The 1st workshop on Understanding Problems (GECCO-UP) is intended to establish a forum for the discussion and exploration of methods for the analysis and synthesis of optimisation problems through both theoretical and experimental methods. In addition to creating a session catering for the presentation of new methods related to problem understanding, the workshop will provide an opportunity for participants to review existing methods, submit position papers discussing potential frameworks and propose future areas of interesting research. Having reviewed the recent literature relating to optimisation problems the following key areas of active research will be of specific interest (although submissions are not limited to these areas):

- creation of test problems;
- experimental methods for analysing and detecting problem landscapes;
- identifiers and metrics for describing problem features;
- spatial descriptors;
- problem space visualisation;
- difficulty and complexity analysis;
- analysis of dynamic problems;
- analysis of noisy problems;
- construction of problem taxonomies and theoretical foundations.

Kent McClymont He is a final year PhD Candidate at the University of Exeter. His research is focused on the study of multi-objective hyper-heuristic methods for solving hard real-world optimisation problems with heterogeneous encodings for which he has published a novel test problem suite. He is a member of AISB committee and was co-chair of the first PCCAT conference, a local postgraduate computer science conference, and continues as the co-chair of the PCCAT steering committee.

Ed Keedwell He is a Lecturer in Computer Science at the University of Exeter. His research is focused on Nature-Inspired Computation techniques and their application to real-world optimisation problems in engineering and bioinformatics. He has published almost 50 papers in this field and currently leads a group of 8-10 postgraduate students and postdoctoral researchers. Dr Keedwell has organized a symposium for AISB 2010 and chaired sessions at AISB 2010 and IEEE CEC 2010.

This workshop provides an opportunity for practitioners, computer scientists, and researchers within evolutionary computing to present and discuss their tangible accomplishments and speculative ideas related to the application of genetic and evolutionary computing in the real world. Of particular interest are ideas that relate to large scale computing, machine learning, mobile devices and engineering applications. The explosion of growth within the business analytic community and a renewed interest within academia for intelligent computing has established a natural need for the implementation of evolutionary algorithms. Traditional machine learning algorithms, optimization techniques and core analytics have been assets for business, government, academia and society in order to maintain sustainable practices and competitiveness. Since, evolutionary practices are exhibiting sustained applicability in aforementioned areas, this workshop will bring together a community to discuss personal field deployments and exploratory ideas for growing evolutionary computing within the real world. The “Partial List of Keywords and Phrases” below may suggest appropriate medical applications.

Nikhil Padhye He is currently a PhD candidate in Department of Mechanical Engineering at Massachusetts Institute of Technology (MIT), MA, USA. He obtained his Bachelor's-Master's (integrated) degree from Indian Institute of Technology (IIT) Kanpur in 2010. His research interests encompass Evolutionary Computation and its applications in Engineering Design, Novel Manufacturing Processes and Efficient Energy Systems.

Aaron Baughman Aaron is a Senior Managing Advanced Analytics and Software Engineer working as a Technical Lead on DeepQA (Jeopardy!) project. In 2011, Aaron was formally IBM Senior Certified as a Technology Consultant with a focus on advanced analytics. He leads the IBM Public Sector Invention Disclosure Team and the IBM Technical Community. Aaron has over 30 filed patent applications, 1 issued patent, 4 published ACM papers, 1 IEEE poster session, and has co-chaired IBM Academy of Technology conference on Maturing and Leveraging Biometric Analytics, co-chaired ACM GECCO Support for Patient Care 2010, and co-chaired ACM KDD 2011 Multimedia Data Mining workshop. He holds a Masters in Computer Science from Johns Hopkins, Bachelors in Computer Science from Georgia Tech, and two certificates in organizational creativity from the Walt Disney Institute.

Stefan Van Der Stockt Stefan is an IBM Certified IT Specialist forming part of the SA Software Solution Lab team, where he spearheads the adoption of emerging technologies such as Cloud Computing and advanced analytics solutions. Stefan has an analytics related patent filed and also has a published ACM paper. He serves on the South African Cloud Council, the IBM Professions board, as well as the Technical Leadership Team (an IBM Academy of Technology affiliate). Stefan was also on the ACM KDD 2011 Multimedia Data Mining workshop committee. He holds a Masters degree in Computer Science from the University of Pretoria, focusing on Computational Intelligence.

Steven Gustafson Steven Gustafson received a Ph.D. in Computer Science from the University of Nottingham in 2004, where he was a Research Fellow in the Automated Scheduling, Optimisation and Planning Research Group. His PhD dissertation was nominated for the BCS/CPHC Distinguished Dissertation award, which recognizes the top Ph.D. thesis in the UK Computer Science community. In 2005 and 2006, he co-authored papers that won the Conference Best Paper Award at the European Conference on Genetic Programming. In 2006, he was awarded the IEEE Intelligent System's AI 10 to Watch in AI award. Dr. Gustafson is currently employed as a Computer Scientist at the General Electric Global Research Center in Niskayuna, New York. His research interests include machine learning, information retrieval, robotics and geneticprogramming. Dr. Gustafson is one of the Technical Editor-in-Chiefs for the new Memetic Computing Computing Journal, published by Springer. Authors are encouraged to submit their manuscripts for the first issue in January 2009. Dr. Gustafson is also on the Editorial board of the Journal of Artificial Evolution and Applications.

Jonathan Jesneck Dr. Jonathan Jesneck is a Research Scientist in the Field Intelligence Lab at the Massachusetts Institute of Technology (MIT), where he leads computational projects and designs artificial intelligence systems for large-scale distributed data projects. Jonathan is also a founder and Principal Consultant for New Millennium Technology Group, an MIT-based consulting group focused on huge-scale computational problems. Jonathan studied geographic information systems (GIS) at MIT and founded GeoSapia, a spatial search engine for the real estate industry. After graduate school, he completed his postdoctoral research in the Cancer Program at the Broad Institute of Harvard and MIT and served as a Computational Biologist in the Pediatric Oncology Department of the Dana-Farber Cancer Institute, where he lead the analysis team for high-throughput screening for drug discovery. Jonathan received his PhD as an A.B. Duke Scholar at Duke University, where he designed and built artificial intelligence systems for early detection of breast cancer by fusing information from radiology, genetics, hematology, and histology.

Symbolic Regression and 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 numeric 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, algorithmic improvements to  make the techniques faster, more reliable and generally better controlled, and feature selection approaches enabled by symbolic modeling.

Steven Gustafson Steven Gustafson leads the Knowledge Discovery Lab at the General Electric Global Research Center in Niskayuna, New York. The Knowledge Discovery Lab is focused on large-scale data, semantics, ontologies and text mining, and pattern search and discovery. As a former member of the Machine Learning Lab and 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, several journal editorial boards, and a Technical Editor-in-Chief of the journal Memetic Computing. In 2006, he received the IEEE Intelligent System's “AI’s 10 to Watch” award.

Ekaterina Vladislavleva Katya is a Chief Data Scientist and Partner at Evolved Analytics and co-owner of Evolved Analytics Europe. She did a PhD on symbolic regression at Tilburg University, the Netherlands. Katya also holds a Professional Doctorate in Engineering (in industrial mathematics) from Eindhoven University of Technology, the Netherlands, and a Master of Science in Mathematics (in mathematical theory of intelligent systems) from Moscow State University of  Lomonosov, Moscow, Russia.  Katya pursues research in data-driven modeling, high-performance computing, and industrial optimization, particularly in the industrial scale data analysis and feature selection for regression.

http://www.symbolicregression.com

Developmental robotics (also known as epigenetic robotics) is mainly concerned with modelling the postnatal development of cognitive behaviours in living systems, such as language, emotion, curiosity, anticipation, and social skills. While current work in this field has shown significant successes, several aspects can be added to go even further. First, ontogenetically, mental development is based on and closely coupled with physical development of an organism, including development of both the body plan and the nervous systems. Autonomous mental development in living system was gradually shaped by a brain-body co-evolution embedded in a changing environment. The introduction of morphogenetic robotics addresses this first challenge in developmental robotics to a certain extent by integrating mental and physical development. Second, biological evidence suggests that autonomous mental development is driven by intrinsic motivational systems among others. Current robotic systems have a predefined intrinsic motivation system. However, the evolutionary origin that accounts for both physical and mental development is still missing. Evolutionary robotics applies evolutionary algorithms to the automatic design of neural controllers for autonomous robots without considering the role of development. Thus, integrating research on developmental (including epigenetic and morphogenetic) robotics and evolutionary robotics is the natural next step. Developmental plasticity can not only bias evolution, but also enhance evolvability by maintaining genetic diversity in changing environments and resolving robustness-variability trade-off. Therefore, we believe that it is high time to bring together evolutionary robotics and developmental robotics to form a new discipline evolutionary developmental robotics (evo-devo-robo).

This half-day workshop aims to bring toget her new theories and methodologies inspired by biological principles for evolutionary developmental robotics. The emphasis of the workshop is on bridging multi-disciplinary research areas, in particular evolutionary robotics, epigenetic robotics, developmental robotics, evolutionary developmental systems, artificial life, systems and developmental biology, cognitive science, and computational neuroscience. Topics of this workshop include, but are not limited to:

• Evolutionary and developmental approaches to design of robot body plan and controller
• Morphogenetic approaches to self-organizing multi-robot systems
• Morphogenetic reconfiguration of modular robots
• Evolution of neural and morphological development in robotic systems
• Evolution of self-organising multi-robot systems
• Neuro-cognitive development
• Interactions between evolution, learning and development
• Computational modelling of neural plasticity and neural development
• Evolutionary and developmental approaches to autonomous learning systems

The targeted audiences are researchers and students who are interested in this emerging new evo-devo-robo area, in particular evolutionary and developmental approaches to the design of robot body-plan and controller as well as cognitive development. This workshop not only fills the gap between the GDS track and Evolutionary Robotics track in GECCO, but also attracts researchers from epigenetic robotics, thus forming a new research area on evo-devo-robo.

Stephane Doncieux He received its PhD in computer science in 2003 from UPMC (Pierre and Marie Curie University, Paris, France). He is associate professor at the UPMC since 2004 and belongs to the ISIR (Institute of Intelligent Systems and Robotics). Together with Bruno Gas, he was the leader of the SIMA research team dedicated to the study of autonomous mobile robots from its creation in 2007. Since 2011, he belongs to the cognition research team dedicated to motor control, cognition, learning and adaptation. His field of research is mainly centered on Evolutionary Robotics and he studies in particular the use of multi-objective algorithms in this context, in particular to help searching for robot controllers exhibiting complex behaviors. To reach that goal, he draws inspiration from computational neuroscience. He has published over 50 peer-reviewed journal articles and conference papers. He has edited two books and organized workshops on bioinspired robotics, adaptive behavior and evolutionary robotics.

Yaochu Jin He received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, China, and the Dr.-Ing. Degree from Ruhr University Bochum, Germany. He is currently a Professor of Computational Intelligence and Head of the Nature Inspired Computing and Engineering (NICE) Group, Department of Computing, University of Surrey, UK. Before joining Surrey, he was a Principal Scientist and Project Leader with the Honda Research Institute Europe in Germany. His research interests include understanding evolution, learning and development in biology and bio-inspired approached to solving engineering problems. He (co)authored over 150 peer-reviewed journal and conference papers. He is an Associate Editor of seven international journals, including three IEEE Transactions. He has delivered over ten Keynote speeches at international conferences on multi-objective machine learning, computational modelling of neural development, morphogenetic robotics and evolutionary aerodynamic design optimization. He is a Fellow of BCS.

Jean-Baptiste Mouret He is currently an assistant professor at Université Pierre et Marie Curie-Paris 6 (UPMC), in which he belongs to the AMAC team (Architecture and Models for Adaptation and Cognition) of the ISIR (Institute for Intelligent Systems and Robotics). He mainly conducts researches that combine neuro-evolution and multi-objective evolutionary algorithms to improve the adaptive abilities of robots. In his recent work, his main contributions deal with behavioral diversity, synaptic plasticity, generalization abilities and generative encodings. He obtained a M.S. in computer science from EPITA in 2004, a M.S. in artificial intelligence from UPMC in 2005 and a Ph.D. in computer science from UPMC in 2008. He edited two books and participated to the organization of several conferences and workshop about bio-inspired robotics and evolutionary robotics.

http://pages.isir.upmc.fr/evodevorobo.

Benchmarking of optimization algorithms is crucial to assess performance of optimizers quantitatively, understand weaknesses and strengths of each algorithm and is the compulsory path to test new algorithm designs. The black-box-optimization benchmarking workshop aims at benchmarking both stochastic or deterministic continuous optimization algorithms. The new edition will follow the BBOB 2009 and BBOB 2010 GECCO workshops. Those previous editions have resulted in (1) collecting data of various optimizers (32 in 2009 and 25 in 2010) that are now freely available for the entire community, (2) providing meaningful tools for the visualization of the comparative results, and (3) have established a standard for the benchmarking of algorithms. As a result the BBOB test-suite as well as the results published at the workshops have been used in various publications (independent from the workshop), the benchmarking procedure proposed is becoming a standard and the data collected have started to be used by statisticians to identify and classify properties of algorithms. The impact of the previous edition is visible in the EC community but also in the mathematical optimization community where BBOB results start now to be cited.

With a new edition, we would like to build on the success of BBOB 2009 and BBOB 2010 and increase and diversify the data collection we already have. We believe that the results from previous editions support the task of designing new and better algorithms and hence we expect results from new algorithms to be submitted to the workshop. We will provide essentially the same test-suite as in 2010. However the source code of the test-functions which was available in Matlab, C, and Java will in addition be available in Python. We will also provide new postprocessing tools for comparing more than 2 algorithms. This new edition will entirely ban different parameter settings for different test functions and will encourage analysis that study the impact of parameter setting changes.

Participants are invited to submit a paper with the results of an algorithm of their choice plus comparisons with algorithms from our database. They are also encouraged to use the existing database for statistical analyses or for designing a portfolio of algorithms. When data for all algorithms of the portfolio are available in the database, the performance of the portfolio can be provided by the postprocessing without conducting further experiments. An overall analysis and comparison will be accomplished by the organizers and presented during the workshop together with the single presentations of each participant. Our presentation will in particular address the question, whether a significant over-adaptation of algorithms to the benchmark function set has been taken place during the last years and discuss the perspective of how benchmarks should (co-)evolve.

Anne Auger She is a permanent researcher at the French National Institute for Research in Computer Science and Control (INRIA). She received her diploma (2001) and PhD (2004) in mathematics from the Paris VI University. Before to join INRIA, she worked for two years (2004-2006) at ETH in Zurich. Her main research interest is stochastic continuous optimization including theoretical aspects and algorithm designs. She is a member of ACM-SIGECO executive committee and of the editorial board of Evolutionary Computation. She has been organizing the biannual Dagstuhl seminar "Theory of Evolutionary Algorithms" in 2008 and 2010.

Alexandre Chotard He graduated in mathematics before obtaining a Master degree in Informatics in 2011 from university Paris-Sud. He started in October his Phd under the supervision of Anne Auger and Nikolaus Hansen at the LRI, in the TAO team, following a six month internship on the convergence of evolution strategies. His subject is the enhancement and analysis of evolution strategies. He participates in Siminole and in COCO (COmparing Continuous Optimizers).

Verena Heidrich-Meisner She received her diploma in physics from the Christian-Albrechts-University Kiel, Germany, in 2004 and joined the Institut für Neuroinformatik at the Ruhr-University Bochum afterwards. She received the doctoral degree in physics from the Ruhr-University Bochum in 2011. After submitting her thesis she worked as postdoctorial researcher at INRIA (French National Research Institute in Computer Science and Applied Mathematics). Her research interests are machine learning and stochastic continuous optimization, in particular reinforcement learning and natural gradient descent. She published several articles at top conferences and journals in this area. She organized a special session on reinforcement learning at ESANN 2007.

Nikolaus Hansen He is a research scientist at INRIA, France. Educated in medicine and mathematics, he received a Ph.D. in civil engineering in 1998 from the Technical University Berlin under Ingo Rechenberg. Before he joined INRIA, he has been working in evolutionary computation, genomics and computational science at the Technical University Berlin, the InGene Institute of Genetic Medicine and the ETH Zurich. His main research interests are learning and adaptation in evolutionary computation and the development of algorithms applicable in practice. His best-known contribution to the field of evolutionary computation is the so-called Covariance Matrix Adaptation (CMA).

Olaf Mersmann He recieved his BSc and MSc in Statistics from the TU Dortmund. He is currently pursuing a PhD in Statistics with work based on his Bachelor Thesis on the design and analysis of benchmark experiments. Part of this work has been presented at conferences and is currently under review for publication. Using statistical and machine learning methods on large benchmark databases to gain insight into the structure of the algorithm choice problem is one of his current research interests.

Petr Posík He recieved his Diploma degree in Technical Cybernetics in 2001 and Ph.D. in Artificial Intelligence and Biocybernetics in 2007, both from the Czech Technical University in Prague, Czech Republic. From 2001 to 2004 he also worked as statistician, analyst and lecturer for StatSoft, Czech Republic. Since 2005 he works as a researcher in the Intelligent Data Analysis Lab, Department of Cybernetics at the Czech Technical University. Being on the boundary of optimization, statistics and machine learning, his research interests are aimed at improving the characteristics of evolutionary algorithms with techniques of statistical machine learning. He also serves as a reviewer for several journals and conferences in the evolutionary-computation field.

Mike Preuss He is Research Associate at the Computer Science Department, TU Dortmund, Germany, where he also received his Diploma degree in 1998. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective niching and the experimental methodology for (non-deterministic) optimization algorithms. He is currently working on the adaptability and applicability of computational intelligence techniques for various engineering domains and computer games, pushing forward modern approaches of experimental analysis as the Exploratory Landscape Analysis (ELA) and innovative uses of surrogate models. He was involved in founding the EvoGames track at Evo* and the Digital Entertainment Technologies and Arts (DETA) track at GECCO.

http://coco.gforge.inria.fr/doku.php?id=bbob-2012

Since Learning Classifier Systems (LCSs) were introduced by Holland [1] as a way of applying evolutionary computation to machine learning problems, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining to automated innovation and on-line control. Classifier systems are a very active area of research, with newer approaches, in particular Wilson's accuracy-based XCS [2], receiving a great deal of attention. LCS are also benefiting from advances in reinforcement learning and other machine learning techniques.

This would be the 15th 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 2011.

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 ...)
• Optimizations and parallel implementations (GPU, matching algorithms)

Interest of the workshop to the Genetic and Evolutionary Computation community LCSs have been an integral part of the evolutionary computation field almost since its beginnings, so this workshop is very interesting for the GEC community for itself, but also because it shares many common research topics with the broader GEC field such as linkage learning, niching techniques, variable-length representations, facet-wise models, etc. Therefore it can attract a broader audience besides the own LCS practitioners. Post-proceedings of the papers accepted for the workshop are published - after an additional selection - as a special issue of the Springer journal Evolutionary Intelligence, which is an extra element of interest for participating in the workshop.

Daniele Loiacono He 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 (FUZZIEEE). 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. Hw will serve as competitions chair for GECCO 2012. He has 43 refereed international publications between journal papers, conference papers, book chapters, and workshop papers.

Albert Orriols-Puig 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. In 2010, he took a software engineer position 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 serves as a reviewer for several conference and machine learning journals

Ryan Urbanowicz 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.

Music provides a perfect area of research for Evolutionary Computation. A number of problems are present and still open to new proposals, such as:

• Generative Music Composition.
• Evolutionary Music Modeling.
• Rhythmic structure and Key analysis.
• Music Transcription.
  
• Music Improvisation.
  
• Music Genre classification.
  
• Music mood analysis.
  
• Optimization of Music Performance.
  
• Sound synthesis.
  
• Machine learning methods for audio content analysis .
  
• Automatic tagging of audio signals.
  
• Content-based audio retrieval.
  
• Music information retrieval.
  
• Music recommendation.
  
• User interfaces for music management and retrieval.
  
• Intelligent audio effects.
  
• Audio restoration.
  
• Etc.

ECMusic 2012, the 2nd workshop on Evolutionary Computation and music, aims particularly at providing a place -both physical and virtual- where the research is not only shown but also performed. It follows the success of the first ECMusic, that was held in New Orleans in 2011 . Authors will thus be encouraged to send both regular papers describing new approaches and results, along with audio records allowing to appreciate the quality of the works. A CD will be compiled and distributed< among the audience with the results published in the workshop.

F. Fernández de Vega He is associate professor at University of Extremadura. He has co-edited several special Issues dealing with Parallel and Distributed Bioinspired Algorithms and the book entitled Parallel and Distributed Computational Intelligence, Springer, 2010. He has also organized the International Workshops on Parallel Bioinspired Algorithms (2005, 2007, 2009) and International Workshops on Parallel Architectures and Bioinspired Algorithms (2007 - ). He has published more than 150 peer reviewed papers in conferences, books and journals.

Evolutionary computation (EC) methods are applied in many different domains. Therefore soundly engineered, reusable, flexible, user-friendly, and interoperable software systems are more than ever required to bridge the gap between theoretical research and practical application. However, due to the heterogeneity of the application domains and the large number of EC methods, the development of such systems is both, time consuming and complex. Consequently many EC researchers still implement individual and highly specialized software which is often developed from scratch, concentrates on a specific research question, and does not follow state of the art software engineering practices. By this means the chance to reuse existing systems and to provide systems for others to build their work on is not sufficiently seized within the EC community. In many cases the developed systems are not even publicly released, which makes the comparability and traceability of research results very hard.

This workshop enables EC researchers to exchange their ideas on how to develop and apply generic and reusable EC software systems and to present open and freely available solutions on which others can build their work on. Furthermore, the workshop should help to identify common efforts in the development of EC software systems and should highlight cooperation potentials and synergies between different research groups. It concentrates on the importance of high-quality software systems and professional software engineering in the field of EC and provides a platform for EC researchers to discuss the following and other related topics:

• development and application of generic and reusable EC software systems
• architectural and design patterns for EC software systems
  
• software modeling of EC algorithms and problems
  
• open-source EC software systems
  
• expandability, interoperability, and standardization
  
• comparability and traceability of research results
  
• graphical user interfaces and visualization
  
• comprehensive statistical and graphical results analysis
  
• parallelism and performance
  
• usability and automation
  
• comparison and evaluation of EC software systems

Additional details are available at http://evosoft.heuristiclab.com

Stefan Wagner He received his MSc in computer science in 2004 and his PhD in technical sciences in 2009, both from the Johannes Kepler University Linz, Austria. From 2005 to 2009 he worked as an associate professor for software project engineering and since 2009 as a full professor for complex software systems at the University of Applied Sciences Upper Austria, Campus Hagenberg, Austria. Dr. Wagner is one of the founders of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) and is the project manager and head developer of the HeuristicLab optimization environment.

Michael Affenzeller He has published several papers, journal articles and books dealing with theoretical and practical aspects of evolutionary computation, genetic algorithms, and meta-heuristics in general. In 2001 he received his PhD in engineering sciences and in 2004 he received his habilitation in applied systems engineering, both from the Johannes Kepler University Linz, Austria. Michael Affenzeller is professor at the University of Applied Sciences Upper Austria, Campus Hagenberg, and head of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL).

The tenth annual Undergraduate Student Workshop at a GECCO conference will occur on Saturday, July 7, 2012 as part of the GECCO-2012 conference in Philadelphia, USA. The workshop will provide an opportunity for undergraduate students to present their research in evolutionary computation. Typically, presentations will describe senior-level research projects supervised by a faculty mentor; however, summer research projects or exceptional course projects may also be appropriate.

The workshop will be a half-day event, during which approximately eight undergraduate students will present their work to each other, to participating students' faculty mentors, and to GECCO participants interested in undergraduate research. Students should plan on 20-minute presentations, followed by five minutes of questions and discussion.

Students invited to the workshop will also participate in the conference poster session. Students will display posters summarizing their work, allowing the larger GECCO community to see what's being done by undergraduates in evolutionary computation. The poster session will also be a great opportunity for networking!

The goals of the Undergraduate Student Workshop are to:

• Provide a forum allowing undergraduate students to put a capstone on their undergraduate research activities, by presenting their work at an international conference;
• Encourage teaching faculty to consider undergraduate research opportunities for their students in the EC field;
• Prepare undergraduate students for graduate work in EC areas;
• Encourage sharing and networking amongst teaching faculty with students participating in undergraduate research projects in EC;
• Provide networking opportunities for graduate school faculty and undergraduate students interested in pursuing advanced degrees;
• Encourage more emphasis on education at the GECCO conference.

Sherri Goings She has been an assistant professor at Carleton College since 2010. She received her B.S. (computer science & engineering) and Ph.D. (computer science & engineering and the interdisciplinary graduate program ecology, evolutionary biology, & behavior) at Michigan State University in 2003 and 2010 respectively. Her research interests are in the fields of evolutionary computation and artificial life.  She specifically seeks to understand the evolutionary theories behind cooperation and altruism and to apply that knowledge to creating evolutionary algorithms that encourage individuals to cooperatively solve problems.

Building on workshops in 2010 and 2011, the 3rd annual workshop on Visualisation Methods for Genetic and Evolutionary Computation (VizGEC), to be held at GECCO 2012 in Philadelphia, is intended to explore, evaluate and promote current visualisation developments in the area of genetic and evolutionary computation (GEC). Visualisation is a crucial tool in this area, and particular topics of interest are:

• visualisation of the evolution of a synthetic genetic population
• visualisation of algorithm operation
• visualisation of problem landscapes
• visualisation of multi-objective trade-off surfaces
• the use of genetic and evolutionary techniques for visualising data
• novel technologies for visualisation within genetic and evolutionary computation
• facilitating human steer of algorithms

As well as allowing us to observe how individuals interact, visualising the evolution of a synthetic genetic population over time facilitates the analysis of how individuals change during evolution, allowing the observation of undesirable traits such as premature convergence and stagnation within the population. In addition to this, by visualising the problem landscape we can explore the distribution of solutions generated with a GEC method to ensure that the landscape has been fully explored. In the case of multi- and many-objective optimisation problems this is enhanced by the visualisation of the trade-off between objectives, a non-trivial task for problems comprising four or more objectives, in which it is necessary to provide an intuitive visualisation of the Pareto front to a decision maker. All of these areas draw together in the field of interactive evolutionary computation, in which it is vital that a decision maker be provided with as much information as they require to interact with the GEC method in the most efficient way possible, order to generate and understand good solutions quickly.

In addition to visualising the solutions generated by a GEC process, we can also visualise the processes themselves. It can be useful, for example, to investigate which evolutionary operators are most commonly applied by an algorithm, as well as how they are applied, in order to gain an understanding of how the process can be most effectively tuned to solve the problem at hand.

GEC methods have also recently been applied to the visualisation of data. As the amount of data available in areas such as bioinformatics increases rapidly, it is necessary to develop methods which can visualise large quantities of data; evolutionary methods can, and have, been used for this. Work on visualising the results of evolutionary data mining is also now appearing in the literature.

All of these methods benefit greatly from developments in high-powered graphics cards and work on 3D visualisation, largely driven by the computer games community. A workshop provides a good environment for the demonstration of such methods.

Based on these areas of interest the target audience for VizGEC is broad. We anticipate that people engaged in visualisation research will be interested, in addition to people from the GEC community who may be interested in using visualisation to advance their own work. We hope to attract both experienced practitioners as well as providing an introduction for those new to visualisation in GEC.

Richard Everson He is Associate Professor of Machine Learning at the University of Exeter. He has a degree in Physics from Cambridge University and a PhD in Applied Mathematics from Leeds University. He worked at Brown and Yale Universities on fluid mechanics and data analysis problems until moving to Rockefeller University, New York to work on optical imaging and modelling of the visual cortex. After working at Imperial College, London, he joined the Computer Science department at Exeter University. His research interests lie statistical pattern recognition, multi-objective optimisation and the links between them. Recent interests include the optimisation of the performance of classifiers, which can be viewed as a many-objective optimisation problem requiring novel methods for visualisation [1,2]. Research on the construction of league tables has led to publications exploring the multi-objective nature and methods of visualising league tables [3,4].

Jonathan Fieldsend He is Lecturer of Computer Science at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has held postdoctural positions as a research fellow (working on the interface of Bayesian modelling and optimisation) and as a business fellow (focusing on knowledge transfer to industry) prior to his appointment to an academic position at Exeter. His research interests include multi- and many-objective optimisation, machine learning and statistical pattern recognition and the interface between these areas. Work in these fields has led to an interest in visualisation, which in turn has led to peer reviewed work on the application and comparison of existing visualisation techniques to new domains (e.g. [1,2]), and the investigation of novel visualisation techniques (e.g. [3,4]). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

David Walker He is currently completing his Ph.D., the focus of which is the understanding of many-objective populations. A principal component of his thesis involves visualising such populations and he is particularly interested in how evolutionary algorithms can be used to enhance visualisation methods [3]. More recently, his research has also investigated evolutionary methods for the data mining of many-objective populations [4]. His general research interests include evolutionary problem solving, techniques for identifying preference information in data and visualisation methods.

[1] Fieldsend JE, Everson RE. (2005) "Visualisation of multi-class ROC surfaces",
2nd ROCML workshop, part of the 22nd International Conference on Machine
Learning (ICML 2005), Bonn, Germany.

[2] Everson RM, Fieldsend JE. (2006) "Multi-class ROC analysis from a multi-objective
optimisation perspective", Pattern Recognition Letters, volume 27, no. 8, pages
918-927.

[3] D.J. Walker, R.M. Everson and J.E. Fieldsend. "Visualisation and Ordering of
Many-objective Populations", In: Proceedings of the 2010 IEEE Congress on
Evolutionary Computation (CEC'10), Barcelona, Spain.

[4] D.J. Walker, R.M. Everson and J.E. Fieldsend. "Rank-based Dimension Reduction for
Many-criteria Populations" In: Proceedings of the 2011 Genetic and Evolutionary
Computation Conference (GECCO'11), Dublin, Ireland

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 the actions of a population of autonomous agents can be coordinated so that some outcome is achieved, or so that some aspect of a phenomenon is elucidated through modeling. 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 effectively drive the evolution of individuals towards some desired goal. Multi-agent simulation (also called agent-based modeling) addresses the bottom-up issue of how collective behavior emerges from individual action. Likewise, the study of evolutionary dynamics within EC often considers how population-level phenomena emerge from individual-level interactions. Thus, at a high level, we may view EC and MASS as examining analogous processes. It is therefore natural to consider how EC may be relevant to MASS, and vice versa; indeed, applications and techniques from one field have often made use of technologies and algorithms from the other field.

Example Topics:

• Multi-agent systems and agent-based models utilizing evolutionary computation    
• Optimization of multi-agent systems and agent-based models using evolutionary computation
• Evolutionary computation models which rely not on explicit fitness functions but rather implicit fitness functions defined by the relationship to other individuals / agents    
  
• Applications utilizing MASS and EC in combination    
  
• Biological agent-based models (usually called individual-based models) involving evolution  
  
• Evolution of cooperation and altruism    - Genotypic representation of the complex phenotypic strategies of MASS    
  
• Evolutionary learning within MASS (including Baldwinian learning and phenotypic plasticity)    
  
• Emergence and feedbacks    
  
• Open-ended strategy spaces and evolution    
  
• Adaptive individuals within evolving populations

http://www.cscs.umich.edu/ecomass/

Forrest Stonedahl He is is an Assistant Professor of Computer Science and Mathematics at Centre College. His dissertation work focussed on the use of evolutionary algorithms to explore the effects of varying parameters in multi-agent simulations, and 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 performing this task.  Forrest has also combined multi-agent systems with evolutionary computation in several earlier publications, including an agent-based model that used restrictive breeding networks for an evolutionary algorithm, and a novel network-based GA crossover operator inspired by a simple agent-based diffusion mechanism.  In addition, Forrest has published on the evolution of rules for non-uniform cellular automata and the analysis of noisy fitness landscapes.  Forrest's substantial experience with multi-agent simulation stems from his work at the Center for Connected Learning and Computer-Based Modeling at Northwestern University and his work contributing to the development of the NetLogo multi-agent modeling language and environment. He has been involved in a variety of agent-based modeling projects in application areas such as urban development (modeling land usage) and linguistics (language cascades in social networks).  Forrest's other scholarly interests include studying dynamic processes on networks, emergence in complex adaptive systems, and computer science education.

Rick Riolo Rick Riolo is an Associate Research Scientist and Director of the Computer Lab in the Center for the Study of Complex Systems at the University of Michigan. His research interests include evolutionary algorithms in theory and in models of complex adaptive systems, and agent-based modeling approaches to studying problems across a wide variety of complex systems, e.g., the spread of antibiotic resistance, the effects of phenotypic plasticity on ecological community dynamics, urban sprawl, and the effects of formal and informal institutions on the sustainability of common resource pools.

The Workshop focuses on the application of genetic and evolutionary computation (GEC) to problems in medicine and healthcare.

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
  

Although the application of GEC to medicine is not new, the reporting of new work tends to be distributed among various technical and clinical conferences in a somewhat disparate manner. A dedicated workshop at GECCO provides a much needed focus for medical related applications of EC, not only providing a clear definition of the state of the art, but also support to practitioners for whom GEC might not be their main area of expertise or experience.

GECCO is widely regarded to be the most authoritative conference in GEC and, as such, represents an ideal home for this important and growing community.

The importance of this application area has been confirmed by authors of accepted papers from MedGEC being invited to submit extended manuscripts for inclusion in special issues of "Genetic Programming and Evolvable Machines" and "Journal of Artificial Evolution and Applications" which have now been published. Additionally, a book containing extended papers accepted for previous MedGEC workshops was published by John Wiley & Sons in November 2010.  Finally, a chapter on medical applications in a book on Cartesian Genetic Programming was published by Springer last month.

Stephen L. Smith He 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 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.

Stefano Cagnoni He 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. 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 Initial Training Network" 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 has been Editor-in-chief of the "Journal of Artificial Evolution and Applications" from 2007 to 2010. 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 has been 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 He 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).

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, the work described in [1] used a genetic programming algorithm 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.

Although the work in [1] consisted of evolving a complete data mining/machine learning algorithm, in the area of optimization this type of approach is named a hyper-heuristic. 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 [2], raising the level of generality of the solutions produced by the hyper-heuristic evolutionary algorithm. For instance, a hyper-heuristic can generate a generic heuristic for solving any instance of the traveling salesman problem, involving any number of cities and any set of distances associated with those cities [3]; whilst a conventional evolutionary algorithm would just evolve a solution to one particular instance of the traveling salesman problem, involving a predefined set of cities and associated distances between them.

Whether we name it an approach for automatically designing algorithms or hyper-heuristics, in both cases, a set of human designed procedural components or heuristics surveyed from the literature are chosen as a starting point (or as "building blocks") for the evolutionary search. Besides, new procedural components and heuristics can be automatically generated, depending on which components are first provided to the method.

The main objective of this workshop is to discuss evolutionary computation methods for generating algorithms and/or hyper-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, and optimization.

[1] G. L. Pappa and A. A. Freitas, Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach, Springer, Natural Computing Series, 2010. xiii + 187 pages.

[2] E. k. Burke, M. Hyde, G. Kendall and J. Woodward, A genetic programming hyper-heuristic approach for evolving two dimensional strip packing heuristics. In: IEEE Transactions on Evolutionary Computation, 2010.

[3] M. Oltean and D. Dumitrescu. Evolving TSP heuristics using multi expression programming. In: Computational Science - ICCS 2004, Lecture Notes in Computer Science 3037, pp. 670-673. Springer, 2004.

Gisele L. Pappa She 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.

John Woodward 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 University of Nottingham 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.

Jerry Swan Prior to obtaining a PhD in computational group theory at Nottingham, Jerry Swan 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.

Matthew Hyde He received a PhD in Computer Science from the University of Nottingham, U.K., in 2009. He is currently a Senior Research Fellow within the Automated Scheduling, Optimisation, and Planning (ASAP) Research Group at Nottingham. His research interests include evolutionary computation, hyper-heuristics, metaheuristics, and operational research. He now works within the LANCS initiative, a collaborative project between four U.K. universities to build operational research theory for practice.

Global increases in living standards, diminishing natural resources and environmental concerns place energy amongst the most important global issues today. On the consumer side, there is an increasing need for more efficient, smart, uses of energy, be it in large-scale computing systems and data warehouses, in homes or in office buildings. On the producer side, there is a push toward the use of sustainable, green, energy sources, which often come in the form of less reliable sources such as wind energy. In addition,  future energy systems are often envisioned to be "smart", consisting of massive amounts of small generators, such as solar panels, located at consumers, effectively turning consumers into potential producers whenever they have a surplus of energy. The management, control and planning of, and efficient use of energy in (future) energy systems brings about many important challenges.

Energy systems are not only real-world systems, they are also one of the most important foundations of the modern world. Especially with the upcoming required changes to make more efficient use of energy and to shift towards a global use of sustainable, green energy sources, there are many challenges in mathematics and computer science. Real-world challenges, such as those arising in (future) energy systems, are typically highly complex because of the many aspects to be considered that are often disregarded in theoretical research such as dynamic changes, uncertainty and multiple objectives. In many situations therefore, problem-specific algorithms are infeasible or impractical. Instead, flexible and powerful approaches such as evolutionary algorithms (EAs) can often provide viable solutions. Typical real-world challenges that are addressed by EAs are of the optimization type. This covers the use of EAs to optimize issues ranging from energy consumption (e.g. scheduling, memory/storage management, communication protocols, smart sensors, etc.) to the planning and design of energy systems at many levels, ranging from small printed circuit boards to entire transmission networks.

The aim of this workshop is to bring together researchers interested in addressing challenging issues related to the use of evolutionary computation for applications in (future) energy systems. The workshop is a follow up of the GreenIT Evolutionary Computation workshop held at GECCO 2011.

The workshop covers all energy-related applications of evolutionary computation, including but not limited to:

- planning of (future) (smart) energy systems
- network design optimization
- management and control of (future) (smart) energy systems
- stability of smart energy systems
- dynamic demand and supply matching in smart energy systems
- smart homes, buildings, offices, streets, ...
- energy-efficient optimization and its applications
- energy-efficient scheduling algorithms
- optimization of energy-efficient protocols
- 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 are welcome.

Alexandru-Adrian Tantar He received his Ph.D. diploma in Computer Science in 2009 from the University of Lille. He was a member of the DOLPHIN team, INRIA Lille - Nord Europe / LIFL (French National Institute for Research in Computer Science and Control / Fundamental Computer Science Laboratory of Lille). By the end of 2008 he was on a short stay in the SEN4 team, CWI, Amsterdam, The Netherlands (INRIA Explorateurs Grant, September 2008 - December 2008). As a postdoctoral researcher in the Advanced Learning Evolutionary Algorithms team, INRIA Bordeaux - Sud-Ouest, he worked on parallel interacting Markov chains based algorithms (September 2009 - March 2010), he co-organized the ALEA working group and the "Rare Events Simulation 2010" workshop. He put the bases of the "Evolutionary Algorithms - Challenges in Theory and Practice" workshop (co-founder), that later led to the "EVOLVE - A bridge between Probability, Set Oriented Numerics and Evolutionary Computation" international conference (co-chair). He worked with the Atomic Energy Commission  (Life Sciences Division and CESTA), the Biology Institute of Lille and the Sea French Research Institute. Since the 1st of April 2010, Dr. Tantar is a Marie Curie (AFR Grant) postdoctoral researcher in the Computer Science and Communications Research Unit, University of Luxembourg. He is involved in the GreenIT (energy-efficient solutions for Cloud Computing / HPC centers, FNR Core 2010-2012). He also co-organizes the GRIPHON working group (CSC), he participates to the University of Luxembourg's Carbon Neutral ICT Operations program. The main research topics he addressed include parallel evolutionary computation, the modeling and optimization of large scale, energy-efficient dynamic systems, Monte Carlo based algorithms with applications in optimization, bio-informatics and rare events simulation.

Emilia Tantar She received both her Diploma degree and MsC 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 grant 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 is co-founder and co-chair of the EVOLVE International Conference and is currently co-authoring with Oliver Schutze a book in Springer series, dealing with performance guarantees and landscape analysis in multi-objective optimization.

Peter A. N. Bosman Peter A.N. Bosman is a scientific staff member in the research group Multi-agent and Adaptive Computation at the Centrum voor Wiskunde en Informatica (CWI) (Centre for Mathematics and Computer Science) located in Amsterdam, the Netherlands. Peter was formerly affiliated with the Department of Information and Computing Sciences at Utrecht University, where also heobtained both his MSc and PhD degrees in Computer Science, more specifically on the design and application of a specific type of evolutionary algorithm, the estimation-of-distribution algorithm (EDA). He has since then been an active researcher in the field of evolutionary computation. His current research position is mainly focused on fundamental EDA research and on applications of EDAs and (multi-)agent technology in energy systems, revenue management and the life sciences. Peter is best known for status of active researcher in the area of EDAs since its upcoming within the area of Genetic and Evolutionary Computation (GEC) and has (co-)authored some 40 publications in the GEC area. At the GECCO conference, Dr. Bosman has previously been track chair (EDA track, 2006, 2009), late-breaking-papers chair(2007) and co-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.

http://gradstudentworkshopgecco2012.blogspot.com/

Alison Motsinger-Reif She is an assistant professor at North Carolina State University in the Bioinformatics Research Center, and Department of Statistics.  Her primary research interest is statistical genetics, and she relies on evolutionary computation approaches to detect and understand associations with genetic, environmental, and metabolomic variables and human diseases.  She has published over 85 publications in statistics, genetics, and computer science journals. http://www4.stat.ncsu.edu/~motsinger/Lab_Website/Home.html