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.

* 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: http://daci2008.cs.rose-hulman.edu.

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.

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.

Biosketches:

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.

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.

http://www.elec.york.ac.uk/intsys/events/MedGEC2008/home.htm

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, ...)

Biosketches:

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.

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.

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:
http://www.cs.ucl.ac.uk/financialcomputing/gecco_arcfec.htm

Biosketch:

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.

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: http://medal.cs.umsl.edu/obupm-2008/

Biosketches:

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.

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.

http://www.gustafsonresearch.com/gecco08_graduate_workshop/

Biosketch:

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.

Biosketches:

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.

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.