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).

Biosketches:

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.

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.

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).

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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 http://www.epistasis.org/softgec2007.html.

Biosketch:

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).

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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.

Biosketches:

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.

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.

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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.

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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.

Biosketches:

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.

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.

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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: http://homepages.cwi.nl/~bosman/evodop2007/index.html

Biosketches:

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.

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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.

Biosketches:

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.

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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: 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.

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.

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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

Biosketches:

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.

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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:

http://groups.google.com/group/petroappworkshop07/web/petroappworkshop

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.

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.

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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.

Biosketches:

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.


Addresses:
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

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(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.

Topics:

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).

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.

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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.

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 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.

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 (www.ad-comtech.co.uk/Workshops.htm)

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 (www.ad-comtech.co.uk/ACDDM_Group.htm) 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.

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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.