Workshops and Tutorials Schedule
1 - Evolutionary Computation Techniques for Constraint Handling
Carlos Artemio Coello Coello -
Dara Curran -
Thomas Jansen -
[ summary | details ]
2 - Fourteenth International Workshop on Learning Classifier Systems
Daniele Loiacono -
Albert Orriols-Puig -
Ryan Urbanowicz -
[ summary | details ]
3 - Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU)
Simon Harding -
W. B. Langdon -
Man Leung Wong -
Garnett Wilson -
Tony Lewis -
[ summary | details ]
4 - Ninth GECCO Undergraduate Student Workshop
Christian Gagné -
François-Michel De Rainville -
Félix-Antoine Fortin -
[ summary | details ]
5 - Medical Applications of Genetic and Evolutionary Computation (MedGEC)
Stephen L. Smith -
Stefano Cagnoni -
Robert Patton -
[ summary | details ]
6 - Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS) - Fifth Annual Workshop
William Rand -
Forrest Stonedahl -
[ summary | details ]
7 - 1st Workshop on Evolutionary Computation for Designing Generic Algorithms
Gisele L. Pappa -
Alex A. Freitas -
Jerry Swan -
John Woodward -
[ summary | details ]
8 - 2nd GECCO Workshop on Visualization Methods for Genetic and Evolutionary Computation (VizGEC)
Jason H. Moore -
[ summary | details ]
9 - Bio-Inspired Solutions for Wireless Sensor Networks (GECCO BIS-WSN 2011)
Maria J. Blesa -
Christian Blum -
[ summary | details ]
10- 3rd Symbolic Regression and Modeling Workshop for GECCO 2011
Steven Gustafson -
Ekaterina Vladislavleva -
[ summary | details ]
11 - Optimization by Building and Using Probabilistic Models (OBUPM-2011)
Mark Hauschild -
Martin Pelikan -
[ summary | details ]
12 - Scaling Behaviours of Landscapes, Parameters and Algorithms
Ender Özcan -
Andrew J. Parkes -
Jonathan Rowe -
[ summary | details ]
13 - GreenIT Evolutionary Computation
Pascal Bouvry -
Samee U. Khan -
Gregoire Danoy -
Alexandru-Adrian Tantar -
Emilia Tantar -
Bernabe Dorronsoro -
[ summary | details ]
14 - Graduate Students Workshop
Miguel Nicolau -
[ summary | details ]
|1 - Evolutionary Computation Techniques for Constraint Handling
Evolutionary algorithms are often applied to difficult optimization
problems where candidate solutions are subject to various constraints.
There is a large body of practical guidelines and theoretical results
about evolutionary algorithms in the context of constrained
optimization. Closely related is the subject of constraint programming
where some solution satisfying all constraints is sought. In both
contexts evolutionary computation techniques can be and are applied to
solve hard problems. The workshop intends to provide a forum for current
research in these areas. Algorithmic techniques, case studies,
theoretical and empirical analyses are all welcome. While focusing on
handling constraints we intend to provide a forum for discussion,
therefore allowing generous time slots for each presentation and ample
time for discussions.
Carlos Artemio Coello Coello
He received a BSc in Civil Engineering from the Universidad
Autonoma de Chiapas in Mexico in 1991 (graduating Summa Cum Laude). Then, he was
awarded a scholarship from the Mexican government to pursue graduate studies in
Computer Science at Tulane University (in the USA). He received a MSc and a PhD in
Computer Science in 1993 and 1996, respectively. Dr. Coello has been a Senior Research
Fellow in the Plymouth Engineering Design Centre (in England) and a Visiting Professor
at DePauw University (in the USA). He is currently full professor at CINVESTAV-IPN in
Mexico City, Mexico. He has published over 250 papers in international peer-reviewed
journals, book chapters, and conferences. He has also co-authored the book
"Evolutionary Algorithms for Solving Multi-Objective Problems", which is now in its
Second Edition (Springer, 2007) and has co-edited the book "Applications of Multi-
Objective Evolutionary Algorithms" (World Scientific, 2004). He has delivered invited
talks, keynote speeches andtutorials at international conferences held in Spain, USA,
Canada, Switzerland, UK, Chile, Colombia, Brazil,Argentina, India, Uruguay and Mexico.
Dr. Coello has served as a technical reviewer for over50 international journals and for
more than 60 international conferences and actually serves as associate editor of the
journals "IEEE Transactions on Evolutionary Computation", "Evolutionary Computation",
"Computational Optimization and Applications", "Pattern Analysis and Applications" and
the "Journal of Heuristics". He is also a member of the editorial boards of the
journals "Soft Computing", "Engineering Optimization", the "Journal of Memetic
Computing" and the "International Journal of Computational Intelligence Research". He
received the 2007 National Research Award (granted by the Mexican Academy of Science)
in the area of "exact sciences". He is member of the Mexican Academy of Science, Senior
Member of the IEEE, and member of Sigma Xi, The Scientific Research Society. His
current research interests are: evolutionary multiobjective optimization and
constraint-handling techniques for evolutionary algorithms.
He received a BSc. in Information Technology from the National University of
Ireland, Galway in 2000. After spending some time in industry, he was awarded an Irish
Research Council for Research Engineering and Technology scholarship to pursue a PhD,
which he obtained from the National University of Ireland, Galway in 2006. He is
currently an Enterprise Ireland Principal Investigator. Prior to this he was a staff
scientist at the Cork Constraint Computation Centre, an Enterprise Ireland Principal
Investigator and an Irish Research Council for Research, Engineering and Technology
Post-doctoral fellow. He has published over 35 research papers and co-organised the
first workshop on Organisation, Cooperation and Emergence in Social Learning Agents at
ECAL 2009. His research is focused on the application of learning models to
evolutionary algorithms and the use of evolutionary computation for Artificial Life
Born 1969, studied Computer Science at the University of Dortmund,
Germany. He received his diploma (1996, summa cum laude) and Ph.D. (2000, summa cum
laude) there. From September 2001 to August 2002 he stayed as a Post-Doc at Kenneth De
Jong's EClab at the George Mason University in Fairfax, VA. He was Juniorprofessor for
Computational Intelligence from September 2002 to February 2009 at the Technical
University Dortmund. Since March 2009 he is Stokes Lecturer at the Department of
Computer Science at the University College Cork, Ireland. His research is centered
around design and theoretical analysis of evolutionary algorithms and other randomized
search heuristics. He has published 16 journal papers, 34 conference papers and
contributed seven book chapters. He is associate editor of Evolutionary Computation
(MIT Press), member of the steering committee of the Theory of Randomized Search
Heuristics workshop series, was program chair at PPSN 2008, co-organized FOGA 2009, a
workshop on Bridging Theory and Practice (PPSN 2008), two Dagstuhl workshops on Theory
of Evolutionary Computation (2004 and 2006) and one Dagstuhl workshop on Artificial
Immune Systems (2011).
|2 - Fourteenth International Workshop on Learning Classifier Systems
Since Learning Classifier Systems (LCSs) were introduced by Holland (1976, 1978) with the aim of creating cognitive systems which used evolutionary computation to learn to perform a certain task by interacting with its environment, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. LCSs are a very active area of research, with interesting, newer approaches that have shown not only to be competitive with respect to state-of-the-art machine learning techniques, but also to be very flexible approaches capable of solving a wide variety of real-world problems that range from data mining to automated innovation and online control. Among the many different approaches, XCS (Wilson, 1995) has recently received a special amount of attention due to its ability to solve problems that previously eluded solution.
The current edition will be the 14th edition of the workshop, which was initiated in 1992, held at the NASA Johnson Space Center in Houston, Texas. Since 1999 the workshop has been held yearly in conjunction with PPSN in 2000 and 2002 and with GECCO in 1999, 2001 and from 2003 to 2010.
In general, the workshop aims at discussing any advances in the LCS and evolutionary learners fields. Topics of interests include but are not limited to:
* Paradigms of LCS (Michigan, Pittsburgh, ...)
* Theoretical developments (behavior, scalability and learning bounds, ...)
* Representations (binary, real-valued, oblique, non-linear, fuzzy, ...)
* Types of target problems (single-step, multiple-step, regression/function approximation, ...)
* System enhancements (competent operators, problem structure identification and linkage learning, ...)
* LCS for Cognitive Control (architectures, emergent behaviors, ...)
* Applications (data mining, medical domains, bioinformatics, intelligence in games ...)
The format of the workshop will be set to encourage discussion. After a brief welcome, there will be presentations of the accepted papers followed by a discussion on the presented topic. At the end of the workshop, we will reserve some time to have a round table where we can brainstorm and discuss any LCS topic.
Graduated cum laude in 2004 in Computer Engineering at Politecnico di Milano. In 2008 he received the Ph.D. in Computer Engineering from the Department of Electronics and Information of Politecnico di Milano, where he is currently a Post-doctoral researcher. His research interests include machine learning, evolutionary computation, and computational intelligence in games.
He has been in the program committee of the ACM Genetic and Evolutionary Computation Conference (GECCO), the IEEE Congress on Evolutionary Computation (CEC), the IEEE Symposium on Computational Intelligence and Games (CIG), the International Workshop on Learning Classifier Systems (IWLCS), and the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). He also reviewed articles for the following journals: IEEE Transactions on Evolutionary Computation, Evolutionary Computation Journal, IEEE Transaction on Computational Intelligence and AI in Games, Genetic Programming and Evolvable Machines.
Since 2008, Daniele Loiacono has been organizing several scientific competitions at major conferences including GECCO, CEC and CIG. In 2009 he was local co-chair of the IEEE Symposium on Computational Intelligence and Games and co-organized the special session on Computational Intelligence in Games at the IEEE Congress on Evolutionary Computation. In 2010 he was in the organizing committee of the special session on Racing Games at the IEEE Symposium on Computational Intelligence. He has 38 refereed international publications between journal papers, conference papers, book chapters, and workshop papers.
He received the M.Sc. and Ph.D. degrees in computer engineering in 2004 and 2008, respectively, from the Ramon Llull University, Spain. His thesis studied how the extended classifier system (XCS), one of the most influential LCS, could deal with domains that contained class imbalances. During his PhD, he was a visiting research fellow at the Illinois Genetic Algorithm Laboratory (University of Illinois at Urbana-Champaign) and at the Soft Computing and Intelligent Information Systems Research Group (University of Granada). In 2009, he was appointed as an assistant professor at the Ramon Llull University.
He is currently a software engineer at Google. His research interests include online evolutionary learning, fuzzy modeling, learning from rarities, data complexity, and machine learning in general. He is especially interested in the application of genetic-based machine learning to real-world problems in the field of supervised and unsupervised learning. He has been in the program committee, among other conferences and workshops, of the Genetic and Evolutionary Computation Conference (GECCO), the International Workshop on Learning Classifier Systems (IWLCS), the Hybrid Artificial Intelligence Systems (HAIS), and the Hybrid Intelligent Systems (HIS). He also serves as a reviewer for, among others, the following journals: IEEE Transactions on Evolutionary Computation, Evolutionary Computation Journal, Soft Computing, Evolutionary Intelligence, and Pattern Recognition Letters. He has 43 refereed international publications between journal papers, conference papers and book chapters, and he has given 3 invited talks.
He received his B. Eng. Degree in Agricultural and Biological Engineering from Cornell University in 2004 and a M. Eng. Degree from the same institution in 2005. His masters thesis explored a ganglioside-liposome biosensor design for the detection of botulinum and cholera toxins. In 2005 he entered the Molecular and Cellular Biology (MCB) Ph.D. program at Dartmouth College and joined a lab specializing in Compuational Biology under mentor/PI Jason Moore. In 2009 he was awarded a Dartmouth Neukom Institute Fellowship funding the development of a learning classifier system algorithm for the detection of complex multifactorial genetic associations predictive of disease. His Ph.D. thesis deals specifically with two complicating phenomena which impede the ability to detect genetic associations in common complex diseases; epistasis and genetic heterogeneity. Completion of his Ph.D thesis is anticipated to occur in early 2011. His research interests include the development of learning classifier systems (LCSs) and other kinds of evolutionary learning for application to problems in genetic epidemiology. More generally, his interests extend to genetics, epidemiology, bioinformatics, artificial intelligence, data mining, and evolutionary algorithms. He has chaired for the Bioinformatics and Computational Biology track at the Genetic and Evolutionary Computation Conference (GECCO), and has given 2 invited talks. He has 5 refereed international publications including an extensive review of LCSs, and two other LCS based works, one of which received best paper at 2010 GECCO in the Bioinformatics and Computational Biology track.
|3 - Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU)
CIGPU 2011 is the fourth workshop on the use of GPUs, games consoles and other consumer hardware for evolutionary algorithms and other computational intelligence techniques.
Due to its speed, price, and availability, there is increasing interest in using mass consumer market commodity hardware for engineering and scientific applications. Mostly this has concentrated upon graphics hardware, particularly GPUs, due to their ability to offer teraflop performance on a desktop using a restricted form of parallel computing. There is also increasing interest in using the computing power of game consoles, the Cell processor, and portable entertainment and/or cellular phone mobile devices for research and applications.
Submissions are invited in (but not limited to) the following areas:
- Parallel genetic programming (GP) on GPU
- Parallel genetic algorithms (GA) on GPU
- Parallel evolutionary programming (EP) on GPU
- Associated or hybrid techniques on GPU
o Particle Swarm Optimisation (PSO)
o Ant colony Optimisation (ACO)
o Support Vector Machines
o Bayesian Networks
o Parallel search algorithms
o Cellular automata (CA)
o Evolutionary strategies (ES)
o Data mining
- Differential Evolution on GPU
- Computational Biology or Bioinformatics on GPU
- Evolutionary computation on video game platforms
- Evolutionary computation on mobile devices
Papers that discuss novel implementations and the practicalities of writing software for these hardware platforms are especially welcome.
The workshop will be held in conjunction with the tutorial Accelerating Evolutionary Computation with Graphics Processing Units, and a GECCO 2011 competition on GPUs for Genetic and Evolutionary Computation.
He was awarded a PhD in Electronic Engineering from the
University of York, UK in 2006. He has published widely in computational
intelligence, unconventional computing, genetic programming and artificial
developmental systems. He is currently a researcher at Memorial University,
Canada. Dr Harding previously co-organised CIGPU 2008, 2009 and 2010. He has
several publications on GPU programming, including the first paper describing
general purpose genetic programming on GPUs. Dr. Harding also administers the
gpgpgpu.com web page.
W. B. Langdon
He was awarded a PhD in Computer Science by University College,
London (UCL) more than ten years ago. He has more than 100 papers, including
writing three books. He is the resource review editor for Genetic Programming
and Evolvable Machines and a member of the editorial board of Evolutionary
Computation. He has given nine tutorials and organised workshops in
international conferences and has chaired both tracks and been editor-in-chief
for the GECCO proceedings. He co-chaired the EuroGP conference three times.
Man Leung Wong
He is an associate professor at the Department of Computing
and Decision Sciences at Lingnan University, Tuen Mun, Hong Kong. His research
interests are evolutionary computation, data mining, parallel algorithms on
Graphics Processing Units, machine learning, knowledge acquisition, fuzzy
logic, and approximate reasoning. His articles on these topics have been
published in Evolutionary Computation, IEEE Transactions on Evolutionary
Computation, IEEE Transactions on Pattern Analysis and Machine Intelligence,
IEEE Transactions on Systems, Man, and Cybernetic, IEEE Intelligent Systems,
IEEE Engineering in Medicine and Biology, Management Science, Decision Support
Systems, Expert Systems with Applications, Journal of the American Society for
Information Science and Technology, Fuzzy Sets and Systems, International
Journal of Approximate Reasoning, etc. He received his B.Sc., M.Phil., and
Ph.D. in computer science from the Chinese University of Hong Kong in 1988,
1990, and 1995, respectively.
He was awarded his PhD in Computer Science from Dalhousie
University, Canada in 2007. He has published in the areas of financial
analysis, linear genetic programming, co-evolutionary algorithms, artificial
developmental systems, information visualization, and GPU programming for
evolutionary computation. Dr. Wilson developed a genetic programming, and
general-purpose computing on graphics processing units (GPGPU), implementation
on a commercial video game system (using the XBox 360). He is currently a
postdoctoral fellow with Memorial University of Newfoundland, where his
research focuses on information visualization and evolutionary computation
applied to fisheries conservation. He is also currently President and CTO of
Afinin Labs, Inc., a company using evolutionary computation to create tools
for financial analysis.
He has a background of a MMath from Durham and several years'
experience as a software engineer for IBM. He moved into researching
computational intelligence via an MSc and his prize winning thesis focussed on
applying GP to scheduling. He is currently working at Birkbeck, University of
London and is researching accelerating GP with GPUs and using the powerful
approach to exploring long term fitness growth. His work on tackling GP with
GPUs recently earned a GECCO best paper award. He has reviewed papers for
conferences and journals and he joint-organised the CIGPU special session at
IEEE WCCI in 2010.
|4 - Ninth GECCO Undergraduate Student Workshop
The ninth annual Undergraduate Student Workshop will occur on Tuesday,
July 12, 2011. This half-day workshop will provide an excellent
opportunity for undergraduate students to present a senior research
project, summer project, or exceptional course project involving
evolutionary computation, and receive valuable feedback from an
international panel of experts. 6-page papers are due March 23; for
additional details, please see:
He is assistant professor of computer engineering at Université Laval in Québec (Canada) since 2008, and member of the Computer Vision and Systems Laboratory. He received is B.Ing. (computer engineering), M.Sc. (electrical engineering) and Ph.D. (electrical engineering) from Université Laval in 2000, 2003 and 2005, respectively. In the 2005-2006 period, he was ERCIM postdoctoral fellow jointly at the INRIA Saclay-Ile-de-France in Orsay (France) and the University of Lausanne (Switzerland). He also worked as research analyst for Informatique WGZ (2006-2007) and MacDonald, Dettwiler and Associates (2007), as consultant on research projects with Defence R&D Canada -- Valcartier. His research interests are on the engineering of distributed intelligent systems, in particular systems involving evolutionary computation (genetic programming, LCS, co-evolution), machine learning (reinforcement learning, ensemble methods, pattern recognition), and distributed computing (sensor networks, autonomic computing, high-performance computing). Prof. Gagné was local chair of GECCO 2009, competition chair at GECCO 2010, and is chair of the Digital Entertainment Technology and Art track at GECCO 2011. He is also the main author of Open BEAGLE, a C++ evolutionary computation framework.
François-Michel De Rainville
He received in 2010 his Master's degree from Université Laval, Québec (Canada), for his work on interactive designs of experiments to enhance comprehension of complex systems. He is currently pursuing his PhD degree at Université Laval on using a swarm of robots to explore and analyze unknown environments. His major area of interest are robotics, pattern recognition, machine learning, evolutionary algorithms, computer vision, and optimization. His major research contributions are on the optimization of configurations of quasi-random number generators with evolutionary algorithms, and DEAP, a distributed evolutionary computation framework in Python.
He is a PhD student at the department of electrical and computer engineering of Université Laval, in Québec (Canada), and a research assistant at CLUMEQ, a research consortium for high-performance computing. He recently completed with a Master's with honours, investigating the usage of genetic algorithms for the automatic placement of surveillance cameras in arbitrary environments. His research interests include evolutionary algorithms, incremental learning, and high-performance computing. He is also the co-author of a new framework called DEAP, for the rapid prototyping of distributed evolutionary algorithms in Python.
|5 - Medical Applications of Genetic and Evolutionary Computation (MedGEC)
MedGEC 2011 is the seventh GECCO Workshop on the application of genetic and evolutionary computation (GEC) to problems in medicine and healthcare.
A dedicated workshop at GECCO provides a 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.
Subjects will include (but are not limited to) applications of GEC to:
- Medical imaging
- Medical signal processing
- Medical text analysis
- Clinical diagnosis and therapy
- Data mining medical data and records
- Clinical expert systems
- Modelling and simulation of medical processes
- Drug description analysis
- Genomic-based clinical studies
- Patient-centric care
A session of the workshop will be dedicated to the presentation of large transnational
projects in which GEC plays a significant role
and of other opportunities for young scientists
to be involved in this field. In particular,
a short presentation of the EU-funded project
MIBISOC ("Medical Imaging using Bio-Inspired and Soft Computing", funded within the Marie-Curie Initial Training Network Action FP7 PEOPLE-ITN-2008),
which funds 16 PhDs in laboratories form 5
European countries will be given.
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. Most recently he was
tutorial chair for the IEEE Congress on Evolutionary Computation (CEC)
in 2009 and local organiser for the International Conference on Evolvable Systems (ICES) in 2010.
Steve and Stefano Cagnoni are co-founders and organizers of the MedGEC
Workshop, which is now in its seventh year. They are also guest editors
for a special issue of Genetic Programming and Evolvable Machines
(Springer) on medical applications and editors of a forthcoming book on the subject (John Wiley, November 2010).
Steve is associate editor for the journals Genetic Programming and Evolvable Machines and the Journal of Artificial Evolution and Applications. Hes is also a member of the editorial board for the International Journal of Computers in Healthcare.
Steve has some 75 refereed publications, is a Chartered Engineer and a
fellow of the British Computer Society.
Graduated in Electronic Engineering at the University of Florence in 1988 where he has been a PhD student 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. Since 1997 he has been with the University of Parma, where he has been Associate Professor since 2004.
His main basic research interests concern soft computing, with particular regard to evolutionary computation, and computer vision. As concerns applied research, the main topics of his research are the application of the above-mentioned techniques to problems in computer vision, pattern recognition and robotics.
Recent research grants regard: co-management of a project funded by Italian Railway
Network Society (RFI) aimed at developing
an automatic inspection system for train
pantographs; a "Marie Curie" grant, for a four-year research training project in Medical Imaging using Bio-Inspired and Soft Computing; a grant from "Compagnia di S. Paolo" on "Bioinformatic and experimental dissection of the signalling pathways underlying dendritic spine function".
He is Editor-in-chief of the "Journal of Artificial Evolution and Applications"
Since 1999, he has been chairman of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing.
Since 2005, he has co-chaired MedGEC, workshop on medical applications of evolutionary computation at GECCO.
Co-editor of special issues of journals
dedicated to Evolutionary Computation for Image Analysis and Signal Processing.
He has been reviewer for international journals and member of the committees of several conferences.
He is member of the Advisory Board of Perada, the UE Coordination Action on Pervasive Adaptation.
He has been recently awarded the "Evostar 2009 Award", in recognition of the most outstanding contribution to Evolutionary Computation.
Robert M. Patton
Dr. Patton received his Ph.D. in Computer Engineering with emphasis on Software Engineering from the University of Central Florida in 2002. In 2003, he joined the Applied Software Engineering Research group of Oak Ridge National Laboratory as a researcher. Dr. Patton primary research interests include data and event analytics, intelligent agents, computational intelligence, and nature-inspired computing. He currently is investigating novel approaches of evolutionary computation to the analysis of mammograms, abdominal aortic aneurysms, and traumatic brain injuries. In 2005, he served as a member of the organizing committee for the workshop on Ambient Intelligence - Agents for Ubiquitous Environments in conjunction with the 2005 Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005).
|6- Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS) - Fifth Annual Workshop
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.
- 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 is an assistant professor of Marketing, Decision, Operations, and Information Technology, and Computer Science at the University of Maryland, where he also serves as the Director of Research for the Center for
Complexity in Business. 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. Bill is co-authoring a textbook with Uri Wilensky at Northwestern University that is the a hands-on introduction to agent-based modeling.
He is an advanced graduate student at Northwestern University, completing his dissertation work on the use of evolutionary algorithms to explore the effects of varying parameters in multi-agent simulations. He has published on this topic at venues such as GECCO, AAMAS, and the AAAI fall symposium, and has also authored an open-source software package for aiding in this task. He is also an active member of the Center for Connected Learning and Computer-Based Modeling at Northwestern University contributing to the development of the NetLogo multi-agent modeling language and environment.
|7 - 1st Workshop on Evolutionary Computation for Designing Generic Algorithms
The main objective of this workshop is to discuss evolutionary
computation methods for generating generic algorithms and/or
heuristics. These methods have the advantage of producing
solutions that are applicable to any instance of a problem domain, instead of
a solution specifically produced to a single instance of the problem.
The areas of application of these methods may include, for instance, data
mining, machine learning, optimization, bioinformatics, image processing, economics, etc.
The range of approaches which will be outlined can be used to evolve algorithms from scratch (i.e. from a
basic set of computationally simple instructions). But they can
also be used to assist or supplement currently existing
specialized heuristics/algorithms, and thus should appeal to researchers
with an interest in a particular real world application which
need further improvement via “algorithmic tuning”.
The motivation to investigate these approaches is that, although most
of the evolutionary computation techniques are designed to generate
specific solutions to a given instance of a problem, some of these
techniques can be explored to solve more generic problems. For
instance, while there are many examples of evolutionary algorithms for
evolving classification models in data mining or machine learning,
genetic programming was already used to create a generic
classification algorithm which will, in turn, generate a specific
classification model for any given classification dataset, in any
given application domain.
This type of methods are also becoming common in the area of optimization, where
they are named as hyper-heuristics.
Hyper-heuristics are search methods that automatically select
and combine simpler heuristics, creating a generic
heuristic that is used to solve any instance of a given target
type of optimization problem. Hence, hyper-heuristics search in
the space of heuristics, instead of searching in the problem
solution space, raising the level of generality of the
solutions produced by the hyper-heuristic evolutionary
Gisele L. Pappa
Dr. Pappa received her PhD in Computer Science from the
University of Kent, Canterbury, UK, in 2007. She is currently
an Associate Professor at the Federal University of Minas Gerais,
Brazil. She is the author of a research-oriented book on data
mining and evolutionary algorithms, and her current research
interests are on data mining, bio-inspired computational
intelligence algorithms and social networks.
He obtained his PhD in Computer Science from the University of Essex, UK, in 1997.
He is currently a Reader in Computational Intelligence (position equivalent to Associate Professor)
at the University of Kent, UK. He has (co)-authored 3 research-oriented books and
over 130 peer-reviewed papers in journals and conferences. He is a member of the editorial
board of four international journals, and has (co-)organized several conferences and workshops
in the areas of evolutionary algorithms, swarm intelligence and data mining.
His current research interests are data mining and knowledge discovery,
biologically-inspired computational intelligence algorithms, bioinformatics and the biology of ageing.
Prior to obtaining a PhD in computational group theory at Nottingham, Jerry has spent 20 years in industry as a software developer. He was the owner of a computer games company for most of the 1990s and has worked in areas as diverse as logistics and generative music. His research interests include hyper-heuristics, symbolic computation and machine learning. He now works at the Automated Scheduling and Planning (ASAP) Research Group at Nottingham.
He has a BSc in Theoretical Physics, an MSc in Cognitive Science distinction) and a PhD in Computer Science, all from the University of Birmingham.
He recently completed a post-doc at the Nottingham of University investigating the use of Genetic Programming to discover novel heuristics.
In addition he has worked at CERN doing research into Particle Physics, the Royal Air Force as an environmental noise scientist, and Electronic Data Systems as a systems engineer.
Currently he is teaching at the University of Nottingham, Ningbo, campus based in China, and a member of the Automated Scheduling, Optimization and Planning Research Group at the University of Nottingham.
His research interests include fundamental issues in Machine Learning especially Genetic Programming.
|8 - 2nd GECCO Workshop on Visualization Methods for Genetic and Evolutionary Computation (VizGEC)
The second annual workshop on Visualization Methods for Genetic and Evolutionary Computation (VizGEC) is intended to explore and critically evaluate the development and application of visualization methods for enhancing the theoretical development of GEC and the application of GEC to real world problems. Visualization methods have an important role to play in understanding theoretical issues such as population structure, diversity and change over time as well as applied issues such as results interpretation and communication in a particular domain. Scientific visualization, information visualization and visual analytics will be discussed.
Jason H. Moore
He has more than 12 years of experience in developing and applying GEC methods for biological and biomedical problems. He has published more than 50 peer-reviewed papers on GEC methods including more than eight papers as part of GECCO. Dr. Moore is funded by the USA National Institutes of Health (NIH) to develop scientific visualization approaches for GEC (NIH R01 LM009012). Dr. Moore organized and chaired the 1st and 2nd SoftGEC workshop at GECCO 2007 and 2008 that focused on open-source software. Both of these workshops were very well attended (n>25) and accomplished to goal of discussing the challenges of developing user-friendly software in the GEC community. Last year, he organized the 1st VizGEC workshop at GECCO 2010 in Portland that was very well attended (n>30). In the past, he has organized and chaired the successful BioGEC workshop at GECCO 2004 and 2005 that focused on biological applications of GEC. There were more than 30 participants at each and the post-workshop reviews were excellent. Dr. Moore has chaired other GEC workshops and sessions including the session on 'Microarray Analysis' at the 1st European Workshop on Evolutionary Computing and Bioinformatics (EvoBIO 2003) and the session on 'Real World Applications in Bioinformatics' at GECCO-2003. Further, Dr. Moore has served on program and technical committees for the European Conference on Evolutionary Computing, Data Mining and Machine Learning in Bioinformatics (EvoBIO 2002-2010), the workshop on 'Grammatical Evolution' at GECCO (2003-2008), GECCO-2003, GECCO-2004, GECCO 2005, GECCO-2006, GECCO-2007 and the 2005 and 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Dr. Moore chaired the Bioinformatics Track at GECCO 2006, 2007 and 2008. Dr. Moore chaired in 2007 and 2008 (with Drs. Carlos Cotta, Elena Marchiori, and David Corne) the European Conference on Evolutionary Computation, Data Mining and Machine Learning in Bioinformatics (EvoBIO). Dr. Moore has also given the GECCO 2005, 2006, 2007, 2008, 2009 and 2010 tutorials on Bioinformatics. Thus, Dr. Moore has been intimately involved in GECCO and other GEC conferences for the last nine years. He will serve as the General Chair of GECCO 2012.
|9 - Bio-Inspired Solutions for Wireless Sensor Networks (GECCO BIS-WSN 2011)
Wireless sensor networks consist of spatially distributed autonomous computing units that cooperatively monitor a variable property, for example, environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants. They are being used in many application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation, and traffic control.
Apart from begin equipped with different sensors, each node in a sensor network is generally equipped with a very limited battery, a rather small memory device, a small hard disk, a simple processing unit, and a radio transceiver or another alternative device for wireless communication. Communication via these devices results in the fact that a sensor network is a wireless ad-hoc network. Each sensor node usually supports multi-hop routing algorithms where nodes function as forwarders, relaying data packets to a base station. Those communication capabilities, together with their very reduced computing and storage capabilities, poses a new challenge for computer scientists: to use sensor networks not only for monitoring, but also for computing. Some of the algorithmic issues that must be addressed in sensor networks concern, for example, event detection, data gathering, object tracking, base station initiated querying, power saving, etc.
The mentioned particularities of the computing units in sensor networks, together with their growing size, ask for a new computing paradigm. Clearly, conventional engineering paradigms seem not to be very well suited for their control and management. The fact that a complex sensor network is composed of simple computing units has an analogy with certain animal societies, whose individuals are often very simple but together they result in a much more complex and capable entity. Thus, from an algorithmic point of view, bio-inspired solutions, such as swarm intelligence techniques, artificial immune systems, or evelutionary algorithms may provide valuable alternatives for solving problems in sensor networks. Genetic and evolutionary algorithms, for example, may be used to solve large-scale optimization problems occuring in sensor networks. On the other side, self-organization may help in distributed controll and management tasks.
For this workshop, original, and so-far unpublished, contributions from the following topic areas are solicited:
* All contributions must deal with algorithmic aspects in wireless sensor networks
* Applications of genetic and evolutionary computation principles
* Use of swarm intelligence principles such as self-organization
* Other bio-inspired computing paradigms such as artificial immune systems
Maria J. Blesa
She received her degree in Informatics Engineering in 2000 and her Ph.D. in Computer Science in 2006, both from the Universitat Politecnica de Catalunya (UPC) in Spain, where nowadays she is associate professor. Her Ph.D. thesis got a special Prize as best thesis defended during the course 2005-2006 in the area of Information Technologies and Communications at UPC. Her research has always been supported by European projects and official grants. Among them, she is currently participating in the European projects FRONTS (fronts.cti.gr) and WISEBED (wisebed.eu), whose research topics involve different theoretical and practical aspects of sensor networks. Her publications include three book chapters, ten journal articles and twenty-three articles in international conferences. She has also edited six LNCS proceedings and a book on Hybrid Metaheuristics. She is currently writting a book about stability in networks, to be published by Springer. She has been part of the organization committee and program committee of several international conferences, workshops and meetings.
He received the doctoral degree in 2004 from the Free University of Brussels (Belgium). After spending one year at the Advanced Computation Laboratory of the Imperial Cancer Research Fund (ICRF) in London, he worked at IRIDIA at the Free University of Brussels under the supervision of Marco Dorigo. He currently is a "Ramon y Cajal" tenure-track research fellow at the ALBCOM research group of the Universitat Politecnica de Catalunya in Barcelona. Subject of his research is the use of swarm intelligence techniques for the management of large-scale mobile ad-hoc and sensor networks, as well as the hybridization of metaheuristics with more classical artificial intelligence and operations research methods. He is a member of the editorial boards of international journals such as Computers & Operations Research and Swarm Intelligence. As a co-founder, he initiated the series of International Workshops on Hybrid Metaheuristics (HM). Moreover, he gave invited tutorials at conferences such as PPSN, GECCO, and HIS. Concerning his publication record, Christian has published around 100 papers in international journals, conference proceedings, or as book chapters.
Finally, Christian Blum has a rather long history of involvement in GECCO conferences. Apart from chairing the "late breaking papers" track at this upcoming GECCO 2011, in the past Christian has chaired different regular GECCO tracks such as "Ant Colony Optimization and Swarm Intelligence (ACSI)" and "Combinatorial Optimization and Metaheuristics (COM)".
|10- 3rd Symbolic Regression and Modeling Workshop for GECCO 2011
Symbolic Modeling is used to designate the search for symbolic descriptions,
usually in the language of mathematics, to describe and predict numerical
data in diverse fields such as industry, economics, finance and science.
Symbolic modeling captures the field of symbolic regression: a genetic
programming based search technique for finding symbolic formulae on
numerical data in order to obtain an accurate and concise description of
that data in symbolic, mathematical form. In the evolutionary computation
field it also captures learning classifier systems, if and when they are
applied to obtain specific interpretable results in the field of interest.
Symbolic modeling can be defined as a set of techniques (including, but not
limited to symbolic regression and learning classifier systems) and
representations that try to find a mathematical description and prediction
in some numerical space. This can be contrasted with numerical modeling such
as (generalized) linear regression, neural networks, kernel regression and
support vector machines.
The key discriminator of producing symbolic results over numerical results
is the ability to interpret and analyze the results, leading either to
acceptance by field experts, or to heightened understanding of the theory in
the field of application. Interpretation is key, and the workshop will focus
heavily on this.
The workshop will focus on advances in using symbolic modeling for real
world problems in industry, economics, finance and science. Papers are
sought that contribute to the state of the art in symbolic modeling, either
through innovative applications, theoretical work on issues of
generalization, size and comprehensibility of the results produced, and
algorithmic improvements to make the techniques faster, more reliable and
generally better controlled.
He is a computer scientist at the General Electric Global
Research Center in Niskayuna, New York. As a member of the Computational
Intelligence Lab, he develops and applies advanced AI and machine learning
algorithms for complex problem solving. He received his PhD in computer
science from the University of Nottingham, UK, where he was a research
fellow in the Automated Scheduling, Optimisation and Planning Research
Group. He received his BS and MS in computer science from Kansas State
University, where he was a research assistant in the
Knowledge Discovery in Databases Laboratory. Dr. Gustafson is a
member of several program committees, a member of the editorial board of the
Journal of Artificial Evolution and Applications, and a Technical
Editor-in-Chief of the new journal Memetic Computing. In 2006, he received
the IEEE Intelligent System's "AI's 10 to Watch" award.
Graduated in mathematical theory of intelligent
systems from the Faculty of Mathematics and Mechanics, Moscow State
Lomonosov, Moscow, Russia. She received the M.Sc. degree in mathematics in
2000 and the P.D.Eng. degree in industrial mathematics from Eindhoven
University of Technology, Eindhoven, The Netherlands, in 2005. She also
received the Ph.D. degree in symbolic regression from Tilburg University,
Tilburg, The Netherlands, in 2008. Currently, she is a Lecturer promoting
scientific computing in the Department of Mathematics and Computer Science,
University of Antwerp, Antwerp, Belgium. Her research interests include
data-driven modeling and high-performance computing, particularly in the
industrial scale symbolic regression.
|11 - Optimization by Building and Using Probabilistic Models (OBUPM-2011)
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.
He is a Ph.D. 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.
He received his 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. 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.
|12 - Scaling Behaviours of Landscapes, Parameters and Algorithms
All too often heuristics and meta-heuristics require significant parameter tuning to work most effectively.
Often this tuning is performed without any a priori knowledge as to how good values of parameters might
depend on features of the problem. This lack of knowledge can lead to lot of computational effort and
also has the danger of being limited to only problem instances that are similar to those that have been seen
before. The aim of the workshop is to develop methods to give deeper insight into problem classes, and
how to obtain and exploit structural information. In particular, we often would like to be able to tune
parameters using small instances (for speed) but then adjust so as to be able to run on large instances.
This will require some theory of how to extrapolate tuning outside of the size or features of the training set.
An analogy is the difference between non-parametric and parametric statistics; the former does not
assume any underlying probability distribution and the latter can (for example) assume a Gaussian.
Naturally, the latter might give stronger results and with smaller sized samples. Hence, to distinguish this
from standard parameter tuning, we might call this "Parametric Parameter Tuning". Of course, this is a
challenging problem; but we hope to be able to discuss any existing work and how the community might
meet the challenge.
Related to this is the common and natural belief that the semantic properties of the landscapes will be
reflected in the performances of algorithms. A subsequent underlying assumption, or hypothesis, if the
landscape has a particular functional dependence on features of the instance, then such functional
dependencies are also likely to play a key role in understanding the behaviour of heuristic algorithms, and
so merit investigation. We are particularly interested the area of phase transitions; when particular
semantic properties display phases of 'almost always true' and 'almost never true'. Statistical methods
can then reveal some appropriate parameters to describe the locations of such phases, and we expect that
this will also influence the understanding, design and tuning of algorithms. This is exemplified by the work
in the artificial intelligence and statistical physics communities on propositional satisfiability and graph
colouring, and that has led to deeper understanding of algorithms, and development of new ones. One of
the goals of the workshop is to look into phase transition theory with a view to potential applications to
traditional GECCO problems.
The target participants are those that:
* Work on the theory of search algorithms, but are seeking ways for the theory to have a practical
* Work on direct applications, but are frustrated with the trial-and-error approaches that often are
often used, and would like to bring 'theoretically-inspired methods' into their work.
We also aim to bring together researchers and practitioners from related fields such as Operational Research
(OR), Artificial Intelligence and Computer Science, providing a medium for sharing and inspiring of
techniques (even if application domains are different) and developing common understandings.
We invite submissions as extended abstracts of around 3-4 pages addressing a relevant
topic. Speculative or position papers also be welcome. Submissionw will be reviewed for
quality and relevance.
He is a lecturer in Operational Research and Computer Science with the Automated
Scheduling, Optimisation and Planning (ASAP) research group in the School of Computer Science at the
University of Nottingham, UK. He received his PhD from the Department of Computer and Information
Science at Syracuse University, NY, USA in 1998. He worked as a lecturer in the Department of
Computer Engineering at Yeditepe University, Istanbul, Turkey from 1998-2007. He established and led
the ARTIficial Intelligence research group from 2002. He served as the Deputy Head of the Department
from 2004-2007. Dr Özcan joined the ASAP group as a senior research fellow in 2008. He has been
serving as an executive committee member for the LANCS
initiative, which is one of the largest Science and Innovation Rewards given by EPSRC (Engineering and
Physical Sciences Research Council, UK). His main research interests include intelligent decision support
systems, search and optimisation using heuristics, hyper-heuristics, meta-heuristics, hybrid approaches and
their theoretical foundations, and their applications to the
real world and theoretical problems.
Andrew J. Parkes
He obtained a Ph.D. in theoretical physics in 1984 from King's College of the University of
London. He then held research positions at the University of Southampton, CERN (the particle physics
accelerator and research laboratory at Geneva, Switzerland), the University of California at Davis, USA,
and the ETH in Zurich, Switzerland. In 1993 he changed from Physics to Artificial Intelligence and
Computer Science. In 1999 he obtained a Ph.D., in Computer Science from the University of Oregon,
USA, He became a faculty member of the Computational Intelligence Research Laboratory at the
University of Oregon working on topics in combinatorial optimisation. In 2005 he joined the Automated
Scheduling, Optimisation and Planning Research Group (ASAP) within the School of Computer Science
of the University of Nottingham where he researched the management and planning of teaching space
facilities within educational institutions. Since 2008 he has been a Lecturer in Operational Research and
Computer Science at the University of Nottingham with particular responsibility to support the recent
EPSRC-funded Science and Innovation award for "The LANCS Initiative: An initiative to build and
maintain national research capacity in foundational Operational Research." He is a member of the LANCS
executive committee and also coordinating a research cluster into the understanding of heuristics.
He was on the organising committee for the 2007 International Timetabling competition. He is a co-
inventor on two patents and has many refereed international publications.
He is a Reader in Natural Computation in the School of Computer Science at the University
of Birmingham. He got his PhD from the University of Exeter in 1991 in Artificial Intelligence, and then
worked in industry for British Maritime Technology. Returning to academia to complete post-doctoral
studies (studying the evolution of neural networks in parallel genetic algorithms) he then became a Senior
Researcher at De Montfort University, specialising in the theory of evolutionary algorithms. He joined the
University of Birmingham in 2000 as a lecturer in Computer Science. As well as contributing to the theory
of evolutionary algorithms, Rowe has many interdisciplinary research interests including complex systems,
biological and social modelling, medical image understanding and quantum computation. He is on the
editorial board of Evolutionary Computation (MIT Press) and Theoretical Computer Science (Elsevier).
|13 - GreenIT Evolutionary Computation
With an increasing cost and environmental impact, energy consumption in large scale and distributed systems has become the new global IT concern in governments, organizations, and companies. High performance computing (HPC) infrastructures have to answer to a continuously increasing service demand and now face power consumption and cooling capabilities issues. Similarly, since battery technologies have not yet matched advances in hardware and software technologies, saving energy in battery-operated devices is of key importance for mobile/wireless networks.
In addition to bringing new energy-efficient objectives and models, energy optimization in large scale and distributed systems opens new challenges for the evolutionary computation community. This covers from the use of evolutionary computation methods that optimize some issues (e.g. scheduling, memory/storage management, communication protocols etc.) that could improve the energy consumption of such systems to the design of green evolutionary optimization methods that reduce the amount of energy required to be executed.
The aim of this workshop is to bring together researchers interested in addressing challenging issues related to the use of evolutionary computation for power consumption optimization in large-scale and distributed computing systems. The workshop covers all energy-efficient related topics of evolutionary computation, including but not limited to:
- energy-efficient optimization and its applications
- energy-efficient scheduling algorithms
- optimization of energy-efficient protocols
- network design optimization
- modeling-representations, simulation and validation for energy consumption optimization problems
- large scale and high-dimensional energy-efficient optimization
- energy-aware smart grids
- thermal optimization in cloud computing/data centers
- online dynamic optimization for energy efficient systems
- energy optimization in uncertain environments
- learning and anticipation
- robustness and performance guarantees
- real-world energy efficient optimization problems
- management and profiling tools for energy efficient systems
Both theoretical papers and papers describing practical experiences will be welcome.
He earned his undergraduate degree in Economical & Social Sciences and his Master degree in Computer Science with distinction ('91) from the University of Namur, Belgium . He went on to obtain his Ph.D. degree ('94) in Computer Science with great distinction at the University of Grenoble (INPG), France. His research at the IMAG laboratory focussed on Mapping and scheduling task graphs onto Distributed Memory Parallel Computers. Next, he performed post-doctoral research on coordination languages and multi-agent evolutionary computing at CWI in Amsterdam.
Dr Bouvry gained industrial experience as manager of the technology consultant team for FICS (SONE) a world leader in electronic financial services. Next, he worked as CEO and CTO of SDC, a Saigon-based joint venture between SPT (a major telecom operator in Vietnam), Spacebel SA (a Belgian leader in Space, GIS and Healthcare), and IOIT, a public research and training center. After that, Dr Bouvry moved to Montreal as VP Production of Lat45 and Development Director for MetaSolv Software (ORCL), a world-leader in Operation Support Systems for the telecom industry (e.g. AT&T, Worldcom, Bell Canada, etc).
Dr. Bouvry is currently heading the Computer Science and Communications (CSC) research unit of the Faculty of Sciences, Technology and Communications of Luxembourg University, and serving as Professor. Pascal Bouvry is also treasurer & member of the administration board of CRP-Tudor, and member of various scientific committees and technical workgroups (ERCIM WG, COST TIST, LIASIT, etc.)
Samee U. Khan
He received a B.S. degree from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan, in May 1999, and a Ph.D. degree from the University of Texas, Arlington, TX, USA, in August 2007. Currently, he is Assistant Professor of Electrical and Computer Engineering at the North Dakota State University, Fargo, ND, USA. He is the founding director of bi-institutional and multi-departmental NDSU-CIIT Green Computing and Communications Laboratory (GCC Lab). The GCC Lab currently hosts 10 faculty members and over 35 research students. Moreover, he also is an Adjunct Professor of Computer Science, COMSATS Institute of Information Technology, Pakistan.
Prof. Khan has extensively worked on the general topic of resource allocation in autonomous heterogeneous distributed computing systems. As of recent, he has been actively conducting cutting-edge research on energy-efficient computations and communications. A total of 75 (journal: 27, conference: 42, book chapter: 3, technical report: 3) publications are attributed to his name. His current work has received external support in excess of $1.5M from the Fonds National de la Recherche Luxembourg, National Academies, and Higher Education Commission of Pakistan.
Prof. Khan is an associate editor of: (a) Cluster Computing, (b) International Journal of Communication Systems, and (c) Security and Communication Networks. He also serves on the editorial boards of: (a) Informatica, (b) Information Systems, (c) Interdisciplinary Sciences, (d) International Journal of Communication Networks and Distributed Systems, (e) International Journal of Distributed Systems and Technologies, (f) International Journal of Green Computing, (g) Journal of Information Technology Research, and (h) Multiagent and Grid Systems. Moreover, he is serving (or has served) as an editor for more than seven special issues related to sustainable computing and communications. Furthermore, he also has organized more than five conferences, tracks, and sessions on topics related to sustainable computing and communications.
Prof. Khan is serving (or has served) as general chair, program committee chair, organizing committee chair, and advisory board member for more than five conferences, such as ACM/IEEE/IFIP International Conference on High Performance Computing and Simulation (HPCS), International Conference on Metaheuristics and Naturally Inspired Computing (META), International Conference on Computational and Systems Biology (ICCSB), IEEE International Conference on Smart Grid and Home (SGH), IEEE/ACM International Conference on Green Computing and Communications (GreenCom), ACM International Conference on Frontiers of Information Technology (FIT), and European Conference on Modeling and Simulation (ECMS). Moreover, he is serving (or has served) on the technical program committees for more than 35 conferences, such as IEEE International Conference on Communications (ICC), IEEE Global Communications Conference (Globecom), IEEE Consumer Communications and Networking Conference (CCNC), and IEEE International Symposium on Multimedia (ISM).
Prof. Khan serves as a member of the steering committee of the IEEE Technical Area of Green Computing. He also is a member of the IEEE Technical Committee on Self-Organized Distributed and Pervasive Systems. Moreover, he also is a member of the European Association of Theoretical Computer Science, the Game Theory Society, the IEEE Communications Society, the IEEE Computer Society, and the Society of Photo-Optical Instrumentation Engineers. Furthermore, he is a domain expert for: (a) Agence Nationale de la Recherche (ANR), France; (b) The Research Council (TRC), Oman; (c) Science Foundations (NWO and STW), Netherlands.
Prof. Khan is the recipient of the John Steven Schuchman Memorial Outstanding Doctoral Student Award, University of Texas, Arlington, TX, USA, 2007 and the Nortel Outstanding Doctoral Dissertation Award, University of Texas, Arlington, TX, USA, 2008. He is the 2007 inductee of Upsilon Pi Epsilon, the Computer Science Honors Society. For more information, please visit: http://sameekhan.org/.
He received the Industrial Engineer Degree in Computer Science from Luxembourg University of Applied Sciences in 2003 and the M.S. degree in Web Intelligence from Ecole des Mines of Saint-Etienne, France, in 2004. He defended his PhD thesis entitled "A Multi-Agent Approach for Hybrid and Dynamic Coevolutionary Genetic Algorithms: Organizational Model and Real-World Problems Applications" in June 2008 with the University of Luxembourg and the Ecole des Mines of Saint-Etienne. Since August 2008 he is scientific collaborator in the Computer Science and Communications research unit (CSC) of the University of Luxembourg. His current research interests include nature inspired algorithms, green computing, vehicular ad hoc networks and multi-agent systems. He is actively participating in the following National and European research projects: TITAN (FNR/CORE - 2009/2010), WiSafeCar (EUREKA/CELTIC - 2009/2011) and GreenIT (FNR/CORE - 2010/2012). He is member of the COST Action IC0702 (Combining Soft Computing Techniques and Statistical Methods to Improve Data Analysis Solutions) and has a total of 22 publications (2 journal, 21 international conferences).
He received his Ph.D. diploma in Computer Science in 2009 from the Universtiy of Lille. He was a member of INRIA Lille - Nord Europe (French National Institute for Research in Computer Science and Control, Lille branch) DOLPHIN Team, and of the Fundamental Computer Science Laboratory of Lille (LIFL). He was involved in the ANR Docking@GRID and the ANR CHOC projects (NSF French equivalent). From September 2009 until March 2010 he was a PostDoctoral Researcher in the Advanced Learning Evolutionary Algorithms (ALEA) Team, INRIA Bordeaux - Sud-Ouest, France, working on parallel and distributed techniques for interacting Markov chains based modeling and development. During his stay at INRIA Bordeaux - Sud-Ouest he co-organized the ALEA working group and he was a member of the organizing committee for the "Evolutionary Algorithms - Challenges in Theory and Practice" and the "Rare Events Simulation" Workshops, held in Bordeaux, in March, respectively November 2010. He addressed topics ranging from evolutionary computation and optimization, parallel and distributed algorithms and Monte Carlo based algorithms with applications in general optimization problems, bio-informatics and rare events simulation. He collaborated with the Atomic Energy Commission (CEA Life Sciences Division and CEA CESTA), the Biology Institute of Lille (IBL) and the Sea French Research Institute (IFREMER).
Since the 1st of April 2010, Dr. TANTAR is a PostDoctoral Researcher at the Computer Science and Communications (CSC) Research Unit, University of Luxembourg (AFR Grant). He is currently involved in the GreenIT (FNR Core 2010-2012) project which aims at providing a holistic autonomic energy-efficient solution to manage, provision and administer the various resources of Cloud Computing / HPC centers. Dr. TANTAR also animates and co-organizes the GRIPHON working group at CSC which regroups several topics on green and energy efficient computing. He is also participating to the Carbon Neutral ICT Operations program at the University of Luxembourg, Luxembourg, an interdisciplinary project aiming at providing solutions for a carbon-free environment for the Belval Campus, to be constructed for the University of Luxembourg. His current research interests address the modeling and the optimization of large scale dynamic systems having energy efficiency as a main objective.
She received her Diploma degree in 2003 and MsC in 2005 in the field of Computational Optimization, both from the Computer Science Faculty at the "Al. I. Cuza University" in Iasi, Romania. In 2005 she joined the French National Institute for Research in Computer Science and Control(INRIA) in Lille. She was awarded the PhD title for Landscape analysis in multi-objective optimization in 2009 at the University of Lille 1. Between 2007 and 2009 she hold a lecturer position at the same university. During her PhD she was also awarded an INRIA Explorateurs scholarship to the CWI, Amsterdam, Netherlands. She developed a strong interest on new challenging aspects regarding landscape analysis in multi-objective, but also the theoretical foundations of stochastic methods and their scaling to practical problems. Before joining the CSC research unit, at the University of Luxembourg, in October 2010, she was an INRIA post-doctoral researcher in the Advanced Learning Evolutionary Algorithms (ALEA) team, at INRIA Bordeaux, dealing with performance guarantees factors for multi-objective particle methods, such as evolutionary algorithms and rare event simulation techniques.
Emilia was actively involved in the dissemination of research through the organization of the 1st Workshop on Evolutionary Algorithms - Challenges in Theory and Practice, supported by the EA association, France. It aimed at providing a unified view over the developments in evolutionary computation through different research fields, as computer science, mathematics and physics. Her main research interests concern the performance guarantee factors for online dynamic multi-objective optimization appearing in energy efficient optimization. This is motivated through the study of the stability of existing approaches by means of evolutionary particle like paradigms. Emilia is currently co-authoring with Oliver Schutze a book in Springer series, dealing with performance guarantees and landscape analysis in multi-objective optimization.
received the degree in engineering (2002) and the Ph.D. in Computer Science (2007) from the University of Malaga (Spain), and he is currently working as scientific collaborator at the Interdisciplinary Centre for Security, Reliability and Trust of the University of Luxembourg. His main research interests include energy efficient Grid and cloud computing, ad hoc networks, the design of new efficient meta-heuristics, and their application for solving complex real-world problems in the domains of logistics, telecommunications, bioinformatics, combinatorial, multiobjective, and global optimization. Dr. Dorronsoro has an international book (published in Springer-Verlag), eight international journals, six of them indexed in the ISI Journal Citation Report (JCR), six publications in the Lecture Notes in Computer Science (LNCS) series of Springer-Verlag, eight book chapters, and over 30 conference papers, obtaining awards and grants in some of these conferences. The publications of Dr. Dorronsoro are interesting for the research community, and an evidence is the high number of references to his work in the last five years (more than 250).
|14 - Graduate Students Workshop
This full day workshop will comprise of presentations by selected students pursing research in some aspect of evolutionary computation. Students will present their work to an audience that will include a 'mentor' panel of established researchers in evolutionary computation. Presentations will be followed by a question and discussion period led by the mentor panel.
The goal of the workshop is to assist students with their 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.
He was awarded a PhD in Computer Science from the University of Limerick, in 2006. Since then, he has pursued a postdoctoral career, first as an expert engineer at INRIA, France, and now as a Research Fellow in University College Dublin, Ireland. His research focus mainly on genetic algorithms, genetic programming, representations, the evolution of executable computer code, and genetic regulatory networks.
He is currently focused on the application of high-level evolutionary algorithms to dynamic environments, with particular emphasis on computer games. He has published extensively across all major EC conferences.