Message from the Workshop Chair
We call for submissions to 16 workshops which will be organised as part of GECCO in Vancouver, July 2014. The workshop programme covers of a wide range of topics related to genetic and evolutionary computation, and provides an excellent opportunity to develop new research directions with existing and new colleagues. We would particularly like to highlight the student workshop which gives undergraduate and graduate students the chance to discuss their work with a panel of established researchers.

I would like to thank all the workshop organisers for proposing and preparing a very exciting set of workshops. Detailed information about the workshops, including call for papers, are provided below.

Best wishes,

Per Kristian Lehre (workshop chair)



List of Workshops


Half day

Seventeenth Annual International Workshop on Learning Classifier Systems
more info
  • Ryan Urbanowicz (Darthmouth College, Lebanon)
  • Muhammad Iqbal (Victoria University of Wellington, New Zealand)
  • Kamran Shafi (University of New South Wales, Australia)
  • VizGEC: Visualisation in Genetic and Evolutionary Computation)
    more info
  • David Walker (University of Exeter, UK)
  • Richard Everson (University of Exeter, UK)
  • Jonathan Fieldsend (University of Exeter, UK)
  • Workshop on Problem Understanding and Real-world Optimisation (PURO)
    more info
  • Kent McClymont (University of Exeter, UK)
  • Kevin Sim (Edinburgh Napier University, UK)
  • Gabriela Ochoa (University of Stirling, UK)
  • Ed Keedwell (University of Exeter, UK)
  • Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef)
    more info
  • Anna I Esparcia-Alcazar, (S2 Grupo, Spain)
  • Frank W. Moore, (University of Alaska Anchorage, USA)
  • Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS) Eighth Annual Workshop
    more info
  • Forrest Stonedahl, (Centre College, USA)
  • William Rand, (University of Maryland, USA)
  • Student Workshop
    more info
  • Tea Tusar (Jozef Stefan Institute, Slovenia)
  • Boris Naujoks (Cologne University of Applied Sciences, Germany)
  • Evolutionary Computation Software Systems (EvoSoft)
    more info
  • Stefan Wagner (University of Applied Sciences Upper Austria, Austria)
  • Michael Affenzeller (University of Applied Sciences Upper Austria, Austria)
  • Evolutionary Synthesis of Dynamical Systems
    more info
  • Zhun Fan, (Shantou University, China)
  • Yaochu Jin (University of Surrey, UK)
  • Hod Lipson (Cornell University, USA)
  • Erik Goodman (Michigan State University, USA)
  • Evolutionary Computation in Education (ECED)
    more info
  • Sylvain Cussat-Blanc (University of Toulouse, France)
  • Una-May O'Reilly (MIT, USA)
  • Erik Hemberg (MIT, USA)
  • 4th Workshop on the Automated Design of Algorithms
    more info
  • John Woodward, (University of Stirling, UK)
  • Jerry Swan (University of Stirling, UK)
  • Earl Barr (University College, London)
  • Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC)
    more info
  • Alexandru-Adrian Tantar (University of Luxembourg, Luxembourg)
  • Emilia Tantar (University of Luxembourg, Luxembourg)
  • Peter A. N. Bosman (Centrum Wiskunde & Informatica, Netherlands)
  • Metaheuristic Design Patterns (MetaDeeP)
    more info
  • Jerry Swan, (University of Stirling, UK)
  • Krzysztof Krawiec, (Poznan University of Technology, Poland)
  • John Woodward, (University of Stirling, UK)
  • Chris Simons, (University of the West of England, UK)
  • John Clark, (University of York, UK)
  • Medical Applications of Genetic and Evolutionary Computation (MedGEC)
    more info
  • Stephen L. Smith (The University of York, UK)
  • Stefano Cagnoni (Universita' degli Studi di Parma, Italy)
  • Robert M. Patton, (Oak Ridge National Laboratory, USA)
  • Evolutionary Computation for Big Data and Big Learning
    more info
  • Jaume Bacardit (University of Newcastle, UK)
  • Ignacio Arnaldo (MIT, USA)
  • Kalyan Veeramachaneni (MIT, USA)
  • Una-May O'Reilly (MIT, USA)
  • Women@GECCO
    more info
  • Una-May O'Reilly (MIT, USA)
  • Anna Esparcia (S2 Grupo, Spain)
  • Gabriela Ochoa (University of Stirling, UK)
  • Aniko Ekárt (Aston University, UK)
  • Carola Doerr (CNRS & Universite Pierre et Marie Curie (Paris 6), France)
  • Anne Auger (INRIA Saclay, France)
  • Symbolic Regression and Modelling
    more info
  • Steven Gustafson (GE Global Research, USA)
  • Ekaterina Vladislavleva (Evolved Analytics Europe BVBA, Belgium)



    Seventeenth International Workshop on Learning Classifier Systems (IWLCS 2014)

    IWLCS 2014

    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 16th 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 2013.

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

    Muhammad Iqbal
    Mr. Muhammad Iqbal received a Master in Computer Science degree from the National University of Sciences and Technology (NUST), Pakistan, in 2008. He joined COMSATS Institute of Information Technology (CIIT), Pakistan, as a faculty member in the Department of Computer Science, in August 2008. He is now working with Dr. Will Browne and Prof Mengjie Zhang at the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand. The research is on Learning Classifier Systems with particular emphasis on extracting and using building blocks of knowledge to develop a scalable classifier system. To date Iqbal has authored 10 international publications exploring the development and scalability of XCS based classifier systems including three journal publications, and one which received best paper award at the Genetic and Evolutionary Computation Conference (GECCO) in 2013 in the Genetics Based Machine Learning track. He is serving as a reviewer for the IEEE Congress on Evolutionary Computation (IEEE CEC) since 2012, and is a member of the organizing committee for the International Workshop on Learning Classifier Systems (IWLCS) from 2013.


    Dr. Kamran Shafi
    Dr. Shafi holds a PhD in computer science, a M.Sc. in telecoms engineering and a B.Sc. in electrical engineering. His research focus is on the development of computational intelligence techniques that can be applied at various stages of data-centric predictive modelling in order to provide effective solutions to real world decision problems in diverse domains including national defence, logistics and computer security. In this context, he has contributed in several disciplines including genetic-based machine learning, game theory and optimisation. His PhD thesis “An online and adaptive signature-based approach for intrusion detection using learning classifier systems” received the Stephen Fester Award for the most outstanding thesis on an information technology topic by a postgraduate research student in the School of ITEE at UNSW Canberra. His other major research achievements in the field of LCS research include the development of an LCS based scenario mining approach in the context of free-flight air traffic control concept and development of an LCS based multi-objective hyper-heuristic framework for the defence logistics problem. He is a program committee member for GECCO and IEEE CEC since 2005. He was the publicity chair for the 2012 World Congress on Computational Intelligence (WCCI 2012).


    Dr. Ryan Urbanowicz
    Dr. Ryan Urbanowicz holds a Ph.D in computational genetics from Dartmouth College, and both a M.Eng. and B.Eng. in agricultural and biological engineering from Cornell University.  His current research focuses on the development of machine learning strategies for feature selection, modeling, classification, and data mining in studies of common complex human disease.  In particular he is interested in developing strategies to deal with two phenomena which hinder these tasks, namely epistasis and genetic heterogeneity. In 2009 he was awarded a Dartmouth Neukom Institute Fellowship funding the development of a learning classifier system (LCS) algorithm for the detection of complex multifactorial genetic associations predictive of disease.  To date Ryan has authored 11 international publications exploring the development and/or application of LCS algorithms including an extensive review of LCS algorithms, one which received best paper at GECCO 2010 in the Bioinformatics and Computational Biology track and another which received best paper at the Translational Bioinformatics Conference(TBC) in 2012. He served on the IWLCS organizing committee from 2010-2012 and is returning as an organizer from 2012-2014.


    VizGEC: Visualisation in Genetic and Evolutionary Computation

    The 5th annual workshop on Visualisation in Genetic and Evolutionary Computation (VizGEC), to be held at GECCO 2014 in Vancouver, will explore, evaluate and promote current visualisation developments in the area of genetic and evolutionary computation (GEC). Visualisation is a crucial tool in this area, providing vital insight and understanding into algorithm operation and problem landscapes as well as enabling the use of GEC methods on data mining tasks. Particular topics of interest include (but are not limited to):
    • visualisation of the evolution of a synthetic genetic population;
    • visualisation of algorithm operation;
    • visualisation of problem landscapes;
    • visualisation of multi-objective trade-off sets;
    • the use of genetic and evolutionary techniques for visualising data;
    • facilitating human steer of algorithms;
    • novel technologies for visualisation within genetic and evolutionary computation;
    • non-visual techniques for presenting results (e.g. audio and audio-visual).

    We invite short papers of up to 8 pages presenting novel developments in one or more of these areas, or other areas relevant to visualisation in an evolutionary context. Submissions in which visualisation techniques have been used or developed as a means to exploring a larger problem are welcome. We also invite submissions in the area of interactive or dynamic visualisations which might form the basis of demonstrations in the workshop.


    David Walker
    David Walker is an Associate Research Fellow with the College of Engineering, Mathematics and Physical Sciences at the University of Exeter. The focus of his PhD was the understanding of many-objective populations. A principal component of his thesis involved visualising such populations and he is particularly interested in how evolutionary algorithms can be used to enhance visualisation methods. More recently, his research has investigated evolutionary methods for the data mining of many-objective populations, as well as for training artificial neural networks. He is currently working on a project which applies evolutionary algorithms to the design of novel photonic structures. His general research interests include visualisation methods, evolutionary problem solving, particularly for machine learning problems, and techniques for identifying preference information in data.


    Richard Everson
    Richard Everson is 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. Research on the construction of league tables has led to publications exploring the multi-objective nature and methods of visualising league tables.


    Jonathan Fieldsend
    Jonathan Fieldsend 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 postdoctoral 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, and the investigation of novel visualisation techniques. He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.



    Workshop on Problem Understanding and Real-world Optimisation (PURO)

    Building on the success of previous Understand Problems and Real-World Optimisation workshops, this workshop aims to provide a single forum for the presentation and discussion of works focused on optimisation problems rather than the methods for solving them. The workshop brings together the study of real-world optimisation problems with the theoretical analysis and synthesis of problems. The workshop will focus on, but not be limited to, topics such as:
    • Methods for identifying and constructing models for new optimisation problems.
    • Study of real-world optimisation problems and case studies.
    • Theoretical and practical analysis of optimisation problems.
    • Fitness landscape analysis of real-world problems.
    • Classification and ontological analysis of problems.
    • Development of and technologies for building benchmark test problems.
    • Technologies and theoretical works supporting the implementation, examination and application of real-world and test problems.

    Kent McClymont
    He is an associate research fellow 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 and novel methods for evaluating heuristics through new test problems and methodologies. He has run two previous GECCO workshops on "Understanding Problems" and is a member of the AISB committee and oversees the AISB workshop series.

    Kevin Sim
    He is a research fellow at Edinburgh Napier University, Scotland, UK. He currently works on a large national EPSRC project (EP/J021628/1) entitled “Real World Optimisation with Life-Long Learning”. His interests lie in the field of hyper-heuristics and classification algorithms. He has previously co-chaired workshops on the subject of real world optimisation problems at GECCO and EvoStar.

    Gabriela Ochoa
    She is a Lecturer at the University of Stirling in Scotland. Her research interests lie in the foundations and application of evolutionary algorithms and heuristic search methods, with emphasis on autonomous (self-*) search and fitness landscape analysis. She has published over 60 international peer reviewed papers. She is associate editor of Evolutionary Computation (MIT Press), was involved in founding the Self-* Search track at GECCO, and has organised several workshops and special sessions at international conferences.

    Ed Keedwell
    Ed Keedwell is a Senior 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 over 70 papers in this field and currently leads a group of 8-10 postgraduate students and postdoctoral researchers.  His currently funded EPSRC research includes 'SEQAH' (EP/K000519/1), a project investigating the interaction between selective hyper-heuristics, heuristics and problems over time.


    Workshop on genetic and evolutionary computation in defense, security and risk management (SecDef)

    With the constant appearance of new threats, research in the areas of defense, security and risk management has acquired an increasing importance over the past few years. These new challenges often require innovative solutions and Computational Intelligence techniques can play a significant role in finding them. We seek both theoretical developments and applications of Genetic and Evolutionary Computation and their hybrids to the following (and other related) topics:

    • Cyber crime and cyberdefense : anomaly detection systems, attack prevention and defense, threat forecasting systems, anti spam, antivirus systems, cyber warfare, cyber fraud  
    • IT Security: Intrusion detection, behavior monitoring, network traffic analysis
    • Corporate security, with special focus on BYOD policies and usability of security                      
    • Risk management: identification, prevention, monitoring and handling of risks, risk impact and probability estimation systems, contingency plans, real time risk management         
    • Critical Infrastructure Protection (CIP)                    
    • Advanced Persistent Threats (APTs)          
    • Design of military systems and sub-systems.         
    • Logistics and scheduling of military operations.      
    • Strategic planning and tactical decision making.    
    • Multiobjective techniques for examining tradeoffs in military, security, and counter-terrorism procedures.   
    • Automated discovery of tactics and procedures for site security, force protection, and consequence management.
    • Other computational intelligence techniques for applications in the areas listed above.

    The workshop invites completed or ongoing work, with the aim to encourage communication between active researchers and practitioners to better understand the current scope of efforts within this domain. The ultimate goal is to understand, discuss, and help set future directions for computational intelligence in security and defense problems.


    Anna I Esparcia-Alcázar            
    Anna I Esparcia-Alcázar is Head of R&D at S2 Grupo. She holds a degree in Electrical Engineering from the Universidad Politecnica de Valencia (UPV), Spain, and a PhD from the University of Glasgow, UK. She has ample experience both in industry and academia. For the past 8 years she has been actively involved in the organisation of the two main conferences in the field of Evolutionary Computation, evostar and GECCO. She is Senior Member of the IEEE, Member of the ACM and elect member of the Executive Committee of SIGEVO.

    Frank Moore
    He is an Associate Professor of Computer Science at the University of Alaska Anchorage. He has taught computer science and engineering for the past 16 years. He also has over six years of industry experience developing software for a wide range of military projects. His recent NASA-funded research (patent pending) used evolutionary computation to optimize transforms that outperform wavelets for lossy image compression and reconstruction. He has received over $750,000 in research funding and has published over 80 technical papers and reports. Dr. Moore is a Senior Member of ACM and a Member of IEEE.


    On Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS) 8th 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 adapt 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 or influence individual decisions. Similarly, most work in EC is concerned with how to engineer selective pressures to drive the evolution of individual behavior 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 (for example in coevolution) often considers how population-level phenomena emerge from individual-level interactions. Thus, at a high level, we may view EC and MASS as examining and utilizing analogous processes. It is therefore natural to consider how knowledge gained within 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. Studying EC and MASS in combination is warranted and has the potential to contribute to both fields.

    The EcoMASS workshop, now in its 8th iteration, welcomes original submissions on all aspects of Evolutionary Computation and Multi-Agent Systems and Simulation, which include (but are not limited to) the following topics and themes:

    • 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

    Forrest Stonedahl
    He 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.


    William Rand
    While doing his graduate work at the University of Michigan, William completed a dissertation under the guidance of John Holland and Rick Riolo on genetic algorithms and dynamic environments, which discusses applications of that work to the use of GAs in multi-agent systems. He also published six papers on the same topic which were featured at both GECCO and EuroGP. At the same time, he continued to develop his interest in modeling by working on a large scale agent-based model of suburban sprawl. William then went on to Northwestern University's Institute on Complex Systems (NICO), where he worked on developing the NetLogo programming language, an integrated development environment for agent-based modeling (ABM). While there he co-authored a textbook on ABM under contract with MIT Press. He is currently the Director for the Center for Complexity in Business at the University of Maryland where he is also an Assistant Professor in Marketing, Decision, Operations and Information Technology and Computer Science. William has become interested in using evolutionary computation techniques to generate and refine agent-based models, and has presented papers on this subject at Agent, Swarmfest, GECCO, the World Congress on Social Simulation (WCSS) and the North American Association for Computational Social and Organization Sciences (NAACSOS). Besides co-organizing ECoMASS at GECCO-2007, GECCO-2008, GECCO-2009, GECCO-2010, GECCO-2011, and GECCO-2013 he was also chair of all workshops for GECCO-2012.


    Student workshop

    Open to all students, graduates and undergraduates
    The goal of the Student Workshop is to assist students with their research in the field of Evolutionary Computation. Students will receive useful feedback on the quality of their work and presentation style. This will be assured by a discussion period after each talk led by a mentor panel of established researchers. Students are encouraged to use this opportunity to get feedback not only on the presented subject but also future research directions. In addition, the contributing students are invited to present their work as a poster at the GECCO 2014 Poster Session - an excellent opportunity present their work to a broader audience and to network with industrial and academic members of the community. Last, but not least, the best contributions will compete for a Best Student Paper Award.


    Tea Tusar
    She is a research assistant at the Department of Intelligent Systems at the Jozef Stefan Institute and a PhD student at the Jozef Stefan Postgraduate School (both in Ljubljana, Slovenia). Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with applications in optimization of metallurgical production processes and design of alternative energy supply systems, and machine learning methods for text processing and outlier detection. Recently, she has been researching visualization techniques for viewing multidimensional Pareto front approximations found by multiobjective optimizers.


    Boris Naujoks
    He received his PhD from Dortmund Technical University where he
    was a long-time research assistant and PhD student. During this time, he also gained industrial experience working for different SMEs. He always stayed in contact to academia and received a post-doc position at Cologne University of Applied Science (CUAS) after his PhD in 2011. Meanwhile, he enjoys the combination of teaching maths as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of CUAS. He focussed on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, and (industrial) applicability of the explored methods.



    Evolutionary Computation Software Systems (EvoSoft)

    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

    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).



    Evolutionary Synthesis of Dynamical Systems

    We are glad to announce that the Workshop on Evolutionary Synthesis of Dynamical Systems will take place at the GECCO-2014 conference in Vancouver! Because submissions for the conference tracks are now closed, submitting an article to the Workshop on Evolutionary Synthesis of Dynamical Systems is a great way to share your results at the coming GECCO conference and solicit immediate feedback from the experts and Evolutionary Synthesis enthusiasts!

    • As you know, Electronic Design Automation (EDA) has achieved great success both in academia and industry. However, automated synthesis of more general dynamical systems, such as mechanical systems or mechatronic systems, poses many more challenges while gaining increasing importance. In particular, when more and more conventional mechanical products are becoming mechatronic products with integration of electronic sensors, actuators and embedded controllers, automated design and optimization of mechatronic systems (including robotic systems) has gradually become a mainstream of research activities. Because it is often the case that both topology exploration and parameters optimization are to be automated in the synthesis of dynamical systems, evolutionary computation approaches have appeared to be one of the most effective ways in automated synthesis of dynamical systems.

      Even though a plethora of successful applications have been reported in the field of automated synthesis of dynamical systems, there remain many open issues and opportunities that are continually emerging as intriguing challenges for the field. The aim of this special session is to serve as a forum for scientists in this field to exchange the latest advantages in theories, technologies, and practice.

      We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:

      ·   Theory of Automated Synthesis using Evolutionary Approaches
      ·   Techniques and Algorithms Used for Evolutionary Synthesis
      ·   Simultaneous Synthesis of both Topologies and Parameters using Evolutionary Approaches
      ·   Define Similarity Metrics Between Individuals of Dynamical Systems (when both topology and parameter are considered)
      ·   Multi-Objective and Many-Objective Optimization Applied in Evolutionary Synthesis of Dynamical Systems
      ·   Data Mining and Machine Learning in Evolutionary Synthesis of Dynamical Systems
      ·   Memetic Computing in Evolutionary Synthesis of Dynamical Systems
      ·   Evolutionary Synthesis of Robotic Systems
      ·  Morphogenetic robotics, evolutionary developmental robotics
      ·  Evolutionary developmental neurocomputing
      ·   Surrogate Models Used for Evolutionary Synthesis of Dynamical Systems

    Zhun Fan
    Zhun Fan received his Ph.D. (Electrical and Computer Engineering) in 2004 from the Michigan State University. He received the B.S. degree in 1995 and M.S degree in 2000, both from Huazhong University of Science and Technology, China. From 2004 to 2011, he was employed as an Assistant Professor and Associate Professor at the Technical University of Denmark. He has also been working at the BEACON Center for Study of Evolution in Action at Michigan State University. He is currently a Professor at the Shantou University, China. His major research interests include applying evolutionary computation and computational intelligence in design automation and optimization of mechatronic systems, computational intelligence, wireless communication networks, MEMS, intelligent control and robotic systems, robot vision etc. He is Principal Investigator for a number of projects sponsored by the Danish Research Agency of Science Technology and Innovation, National Natural Science Foundation of China etc. He is a Senior Member of IEEE, and member of ACM.


    Yaochu Jin
    Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. Degree from Ruhr University Bochum, Germany, in 2001.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. His research interests include understanding evolution, learning and development in biology and bio-inspired approached to solving engineering problems. He is an Associate Editor of BioSystems, , the IEEE Transactions on Cybernetics, IEEE Transactions on NanoBioscience and the IEEE Computational Intelligence Magazine. He is also an Editorial Board Member of Evolutionary Computation. He is an Invited Plenary / Keynote Speaker on several international conferences on various topics, including multi-objective machine learning, computational modeling of neural development, morphogenetic robotics and evolutionary aerodynamic design optimization. He is the General Chair of the 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology and Program Chair of 2013 IEEE Congress on Evolutionary Computation. Dr Jin is Vice President for Technical Activities and an IEEE Distinguished Lecturer of the IEEE Computational Intelligence Society. He is Fellow of BCS and Senior Member of IEEE.


    Hod Lipson
    Hod Lipson is a Professor of Engineering at Cornell University in Ithaca, New York, and a co-author of the recent book “Fabricated: The New World of 3D printing”. His work on self-aware and self-replicating robots, food printing, and bio-printing has received widespread media coverage including The New York Times, The Wall Street Journal, Newsweek, Time, CNN, and the National Public Radio. Lipson has co-authored over 200 technical papers and speaks frequently at high-profile venues such as TED and the US National Academies. Lipson received his Mechanical Engineering PhD in 1998 from Technion Israel Institute of Technology. Before joining the faculty of Cornell in 2001, he was a postdoctoral researcher at Brandeis University's Computer Science Department and a Lecturer at MIT's Mechanical Engineering Department. Hod directs the Creative Machines Lab, which pioneers new ways to make machines that create, and machines that are creative.


    Erik Goodman
    Erik D. Goodman is PI and Director of the BEACON Center for the Study of Evolution in Action, an NSF Science and Technology Center headquartered at Michigan State University, funded at $25 million for 2010-15, extendable to 2020. He studies application of evolutionary principles to solution of engineering design problems. He received the Ph.D. in computer and communication sciences from the University of Michigan in 1971. He joined MSU’s faculty in Electrical Engineering and Systems Science in 1972, was promoted to full professor in 1984, and holds appointments in Mechanical Engineering and in Computer Science and Engineering. He directed the Case Center for Computer-Aided Engineering and Manufacturing from 1983-2002, and MSU’s Manufacturing Research Consortium from 1993-2003.  He co-founded MSU’s Genetic Algorithms Research and Applications Group (GARAGe) in 1993.  In 1999, he co-founded of Red Cedar Technology, Inc., which develops design optimization software. He was chosen Michigan Distinguished Professor of the Year, 2009, by the Presidents Council, State Universities of Michigan. He was Chair of the Executive Board and a Senior Fellow of the International Society for Genetic and Evolutionary Computation, 2003-2005. He was founding chair of the ACM’s SIG on Genetic and Evolutionary Computation (SIGEVO) in 2005.



    Evolutionary Computation in Education (ECED)

    In order to perform cutting edge, long range, influential and relevant Evolutionary Computation (EC) research, there is a need to provide a steady stream of new and competent EC researchers to the research pool. Furthermore, for engineers and scientists to be able to efficiently apply, develop and improve EC in pertinent practice, we need to continuously refine the pedagogical experience. By combining the ideas and experiences from a global field, EC educators will be able to raise the standards of EC education practices and material, and ultimately the students.
    The goals of the workshop are to exchange information and knowledge on how EC is taught, learn from each other and figure out how to further leverage the resulting discussion in order to boost the global EC awareness and literacy. We also want to inspire discussion and share ideas of how EC could be taught. The path to reach these goals involves having the workshop participants present their teaching experience, curriculum and bring all this together into a discussion. The scope of the workshop covers how EC is taught at varying education levels all over the world. An expected outcome of the workshop is to create an EC community education repository where educators can find education material and exercises, text, source code and videos.

    Submissions might include:

    • Describe a syllabus containing one or several EC modules, pedagogical content and the student body
    • Anecdotes from the teaching podium
    • Ideas for MOOCs, and other position contributions are also welcome.

    In addition, we believe that it is very interesting to hear how EC is taught as a module within a larger course as well as an entire course focusing on EC.


    Sylvain Cussat-Blanc
    Sylvain Cussat-Blanc has a permanent research and teaching position at University of Toulouse. He is a permanent member of the Institute of Research in Computer Science of Toulouse (IRIT), a research unit of the French National Center for Research (CNRS). He is interested in developmental models, gene regulatory networks, evolutionary robotics, artificial life and evolutionary computation in general. He is also working on a serious game to teach biologists cell proliferation mechanisms.
    He obtained his Ph.D. in 2009. His work was about a cell-based developmental I to produce artificial creatures. During his postdoctoral fellowship with Jordan Pollack in 2011 at Brandeis University (USA), he applied these approaches to evolutionary robotics with the aim to automatically design the real modular robots' morphologies. email:sylvain.cussat-blanc at irit dot fr


    Una-May O'Reilly
    Una-May O'Reilly leads the AnyScale Learning for All (ALFA) Group at CSAIL, MIT. She is a 15+ year veteran in the Evolutionary computation community. Her research interests are in scalable EC, genetic programming and GBML. Her group currently has projects in clinical medicine knowledge discovery, wind energy, and MOOC Technology. email:unamay at csail dot mit dot edu
    Erik Hemberg
    Erik Hemberg is a Post Doctoral Associate with the ALFA group at CSAIL at MIT. He received his Ph.D in Computer Science from University College Dublin, Ireland in 2010 and has a M.Sc from Chalmers University of Technology, Sweden.
    In 2013, Hemberg and O'Reilly co-taught a 1 week, full time course on EC at Shantou University in China employing interactive learning activities and other means of engaging learners to gain a clear, tangible, experience-based abstract understanding of evolution at a level above and beyond textbook biology. They are developing and delivering a extension which is a small private online course (SPOC) course on Evolutionary Processes and Computation for Shantou University, China. email:hembergerik at csail dot mit dot edu


    4th Workshop on the Automated Design of Algorithms


    How can we automatically generate algorithms on demand? While this was one of the original aims of Machine Learning and Artificial Intelligence in the early 1950s, and more recently Genetic Programming in the early 1990s, existing techniques have fallen short of this elusive goal. This workshop will outline a number of steps in the right direction on the path to achieving this goal. While we often regard genetic programming as a population-based method of generating programs, we prefer the wider definition as any metaheuristic applied to a space of programs, i.e. any method of sampling a set of candidate programs. Indeed random search and iterative hill-climbing have both successfully been employed to automatically design novel (components of) algorithms. 

    This approach is in contrast to standard Genetic Programming which attempts to build programs from scratch from a typically small set of functions. Instead we take an already existing program(s) and allow evolution to improve the program. When the automatic design of algorithms is done using a genetic programming system (effectively in vitro), the methodology is typically referred to as Genetic Programming as a Hyper-heuristic. When the automatic design of algorithms is done directly on source code (effectively in situ), the methodology is typically referred to as Genetic Improvement [5, 7].

    Although most 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. In other words, the genetic programming system is operating at a higher level of abstraction compared to how most search methodologies are currently employed, i.e. we are searching for solution methods, as opposed to solutions themselves.

    The automatic design of algorithms has some distinctions from standard genetic programming. In essence, automatic design consists of a stage where an algorithmic framework or template is defined and another stage where genetic programming supplies candidate algorithms to plug into the overall template. This approach can be identified with the Template Method pattern from Designed Patterns associated with Object Oriented programming. In short, the human provides the overall architecture of the algorithm (for example WHILE-loops, FOR-loops and IF-THEN-ELSE statements) and genetic programming fills in the details (for example, the bodies of the loops, or the condition and actions of the IF-THEN-ELSE statements). Thus, the resulting algorithm is part man-made and part machine-made.

    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 the space of heuristics, instead of directly 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" or primitive components) 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 [4].

    [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.

    [4] John R. Woodward and Jerry Swan, "The automatic generation of mutation operators for genetic algorithms", in Proceedings of the 14th international conference on Genetic and evolutionary computation conference, 2012.

    [5] William B. Langdon and Mark Harman. Genetically Improving 50000 Lines of C++. Research Note , RN/12/09, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK, 2012.

     [6] Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan. Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming. IEEE Transactions on Evolutionary Computation. Accepted for future publication.

    [7] Justyna Petke, Mark Harman, William B. Langdon, and Westley Weimer Using Genetic Improvement & Code Transplants to Specialise a C++ Program to a Problem Class Proceedings of the 17th European Conference on Genetic Programming, EuroGP 2014, Granada, Spain, 2014. Springer Verlag.


    John R. Woodward
    John R. Woodward is a lecturer at the University of Stirling, within the CHORDS group ( and is employed on the DAASE project (, and for the previous four years was a lecturer with the University of Nottingham. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, particularly Search Based Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming.  He has over 50 publications in Computer Science, Operations Research and Engineering which include both theoretical and empirical contributions, and given over 50 talks at International Conferences and as an invited speaker at Universities. He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities.

    Jerry Swan
    Before entering academia, Jerry Swan spent 20 years in industry as a systems architect and software company owner. He obtained his PhD in Pure Mathematics (Computational Group Theory) from the University of Nottingham in 2006. His research interests lie at the intersection of software engineering, formal methods and optimization and include meta- and hyper-heuristics, symbolic computation and machine learning. He has published more than 30 papers in international journals and conferences and serves as a reviewer for numerous journals and program committees. Jerry's research has been presented at renowned conferences worldwide and he has been a presenter and co-organizer of the GECCO "Automated Design of Algorithms" Workshop from 2011 to 2013.


    Earl Barr
    Earl Barr is a lecturer at the University College London. He received his Ph.D. (2009) degree in Computer Science at the University of California at Davis. He won the I3P Fellowship from the US Department of Homeland Security in 2010 and serves as a co-PI on three NSF grants and an Air Force DURIP grant. He is a co-investigator on SemaMatch, a UK EPSRC grant on malware.  Dr. Barr's research interests include testing and analysis, empirical software engineering, computer security, and distributed systems. His recent work focuses on testing and analysis of numerical software, automated debugging, defect analysis and prediction, and code obfuscation.



    Green and Efficient Energy Applications of Genetic and Evolutionary Computation (GreenGEC)

    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 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. We invite submissions up to 8 pages in ACM format. The talks should present recent research ideas, identify new challenging problems or show obtained results. Both theoretical papers and papers describing practical experiences will be welcome. Accepted papers will be published in a separate workshop proceedings associated to the GECCO conference which also appears in
    the ACM Digital Library.

    Alexandru-Adrian Tantar
    Alexandru-Adrian Tantar received his PhD in Computer Science in 2009 from the Universtiy of Lille. He was a member of the French National Institute for Research in Computer Science and Control, and of the Fundamental Computer Science Laboratory of Lille. He addressed topics ranging from evolutionary computation and optimization, parallel and Monte Carlo based algorithms, with applications in optimization, bio-informatics and rare event simulation. During this time, 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 2012, Dr. Tantar is a Research Associate at the University of Luxembourg. He is currently involved in the Green@Cloud project which aims at providing a holistic autonomic energy-efficient solution to manage, provision and administer the various resources of Cloud Computing / HPC centers. Alexandru is one of the co-founders and general chairs of the EVOLVE International Conference, co-editor for the EVOLVE book series and editor of the "Evolutionary Computing & Complex Systems" Soft Computing special issue.


    Emilia Tantar
    Dr. Tantar is a Research Associate at the Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg. From 2005 until 2010, Emilia was a member of the French National Institute for Research in Computer Science and Control (INRIA). She was awarded the PhD title in 2009 for her research on Landscape Analysis in Multi-Objective Optimization carried at the University of Lille 1.

    Emilia has a strong interest for new challenging aspects in landscape analysis in the multi-objective area as well as for the theoretical foundations of stochastic methods and their scaling to practical problems. Emilia is actively involved in the dissemination of research through the co-chairing and foundation of the EVOLVE International Conference series, and as co-editor of the Springer volumes "EVOLVE - A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation". Her main research interests concern the performance guarantee factors for online dynamic multi-objective optimization appearing in energy efficient optimization. She was actively supporting the GECCO conference as a co-organizer of the GreenGEC workshop since its foundation in 2010, as well as a Students chair for the 2013 edition.


    Peter A.N. Bosman
    Peter A.N. Bosman is a senior researcher in the Intelligent Systems research group at the Centrum Wiskunde & 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 he obtained both his MSc and PhD degrees in Computer Science, more specifically on the design and application estimation-of-distribution algorithms (EDAs). His current research position is mainly focused on fundamental EA research and on applications of EAs (in energy systems and the life sciences). Peter is best known for his status of active researcher in the area of EDAs since its upcoming and has (co-)authored over 60 publications in the field of evolutionary computation. At the GECCO conference, Peter has previously been track (co-)chair (EDA track, 2006, 2009), late-breaking-papers chair (2007), (co-)workshop organizer (OBUPM workshop, 2006; EvoDOP workshop, 2007; GreenGEC workshop, 2012, 2013) and (co-) local chair (2013).



    Metaheuristic Design Patterns (MetaDeeP)

    Over the last 20 years, Evolutionary Computation (and meta­- and hyper­-heuristics in general) has flourished, spawning an enormous variety of algorithms, operators and representations. The history of science and mathematics demonstrates that such proliferation is inevitable in a growing field, but that in order to progress non­incrementally it is periodically necessary to obtain a unifying perspective. Existing conceptual frameworks for EC/metaheuristics provide a certain level of abstraction, but these are typically limited in scope and not sufficient to capture higher ­level or cross­cutting concerns such as the automatic design of metaheuristics "in the large" (c.f. Dijkstra on software componentization), or (for example) the use of metrics derived from a population to drive online component selection.

    The "Design Patterns" revolution in 1994 was successful in addressing analogous issues in the software industry. The default level of discourse among practitioners was consequently significantly increased, and today “factory method” or “chain of responsibility” are software engineers’ lingua franca, immensely facilitating communication and design of software systems. The workshop organizers strongly believe that the EC/metaheuristics community needs and deserves a corresponding breakthrough. The domain of metaheuristics has good mathematical and conceptual foundations, so nothing precludes the creation a coherent and useful set of concepts that would help us to move our thinking about metaheuristics up an abstraction level. For instance, it can be formally shown that hyper-­heuristics are an instance of the well-­known composite pattern as applied to metaheuristics. The vision for framing of such Metaheuristic Design Patterns (MDP) has been advocated in a recent lecture [1] and several papers [2,3]; similar desires have also been expressed elsewhere [4].

    The goal of this workshop to provide a forum for those interested in contributing to the MDP vision and/or willing to demonstrate its usefulness in practical and theoretical studies. It is worth emphasizing that we envision this initiative as primarily bottom­-up, driven by ideas and needs of the community rather than by any arbitrary assumptions. A workshop including an interactive component seems thus to suit these needs perfectly.

    In a longer perspective, adopting the MDP approach to metaheuristics will deliver at least two significant advantages to the community: (i) much­ needed `refactoring' of old and new ideas and best practices into a catalog of abstract components, and (ii) a declarative specification of these components in a form amenable to the automated assembly of metaheuristics. This latter aspect is a key step in helping the community move away from the "hybridization by hand" and "persistent operator ­tweaking" approaches that have increasingly mired metaheuristic research.

     By publicizing this vision via the workshop, we hope to see it adopted by a large part of community and thus help to advance our domain as a whole. In an attempt to reach a wider audience, high quality submissions will be considered for inclusion in a forthcoming book by Springer.

    [1] Jerry Swan. Metaheuristic Design Patterns. Invited lecture at the Workshop on Evolutionary Computation for Automatic Design of Algorithms, GECCO’ 2013. [Slides].

    [2] Krzysztof Krawiec and Jerry Swan, Pattern-­guided genetic programming. GECCO 2013.

    [3] John R. Woodward and Jerry Swan, The automatic generation of mutation operators for genetic algorithms, GECCO 2012.

    [4] Natalio Krasnogor. Handbook of Natural Computation, chapter “Memetic Algorithms”. Natural Computing. Springer Berlin / Heidelberg, 2009.


    Jerry Swan.
    Before entering academia, Jerry spent 20 years in industry as a systems architect and software company owner. He obtained his PhD in Pure Mathematics (Computational Group Theory) from the University of Nottingham in 2006. His research interests lie at the intersection of software engineering, formal methods and optimization and include meta­- and hyper-­heuristics, symbolic computation and machine learning. He has published more than 30 papers in international journals and conferences and serves as a reviewer for numerous journals and program committees. Jerry has lectured and presented research worldwide and has been a presenter and co-­organizer of the GECCO "Automated Design of Algorithms" Workshop from 2011 to 2014. He currently works in the CHORDS Research Group at Stirling University.


    Krzysztof Krawiec
    Krzysztof (Chris) is an associate professor affiliated to Poznan University of Technology, Poland, where he leads the Computational Intelligence Group, a part of Laboratory of Intelligent Decision Support Systems. His current research interests include program synthesis, evolutionary computation, genetic programming, semantics in GP, modularity and coevolution. He published ~100 papers on these topics, and on applications of computational intelligence methods in image analysis, pattern recognition, and games. Krzysztof co-­chaired EuroGP ’13 and EuroGP 14, and is a member of the editorial board of Genetic Programming and Evolvable Machines journal.


    Chris Simons.
    Prior to obtaining his PhD in interactive evolutionary computation at the University of the West of England, Chris spent 15 years in the software industry in a variety of roles spanning software engineer and technical architect, to agile development methodology consultant and trainer. His research interests now lie in human-­centred bio­-inspired metaheuristics and Search­ Based Software Engineering (SBSE), researching with both the Artificial Intelligence Group and the Software Engineering Research Group at the University of the West of England, Bristol.


    John R. Woodward
    John R. Woodward is a lecturer at the University of Stirling, within the CHORDS group ( and is employed on the DAASE project (, and for the previous four years was a lecturer with the University of Nottingham. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, particularly Search Based Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming.  He has over 50 publications in Computer Science, Operations Research and Engineering which include both theoretical and empirical contributions, and given over 50 talks at International Conferences and as an invited speaker at Universities. He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities.

    John A Clark.
    John is a Royal Society Wolfson Research Merit Award holder (2013­2018). His interests are mostly in software and security. Major themes of research have addressed application of metaheuristic search to software testing, reverse engineering, low power algorithm development, quantum algorithm synthesis and most recently NMR pulse sequence generation. He is coauthor of 12 award winning papers. His work is currently sponsored by the EPSRC Dynamic Adaptive Automated Software Engineering (DAASE) programme grant (EP/J017515/1) and the UK Government.


    Medical Applications of Genetic and Evolutionary Computation (MedGEC)

    MedGEC is the GECCO Workshop on the application of genetic and evolutionary computation (GEC) to problems in medicine and healthcare. 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.

    New for GECCO MedGEC 2014

    "Commercialisation of GEC in Medicine"

    The Workshop Organisers will give a presentation on recent commercialisation of medical applications of GEC and anticipated future developments.


    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 Conference on Information Processing in Cells and Tissues (IPCAT) and guest editor for the subsequent special issue of BioSystems journal. More recently he was tutorial chair for the IEEE Congress on Evolutionary Computation (CEC) in 2009, local organiser for the International Conference on Evolvable Systems (ICES) in 2010 and co-general chair for the Ninth International Conference on Information Processing in Cells and Tissues (IPCAT) in April 2012.

    Steve and Stefano Cagnoni are co-founders and organizers of the MedGEC Workshop, which is now in its ninth 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 journal Genetic Programming and Evolvable Machines and a member of the editorial board for the International Journal of Computers in Healthcare and Neural Computing and Applications.

    Steve has some 75 refereed publications, is a Chartered Engineer and a fellow of the British Computer Society.


    Stefano Cagnoni  
    Stefano Cagnoni 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. Member of the Editorial Board of the journals “Evolutionary Computation” and “Genetic Programming and Evolvable Machines” .

    Since 1999, he has been chair of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing, now a track of the EvoApplications conference. Since 2005, he has co-chaired the MedGEC workshop at GECCO. Co-editor of special issues of journals dedicated to Evolutionary Computation for Image Analysis and Signal Processing. Reviewer for several international journals and is member of the program committees of several conferences.

    He has been awarded the "Evostar 2009 Award", in recognition of the most outstanding contribution to Evolutionary Computation.


    Robert M. Patton
    Dr. Patton received his PhD in Computer Engineering with emphasis on Software Engineering from the University of Central Florida in 2002. He joined the Computational Data Analytics group at Oak Ridge National Laboratory (ORNL) in 2003. His research at ORNL has focused on nature-inspired analytic techniques to enable knowledge discovery from large and complex data sets, and has resulted in approximately 30 publications pertaining to nature-inspired analytics and 3 patent applications. He has developed several software tools for the purposes of data mining, text analyses, temporal analyses, and data fusion, and has developed a genetic algorithm to implement maximum variation sampling approach that identifies unique characteristics within large data sets.


    Evolutionary Computation for Big Data and Big Learning

    We live in a time of unprecedented access to cheap and vast amounts of computational resources, which is producing a big leap forward in the fields of machine learning and data mining. We can tackle datasets of a scale (be it instances, attributes, classes, etc.) that was unimaginable some years ago, in what is well known as big data. On the other hand we can also use all these vast computational resources with the aim of understanding better our machine learning methods, by performing large scale evaluations, parameter sweeps, etc. We refer to the overall use  massive on-demand computation (cloud or GPUs) for machine learning as Big Learning. Evolutionary Machine Learning techniques are perfect candidates for big learning tasks due to their flexibility in knowledge representations, learning paradigms and their innate parallelism.

    The overall objective of this workshop is to assess the state of the art in evolutionary computation methods for big data and big learning.  To achieve this aim we will make a call for participants to present their methods at the workshop but also, and more importantly, to participate in a big data competition in which we will set up a uniform evaluation framework so that all participants are in the same conditions: uniform (very large) datasets and uniform computational resources. It will be through the results and lessons learnt in this competition that we will be able to show a very clear picture of the competence of evolutionary computation in the big learning world.

    The topics covered by this workshop, within the scope of evolutionary computation, contain (but are not limited to):

    • large ensemble learning in parallel
    • parameter sweeps in parallel
    • machine learning for high dimensional data
    • learning many-class classification problems (extreme class problems)
    • parallel cross-validation at scale
    • adaptive learning algorithms and online parameter tuning
    • machine learning for Big Data
    • GPGPUs for Machine Learning


    Jaume Bacardit
    Bacardit is a Senior Lecturer in Biodata Mining at Newcastle University. His research is focused on the development of (rule-based) machine learning methods for complex, large-scale, problems, and the application of these to biological/biomedical problems. He has published papers on algorithmic advances for rule-based machine learning related to tackling large dimensionality spaces, large sets of records or post-processing operators. His methods have been applied to a variety of biological/biomedical domains and technologies: -omics data, clinical records or protein structure prediction data. He has more than 40 refereed international publications between journal papers, conference papers and book chapters, has given 8 invited talks and co-edited two books. He accumulates 1100+ citations and an H-index of 18, according to Google Scholar.



    Ignacio Arnaldo
    Ignacio received a MEng in Computer Engineering and a PhD in Computer Science from the Universidad Complutense in Madrid, Spain in 2010 and 2013 respectively. In 2013, he joined the Anyscale Learning For All (ALFA) group at the Computer Science and Artificial Intelligence department at MIT as a postdoctoral associate. His research interests include Machine Learning, Evolutionary Computation, and parallel architectures with special focus on GPU computing.



    Kalyan Veeramachaneni
    Kalyan is a Research Scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL,MIT) and a Research Leader in Anyscale Learning For All (ALFA) group. He holds a PhD in Electrical Engineering from Syracuse University. ALFA focuses on the development and application of evolutionary algorithms in tandem with convex optimization and machine learning techniques for solving a spectrum of engineering and AI problems within the realms of Architecture, sensory evaluation, networks, circuits, embedded systems and parallel high-performance systems. He has more than 50 international publications and accumulates more than 750 citations and an H-index of 11 according to Google Scholar.


    Una-May O'Reilly
    Una-May is a Fellow of the International Society of Genetic and Evolutionary Computation, now ACM Sig-EVO. She holds a B.Sc. from the University of Calgary, and a M.C.S. and Ph.D. (1995) from Carleton University, Ottawa, Canada. She joined the Artificial Intelligence Laboratory as a Post-Doctoral Associate in 1996. Currently a principal research scientist at CSAIL, Una-May is leader of the Anyscale Learning For All (ALFA) group.  Una-May serves on the executive board of the ACM Sig-EVO. She was chair of the Genetic Programming track at GECCO 2003, co-chair of the 2004 European Conference on Genetic Programming and was chair of GECCO in 2005. She has served on the GECCO business committee since 2008. She co-led the 2006 and 2009 Genetic Programming: Theory to Practice Workshops. She is an associate and action editor of Genetic Programming and Evolvable Machines, and MIT Press Journal of Evolutionary Computation, and action editor for the Journal of Machine Learning Research.



    Women@GECCO Workshop

    Women form an under-represented cohort in evolutionary computation, whether the cohort is examined in industry, academics or both. This situation is reflective of a larger scale demographic divide in computer science and STEM[i] in general, recognized by organizations such as ACM and IEEE.  ACM are organizing this year the first womENcourage event to bring together students, early career researchers, and practitioners from the computing profession, while IEEE are embarking on an international leadership conference  for women in engineering. 

    Our goal at Women@GECCO is to bring women attending GECCO together to share in ways that will generate, encourage and support academic, professional and social opportunities for women in evolutionary computation.

    No manuscript submissions are required!

    Women participating at GECCO are invited to submit topics for discussion as outlined below.

    The discussion topics include, but are not limited to:

    • How can women be encouraged to enter the field of evolutionary computation?
    • What will help women in evolutionary computation remain in the field long term?
      • What are the different challenges along a career path?
      • What strategies will help women navigate career and family responsibilities?
      • What changes can be adopted by women as a group, by our larger community with respect to our conferences and awards, or by our academic institutions with respect to positions and promotions?
    • How can we promote evolutionary computation to women choosing graduate degrees?
    • How will industry’s need for evolutionary technology expertise be filled without more equal representation?
    • How can we efficiently disseminate evolutionary computation information to pre-college girls?
    • Are there experiences and strategies that can be shared which allow senior women to support more junior ones or peers to support each other?   Every women’s experience is different and shaped by cultural, society, institutional and personal influences.  Are there ways to support across these differences? Often, numbers are such that a woman can feel quite isolated. What can other women do to help?

    Workshop format
    A number of relevant topics proposed in advance by participants will be introduced at the workshop and short group discussion will follow. Workshop participants can choose in which break out discussion they want to participate. A panel discussion on work-life balance will take place in the evening of the Women@GECCO workshop.

    Call for participation 
    No manuscript submission is expected.  All women attending GECCO are invited and encouraged to attend. Specific registration details will be available soon.

    We welcome attendees to propose to lead one or more discussion topics. In your proposal, outline the topic, why you think it is important to the workshop's focus and how you would lead discussion around it in a 10-30 minute time interval. We encourage all modes of discussion including (but not limited to) break outs, role playing, and panels.  Details on how to  submit your proposal will be posted soon.

    Panel discussion 
    We will have several panelists at different stages of their career who are all happy to share their experience on how to combine a successful research career with a rich private life. Among other topics we will discuss the following questions:

    • Is it more difficult for women to pursue a research career in EC?
    • Or is it rather, independently of the gender, difficult per se to combine ambitious career plans (in academia or industry) with a satisfying private life?
    • What are tips to ease a good work and life balance?
    • How does having a family change the priorities between private life and research ambitions?
    • What can the community do to support academics (and in particular young women) with families ?

    The names of the panelists are to be announced soon. If you have any topics that you would like to see discussed in this panel, please do not hesitate to contact the organizers Anne Auger () and Carola Doerr ().

    [i] STEM is an (American) acronym that stands for Science, Technology, Engineering and Mathematics


    Una-May O'Reilly
    Una-May is a Fellow of the International Society of Genetic and Evolutionary Computation, now ACM Sig-EVO.  In 2013 she received the EVO-Star award recognizing her contributions to evolutionary computation. At MIT CSAIL, Una-May is leader of the AnyScale Learning For All (ALFA) group.  ALFA focuses on scalable machine learning techniques, their deployment on different computational resource models and application to real-world domains.  Una-May is Vice-Chair of ACM Sig-EVO. She was chair of the Genetic Programming track at GECCO 2003, co-chair of the 2004 European Conference on Genetic Programming and was chair of GECCO in 2005. She served on the GECCO business committee 2008-2011. She co-edited the 2006 and 2009 Genetic Programming: Theory to Practice Workshops. She serves as the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines (Kluwer), editor for MIT Press Journal of Evolutionary Computation, and action editor for the Journal of Machine Learning Research.  In 2013, with Anna Esparcia, Anikó Ekárt and Gabriela Ochoa, she inaugurated the Women@GECCO meeting and chairs the group.


    Anna Esparcia
    Anna I Esparcia-Alcázar is Head of R&D at S2 Grupo. She holds a degree in Electrical Engineering from the Universidad Politecnica de Valencia (UPV), Spain, and a PhD from the University of Glasgow, UK. She has ample experience both in industry and academia. For the past 8 years she has been actively involved in the organisation of the two main conferences in the field of Evolutionary Computation, evostar and GECCO. She is Senior Member of the IEEE, Member of the ACM and elect member of the Executive Committee of SIGEVO.


    Anikó Ekárt
    Anikó is a senior lecturer at Aston University. She co-chaired the European Conference on Genetic Programming in 2010, co-chaired and then chaired the Genetic Programming track at GECCO in 2012 and 2013, respectively.  She is a member of the editorial board of the Genetic Programming and Evolvable Machines (GPEM) and the Neural Computing and Applications (NCA) journals.



    Gabriela Ochoa
    Gabriela is a Lecturer in Computing Science at the University of Stirling, Scotland.  She received BSc and MSc degrees in Computer Science from University Simon Bolivar, Venezuela and a PhD from University of Sussex, UK.  She worked in industry for 5 years before joining academia, and has held faculty and research positions at the University Simon Bolivar, Venezuela and the University of Nottingham, UK.  Her research interests lie in the foundations and application of evolutionary algorithms and heuristic search methods, with emphasis on autonomous (self-*) search, hyper-heuristics, fitness landscape analysis, and applications to healthcare, scheduling and software engineering. She has published over 70 scholarly papers and serves various program committees. She is associate editor of Evolutionary Computation (MIT Press), was involved in founding the Self-* Search track atGECCO, proposed the first Cross-domain Heuristic Search Challenge (CHeSC 2011)  and is chairing EvoCOP 2014.


    Carola Doerr
    Carola and Anne Auger of INRIA Saclay will organize the panel on work-life balance strategies as part of Women@GECCO. Carola is a permanent researcher with CNRS and the Université Pierre et Marie Curie (Paris 6). She studied mathematics at Kiel University (Diploma in 2007) and computer science at the Max Planck Institute for Informatics and Saarland University (PhD in 2011). From Dec. 2007 to Nov. 2009, Carola Doerr has worked as a business consultant for McKinsey & Company, mainly in the area of network optimization. She was a post-doc at the Université 7 in Paris and the Max Planck Institute for Informatics in Saarbrücken. Her PhD studies were supported by a Google Europe Fellowship in Randomized Algorithms and her thesis has has been awarded the Otto-Hahn medal of the Max Planck Society.

    Carola Doerr's main research interest is in the theory of randomized algorithms, both in the design of efficient algorithms as well as in randomized query complexities. She has published several papers in the field of evolutionary computation.


    6th Symbolic Regression and Modeling Workshop


    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 formula 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.

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



    GECCO 2013 site   GECCO 2012 site      GECCO 2011 site    GECCO 2010 site    GECCO 2009 site   GECCO 2008 site   
        GECCO 2007 site     GECCO 2006 site    GECCO 2005 site        GECCO 2004 site    GECCO 2003 site  
         GECCO 2002 site      GECCO 2001 site      GECCO 2000 site      GECCO 1999 site