company since 2001 and has applied evolutionary computation techniques to a number of petroleum reservoir studies, including calibration of models to production data and uncertainty estimation of production profiles. He is a technical editor for the Reservoir Engineering Journal of the Society of Petroleum Engineers.

Charles Guthrie:

Charlie received his M.S. in Chemical Engineering from the Massachusetts Institute of Technology in 1983 and joined Gulf Oil in Pittsburgh, Pennsylvania. In 1985, Chevron purchased Gulf Oil and Charlie transferred to Chevron Energy Technology Company in Richmond, California. Since then, he has held a variety of positions in process development, process engineering and most recently process modeling for refinery and oil production. In his modeling work, Charlie applied a wide variety of techniques from traditional gradient-based non-linear optimizers to genetic algorithms and particle swarms. Charlie’s expertise lies in matching solution techniques to problem requirements, designing robust delivery platforms and doing whatever it takes to get it used.

Charles Guthrie

Burak Yeten

Burak Yeten :

Burak Yeten works as a reservoir simulation engineer at the Resevoir Simulation Consulting Team of Energy Technology Company of Chevron, based in San Ramon, California, USA. He holds BS. and MSc. degrees from Middle East Technical University and a Phd. Degree from Stanford University, all in petroleum engineering.

His research areas include optimization of development and management of petroleum reservoirs, history matching, uncertainty assessment, decision analysis and deployment and control of smart wells. He has published more than 20 papers on these fields. He has been a technical editor with Society of Petroleum Engineers Reservoir Engineering and Evaluation Journal for more than two years and is also serving to the same journal as the review chairperson.

Tina Yu:

Tina Yu is an Associate Professor of Computer Science at the Memorial University of Newfoundland in Canada. Tina received her Ph.D. from University of London, U.K in 1999. After her graduation, she joined Chevron Technology Company, working with petroleum engineers, geologists, geophysists and geostatisticans applying evolutionary computation techniques to oil business.

In October of 2005, she joined the Memorial University of Newfoundland as an Associate Professor. Meanwhile, she continues collaborating with Chevron in various research projects.

Tina Yu

Tina is active in the evolutionary computation community. Her publications include two edited books, five journal articles, five invited book chapters and 15 referred conference papers.

to top

Defense Applications of Computational Intelligence

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

The workshop invites completed or ongoing work in using computational intelligence techniques for addressing these or any other applications to defense related problems. This workshop is intended to encourage communication between active researchers and practitioners to better understand the current scope of efforts within this domain. The ultimate goal is to understand, discuss, and help set future directions for computational intelligence in defense problems.


Frank W. Moore

Frank W. Moore:

Frank has taught computer science, computer engineering, and electrical engineering courses at the undergraduate and graduate level for the past 11 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 three Visiting Faculty Research Program awards and over $200,000 in research funding from the Air Force, and has published over 50 journal articles, conference papers, and technical reports.

Laurence D. Merkle:

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 continues to serve as a reservist. 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. He has published over 50 conference papers and journal articles.

Steve Upton :

Steve has worked in the military domain for over 34 years, including serving on active duty in the US Marine Corps for 24 years. He has been applying evolutionary computation techniques to problems in this domain since 1996. His most recent work has been in using evolutionary algorithms to robustly optimize combat simulations and further developing the concept of automated red teaming and its application to problems in homeland defense and force protection.

Frank W. Moore
University of Alaska Anchorage
SSB 154 L, 3211 Providence Dr., Anchorage, AK 99508
PH: 907-786-4819
FAX: 907-786-6162

Laurence D. Merkle
Rose-Hulman Institute of Technology
5500 Wabash Ave., CM-103, Terre Haute, IN 47803
PH: 812-877-8474
FAX: 812-872-6060

Stephen C. Upton
Referentia Systems, Inc.
11435 Newington Ave., Spring Hill, FL 34609
PH: 352-684-1353
FAX: 808-423-1960

to top

Evolution of Natural and Artificial Systems - Metaphors and Analogies in Single and Multi-Objective Problems

(GECCO- ENAS- 2007)

The aim of this workshop is to understand the similarities and dissimilarities between biological evolution and computational evolution.

Work on evolutionary computation (EC) has made extensive use of concepts from biology such as the notion of "the fittest" or "optimal" solution to an evolutionary problem. Other concepts from biology such as mutation, speciation, and co-evolution, have also been used in work on EC. There are two complementary views about the utility of analogy between natural and artificial systems. The first view is that the study of natural systems can lead to the development of better artificial systems. The second view is that the study of artificial systems can lead to new insights on the evolution of natural systems. While the former view is commonly accepted, especially in the EC community, the latter view has yet to be fully accepted and explored. Design, planning and analyses of man-made systems often involve the use of Pareto-optimality and the notion of non-dominancy. When studying biological systems these are not easily apparent. Computational tools, such as Multi-objective Evolutionary Algorithms, may be particularly useful in attempts to understand the nature of evolutionary tradeoffs and the degree to which evolution involves a "balance" between selection for multiple objectives.


Understanding of applied metaphors and analogies in EC
New metaphors and analogies in EC
Dissimilarities between natural and artificial evolution
The adaptation/optimization debate and its relation to teleology and the notion of objectives
Tradeoffs in natural systems and how these arise
The relationships between niches, species formation and objectives in natural and artificial systems
The relationships between design concepts and "natural concepts"
Co-evolution and its relationship to multi-objective optimization
Other related topics and application areas (see a related comment in the CFP)


Amiram Moshaiov

Amiram (Ami) Moshaiov:

Ami is the founder and head of the robotics and mechatronics program of the School of Mechanical Engineering at Tel-Aviv University (TAU). Before joining the engineering faculty of TAU he has been an assistant professor in the Department of Ocean Engineering at MIT. He is a member of the Management Board of the European Network of Excellence in Robotics (EURON II), and of both the TC on Soft Computing and the TC on Education of the Int. Assoc. of Science & Technology for Development (IASTED). He has been the Program Chair of the 1^st Israel Conference on Robotics (ICR 2006), and a Co-chair of the IEEE/RSJ IROS- 2006 Workshop on Multi-Objective Robotics (IROS-MOR 2006).

Ami has been a member of the IPCs of many Int. Conf. on areas such as Mechatronics, Control, Cybernetics, IT, Engineering Design, Soft Computing & Education. He is also an expert reviewer for the Marie-Curie Research and Training Networks Program of the EU. Ami has worked and published on a wide variety of topics including Structural Mechanics, Ship Manufacturing Processes, Robotics, Bio-mechanics, MEMS, Computer Vision, Neural Networks, Engineering Design, Education, and EC. His current EC research focuses on defining and solving concept-based multi-objective problems including applications in areas such as engineering design, soft computing, and robotics.

Steven Orzack:

He is the President of the Fresh Pond Research Institute, a non-profit research institute located in Cambridge, MA. He received degrees from the University of Rochester and Harvard University. He worked at the University of Chicago prior to working at the Fresh Pond Research Institute. His recent research interests include bioinformatics, population dynamics, population genetics, comparative methods in evolutionary biology, evolution of life histories, and the history and philosophy of biology. With Elliott Sober he coedited Adaptationism and Optimality a collection of original papers on adaptationism, natural selection, and optimality (Cambridge University Press 2001).

Steven Orzack


Joshua Knowles

Joshua Knowles:

He is a research fellow in the School of Computer Science at the University of Manchester, UK. He works within the Manchester Interdisciplinary Biocentre on a range of projects involving evolutionary simulation and optimization in biological applications. In much of his research, both within and outside of biology, Joshua sees benefits in the exploration of tradeoffs through multiobjective optimization. Over the past 10 years, he has contributed to several aspects of the developing field of evolutionary multiobjective optimization, largely with David Corne, including: the development of the simple, elitist local search algorithm, PAES; memetic algorithms; archiving theory and algorithms; No-Free-Lunch; and performance assessment methods.

More recently, Joshua has investigated multiobjective instrument optimization and, with Julia Handl, multiobjective data clustering and feature selection. Last year, Joshua and David won the IEEE Transactions on Evolutionary Computation Outstanding Paper Published in 2003 Award for a paper on archiving algorithms in EMO.

to top

Medical Applications of Genetic and Evolutionary Computation


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 third year. They are also guest editors for a forthcoming 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 and EPSRC Peer Review College.

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 member of the Managing Board and secretary of the Italian Association for Artificial Intelligence (AI*IA). He has been chairman of EvoIASP since 1999.

Stefano Cagnoni

In 2001 and 2002 he has been 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. 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 and 2006, MedGEC, a 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 international journals: 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" (2006).

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 a large number of papers in recognized international journals and conferences.

FX-SBSE - Foundations and cross cutting issues in Search Based Software Engineering

FX-SBSE will focus on foundational and cross cutting issues, that applies across the spectrum of applications in SBSE research. The aim of the workshop will be to strengthen and deepen research in this area. Although there is an existing, established SBSE track at GECCO, no paper within the SBSE track can do this, so a workshop is required to draw these themes together. An indicative (but non exhaustive) list of theses include: scalability of results, robustness of results, feedback, insight, characterisation of landscapes and problem complexity, hybrid approaches to SBSE, multi objective approaches to SBSE, interactive SBSE, runtime SBSE.

User-centric Evolutionary Computation

Interactive evolutionary computing, in the main, relates to partial or complete human evaluation of the fitness of solutions generated from evolutionary search. This has been introduced where quantitative evaluation is difficult if not impossible to achieve. Such applications rely upon a human-centred, subjective evaluation of the fitness of a particular design, image, taste etc as opposed to an evaluation developed from some analytic model.

Partial human interaction that complements quantitative machine-based solution evaluation is also in evidence. For instance, the user addition of new constraints in order to generate viable solutions within evolutionary scheduling systems or the introduction of designer-generated solutions into selected evolving generations.

Solutions can also provide information which supports a better understanding of the problem domain whilst helping to identify best direction especially when operating within poorly defined problem spaces. This supports development of the problem representation in an iterative, interactive evolutionary design environment. Such human-centric approaches generate and succinctly present information re complex relationships between variables, objectives and constraints defining a decision space.

It is possible to view complete human evaluation as explicit and partial evaluation and interaction less explicit. Completely implicit interaction occurs where users are unaware of their role in the evolution of a system. A simple implicit/explicit spectrum of user-centric evolutionary approaches can thus be developed.

Short papers relating to user-centric evolutionary processes across this spectrum are invited. Authors will have an opportunity during the Workshop to present the main aspects of their research for discussion. The primary aim of the workshop is to provide a discussive forum as opposed to straighforward presentation of rigorous, results-oriented papers hence speculative short papers are welcome in addition to short results-oriented papers describing work in progress.

More details of the above with references to the examples can be found on the Workshop website (


Ian Parmee

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

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

to top

FD-ET: Future Directions in Evolutionary Testing

FD-ET will build on existing work that has taken place over 10 years of rapid development of Evolutionary Testing to map out a road map for future research in this area. It will draw on the themes identified in FX-SBSE, augmenting these with ET specific themes. An indicative (but non exhaustive) list of theses include: Non functional requirements, new testing applications, search space characterisation, fitness function improvement, landscape smoothing and size reduction, tailed evolutionary algorithms, test case selection and prioritization problems.


                        Genetic and Evolutionary Computation Conference (GECCO-2007)
 GECCO 2006 site      GECCO 2005 site         GECCO 2004 site    
GECCO 2003 site       GECCO 2002 site         GECCO 2001 site      GECCO 2000 site