Message from the Workshops Chair
1 -Workshop on Problem Understanding and Real-World Optimisation
Kent McClymont -
Ed Keedwell -
[ summary | details ]
2 - Fifth half-day indusrty-oriented workshop on Symbolic Regression and Modeling
Steven Gustafson -
Ekaterina Vladislavleva -
[ summary | details ]
3- Black Box Optimization Benchmarking 2013 (BBOB 2013)
Anne Auger -
Bernd Bischl -
Dimo Brockhoff -
Nikolaus Hansen -
Olaf Mersmann -
Petr Posík -
Heike Trautmann -
[ summary | details ]
Muhammad Iqbal -
Kamran Shafi -
[ summary | details ]
5- Evolutionary Computation Software Systems (EvoSoft)
Stefan Wagner -
Michael Affenzeller -
[ summary | details ]
6- Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2013)
David Walker -
Richard Everson -
Jonathan Fieldsend -
[ summary | details ]
7- Evolutionary computation and Multi-Agent Systems and Simulation (EcoMass) Seventh Annual Workshop
Forrest Stonedahl -
William Rand -
[ summary | details ]
8- Medical Applications of Genetic and Evolutionary Computation (MedGEC)
Stephen L. Smith -
Stefano Cagnoni -
Robert M. Patton -
[ summary | details ]
9- 3rd Workshop on Evolutionary Computation for the Automated Design of Algorithms
Gisele L. Pappa
John Woodward -
Jerry Swan -
Krzysztof Krawiec -
[ summary | details ]
10- Green and Efficient Energy Applications of Genetic and Evolutionary Computation Workshop
Peter A.N. Bosman -
[ summary | details ]
11- International Workshop on Evolutionary Computation in Bioinformatics
Jose M. Chaves-Gonzalez
David L. Gonzalez-Alvarez
[ summary | details ]
12- Stack-based Genetic Programming
[ summary | details ]
13- Student Workshop
Tea Tusar -
[ summary | details ]
|1.- Workshop on Problem Understanding and Real-World Optimisation
This workshop sees the joining of two related workshops to reﬂect the link between stronger methods in problem understanding and the signiﬁcant importance of scientiﬁc approaches to tackling real-world optimisation problems. This workshop will address the wider topic of scientiﬁc methods for analysing and solving difficult and real problems currently faced by optimisation researchers.
The workshop will continue the themes of last year's GECCO-UP workshop and aims to provide a forum for the discussion and exploration of methods for the analysis, creation and synthesis of optimisation problems through both theoretical and experimental methods. A crucial aspect of this research is developing problems that reflect the complexity and idiosyncrasies of real-world optimisation problems. Therefore, the workshop will open with a session focused on real-world optimisation problems, in which speakers from both industry and academia discuss their experiences of what makes real-world problems difficult. The workshop as a whole provides an opportunity for discussion and interactions that might move towards bridging the gap between those problems addressed by scientific research and the types of problem addressed by commercial optimisation software vendors.
Furthermore, the continued the ethos of open debate of the previous workshops will be upheld in a discussion session held at the end of the workshop which will give an opportunity for participants to discuss and present recently published research and position papers.
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;
- development of new benchmark sets of data that include relevant real-world constraints to demonstrate relevance of academic techniques;
- metrics by which problems should be evaluated that are relevant to practitioners;
- methods for comparing algorithms that address concerns of practitioners;
- discussion of real-world problem constraints.
See links below for the workshop programme and more information on the workshop:
is an Associate Research Fellow in Computer Science at
the University of Exeter. His research is focused on the study of
optimisation problem analysis and multi-objective hyper-heuristic
methods for solving hard real-world optimisation problems. Kent has a
specific interest in heterogeneous encodings for which he has
published a novel test problem suite and is continuing work in this
area. He is a member of AISB committee and was is a member of the AISB
2013 Convention's organising committee and has chaired sessions at
previous GECCO and IEEE CEC.
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 is co-chair for the
AISB2013 Convention and is organizer of a symposium for AISB 2013 and
chaired sessions at previous AISB conventions and IEEE CEC 2010.
is an experienced researcher in the area of optmisation and hyper-heuristics, being one of the original authors of the term'hyper-heuristics' in the early 2000s. She has published widely on the use of evolutionary algorithms applied to scheduling and timetabling problems, as well as in hyper-heuristics applied to a variety of domains. She is currently the holder of a large national research grant, investigating the use of hyper-heuristics that continuously learn and improve over time, which has collaborators from the logistics and forestry industry.
is a researcher who works in the field of hyper-heuristics and problem-classification algorithms, and works with Prof. Hart on a large national grant on hyper-heuristics. He is also co-chair of a workshop at Evo-Star 2013 called EvoIndustry.
|2.- Fifth half-day indusrty-oriented workshop on Symbolic Regression and Modeling
The Fifth Symbolic Regression and Modeling (SRM) 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.
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 ³AI¹s 10 to
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.
|3.- Black Box Optimization Benchmarking 2013 (BBOB 2013)
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 and deterministic continuous optimization
algorithms in an anytime scenario. The new edition will follow the BBOB 2009,
2010 and 2012 GECCO workshops (http://coco.gforge.inria.fr/). Those previous
editions have resulted in (1) collecting data of various optimizers (32 in
2009, 25 in 2010, 30 in 2012) 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 editions is visible in the EC community but also in the
mathematical optimization community where BBOB results start now to be
With a new edition, we would like to build on the success of the
previous workshops and increase and diversify the data collection we
already have. In addition we would like to focus on expensive
optimization and collect data of optimizers that are especially
tailored for problems where only a small budget of function evaluations
is affordable. As for the previous editions, we will provide code for running
experiments in different languages (C, Matlab, Java, R, Python) and
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.
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 and the BBOB workshops in 2009, 2010 and 2012.
is currently a Research Associate at the Statistics
Faculty, TU Dortmund, Germany. He studied computer science in Hamburg
(Germany) and data science in Dortmund (Germany) where he received his
M.Sc. degree in 2009. His research interests focus mainly on machine
learning, especially model selection and optimizing machine learning
systems. But he is also interested in using machine learning
techniques to improve optimization methods. He was involved in
organizing the "Joint Workshop on Automated Selectio and Tuning of
Algorithms" at PPSN 2012.
received his diploma in computer science from
University of Dortmund, Germany in 2005 and his PhD (Dr. sc. ETH) from
ETH Zurich, Switzerland in 2009. Afterwards, he held two postdoctoral
research positions in France at INRIA Saclay Ile-de-France (2009-2010)
and at Ecole Polytechnique (2010-2011). Since November 2011, he has
been a permanent researcher at INRIA Lille - Nord Europe, France. His
research interests are focused on evolutionary multiobjective
optimization (EMO), in particular on theoretical aspects of
indicator-based search and performance assessment and in the
benchmarking of blackbox algorithms in general.
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).
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.
received 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
is a postdoctoral researcher at the Statistics
Department, TU Dortmund, Germany. She graduated in Statistics and,
after working in a consulting company for two years, she joined the
Graduate School of Production Engineering and Logistics at the TU
Dortmund and received her PhD in 2004. Her current research activities
are focused on multiobjective (evolutionary) optimisation -- in
particular preference incorporation, performance assessment and
stopping criteria -- as well as benchmarking concepts and Exploratory
Landscape Analysis (ELA). She was involved in organizing the special
session "Designing Evolutionary Processes" at CEC 2010 and the "Joint
Workshop on Automated Selection and Tuning of Algorithms" at PPSN
|4.- Sixteenth International Workshop on Learning Classifier Systems
Since Learning Classifier Systems (LCSs) were introduced by Holland  as a way of applying evolutionary computation to machine learning problems, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining to automated innovation and on-line control. Classifier systems are a very active area of research, with newer approaches, in particular Wilson's accuracy-based XCS , receiving a great deal of attention. LCS are also benefiting from advances in reinforcement learning and other machine learning techniques.
received his Master in Computer Science at the National University of Sciences and Technology (NUST), Pakistan, in 2008. He conducted research on “Image Fusion” in his Master research thesis. He joined COMSATS Institute of Information Technology, Pakistan, as a Lecturer in the Department of Computer Science, in August 2008. He supervised research thesis students in Image Fusion and Object Recognition using Evolutionary Computational Techniques. He is now working with Dr. Will Browne and Prof. Mengjie Zhang at the School of Engineering and Computer Science at Victoria University of Wellington, New Zealand, on Learning Classifier Systems (LCSs), with particular emphasis on extracting and using building blocks of knowledge to develop a scalable classifier system. He has ten publications, including four on the LCS in his first two years PhD research work. He has been invited for a talk in the 2012 International Workshop on Learning Classifier Systems (IWLCS 2012), and has served as a reviewer for the 2012 IEEE Congress on Evolutionary Computation (CEC 2012).
He 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).
He holds a Ph.D in 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 8 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.
|5.- 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
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.
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).
|6.- Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2013)
The fourth workshop on Visualisation in Genetic and Evolutionary Computation (VizGEC 2013) provides an opportunity for researchers to promote current visualisation developments in the area of genetic and evolutionary computation. Visualisation is an important 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 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).
The workshop will include the presentation of papers, however we hope to take advantage of the workshop format to allow researchers to demonstrate their visualisation tools. This is intended to enable participants to interact with visualisation tools at the forefront of the evolutionary computation field and encourage discussion. Previous years' workshops have been well attended and have featured a diverse selection of topics, which we hope to continue this year. We look forward to seeing you in Amsterdam.
is an Associate Research Fellow with the Department of Computer Science and the Centre for Water Systems at the University of Exeter. He recently submitted his PhD, 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. More recently, his research has also investigated evolutionary methods for the data mining of many-objective populations. His general research interests include evolutionary problem solving, particularly machine learning problems, techniques for identifying preference information in data and visualisation methods.
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 in 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.
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.
|7.- Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS) Seventh Annual Workshop
Genetic and 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 (sometimes called agent-based modeling (ABM)) addresses the bottom-up issue of how collective behavior emerges from individual action. Likewise, the study of evolutionary dynamics (particularly in coevolution) 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 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.
Topics relevant to the proposed workshop include, but are not limited to:
-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 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
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.
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, and GECCO-2011, he was also chair of all workshops for GECCO-2012.
|8.- 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.
Stephen L. Smith
received a BSc in Computer Science and then an MSc and PhD in Electronic
Engineering from the University of Kent, UK. He is currently a senior
lecturer in the Department of Electronics at the University of York, UK.
Steve's main research interests are in developing novel representations
of evolutionary algorithms particularly with application to problems in
medicine. His work is currently centered on the diagnosis of
neurological dysfunction and analysis of mammograms. Steve was program
chair for the Euromicro Workshop on Medical Informatics, program chair
and local organizer for the Sixth International 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.x
Steve has some 75 refereed publications, is a Chartered Engineer and a
fellow of the British Computer Society.
graduated in Electronic Engineering at the University of Florence in 1988 where he has been a PhD student and a post-doc until 1997.
In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology.
Since 1997 he has been with the University of Parma, where he has been Associate Professor since 2004.
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, signal processing and pattern recognition, presently a track of the EvoApplications Conference held yearly as part of Evostar.
In 2009 he was presented with the "Evostar Award", in recognition of the most outstanding contribution to Evolutionary Computation.
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).
|9.- 3rd Workshop on Evolutionary Computation for the Automated Design of Algorithms
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.
Gisele L. Pappa
received her PhD in Computer Science from the
University of Kent, Canterbury, UK, in 2007. She is currently
an Associate Professor at the Federal University of Minas
Gerais, Brazil. She is the author of a research-oriented book
on data mining and evolutionary algorithms, and her current
research interests are on data mining, bio-inspired
computational intelligence algorithms and social networks.
has a BSc in Theoretical Physics, an MSc (with distinction)
in Cognitive Science 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.
After spending four years teaching at the University of Nottingham,
Ningbo, campus based in China, he returned to Stirling University in the UK,
working for the Computational Heuristics,
Operations Research and Decision Support (CHORDS)
Research Group. His research interests include
fundamental issues in Machine Learning especially Genetic
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 Computational Heuristics,
Operations Research and Decision Support (CHORDS)
Research Group at Stirling.
Krzysztof (Chris) Krawiec
is an associate professor affiliated to
the Laboratory of Intelligent Decision Support Systems, Institute of Computing Science,
Poznan University of Technology, Poland. His current research interests include modularity,
the role of interactions in coevolution, and the role of semantics in genetic programming,
in particular in autonomous problem decomposition and operator design. He is co-chair
of EuroGP 2013, the 16th European Conference on Genetic Programming and a member
of (amongst others) the editorial boards of Genetic Programming and Evolvable Machines
and the SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation.
|10.- Green and Efficient Energy Applications of Genetic and Evolutionary Computation
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. 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.
He received his Ph.D. diploma in
Computer Science in 2009 from the Universtiy of Lille. He was
a member of INRIA Lille - Nord Europe (French National
Institute for Research in Computer Science and Control, Lille
branch) DOLPHIN Team, and of the Fundamental Computer Science
Laboratory of Lille (LIFL). He was involved in the ANR
Docking@GRID and the ANR CHOC projects (NSF French
equivalent). From September 2009 until March 2010 he was a
PostDoctoral Researcher in the Advanced Learning Evolutionary
Algorithms (ALEA) Team, INRIA Bordeaux - Sud-Ouest, France,
working on parallel and distributed techniques for
interacting Markov chains based modeling and development.
During his stay at INRIA Bordeaux - Sud-Ouest he co-organized
the ALEA working group and he was a member of the organizing
committee for the "Evolutionary Algorithms - Challenges in
Theory and Practice" and the "Rare Events Simulation"
Workshops, held in Bordeaux, in March, respectively November
2010. He addressed topics ranging from evolutionary
computation and optimization, parallel and distributed
algorithms and Monte Carlo based algorithms with applications
in general optimization problems, bio-informatics and rare
events simulation. He collaborated with the Atomic Energy
Commission (CEA Life Sciences Division and CEA CESTA), the
Biology Institute of Lille (IBL) and the Sea French Research
Since the 1st of April 2010, Dr.
TANTAR is a PostDoctoral Researcher at the Computer Science
and Communications (CSC) Research Unit, University of
Luxembourg (AFR Grant). He is currently involved in the
GreenIT (FNR Core 2010-2012) project which aims at providing
a holistic autonomic energy-efficient solution to manage,
provision and administer the various resources of Cloud
Computing / HPC centers. Dr. TANTAR also animates and
co-organizes the GRIPHON working group at CSC which regroups
several topics on green and energy efficient computing. He is
also participating to the Carbon Neutral ICT Operations
program at the University of Luxembourg, Luxembourg, an
interdisciplinary project aiming at providing solutions for a
carbon-free environment for the Belval Campus, to be
constructed for the University of Luxembourg. His current
research interests address the modeling and the optimization
of large scale dynamic systems having energy efficiency as a
She received her Diploma degree in 2003 and MsC in 2005 in
the field of Computational Optimization, both from the
Computer Science Faculty at the "Al. I. Cuza University" in
Iasi, Romania. In 2005 she joined the French National
Institute for Research in Computer Science and Control(INRIA)
in Lille. She was awarded the PhD title for Landscape
analysis in multi-objective optimization in 2009 at the
University of Lille 1. Between 2007 and 2009 she hold a
lecturer position at the same university. During her PhD she
was also awarded an INRIA Explorateurs scholarship to the
CWI, Amsterdam, Netherlands. She developed a strong interest
on new challenging aspects regarding landscape analysis in
multi-objective, but also the theoretical foundations of
stochastic methods and their scaling to practical problems.
Before joining the CSC research unit, at the University of
Luxembourg, in October 2010, she was an INRIA post-doctoral
researcher in the Advanced Learning Evolutionary Algorithms
(ALEA) team, at INRIA Bordeaux, dealing with performance
guarantees factors for multi-objective particle methods, such
as evolutionary algorithms and rare event simulation
Emilia was actively involved in the
dissemination of research through the organization of the 1st
Workshop on Evolutionary Algorithms - Challenges in Theory
and Practice, supported by the EA association, France. It
aimed at providing a unified view over the developments in
evolutionary computation through different research fields,
as computer science, mathematics and physics. Her main
research interests concern the performance guarantee factors
for online dynamic multi-objective optimization appearing in
energy efficient optimization. This is motivated through the
study of the stability of existing Â approaches by means of
evolutionary particle like paradigms. Emilia is currently
co-authoring with Oliver Schutze a book in Springer series,
dealing with performance guarantees and landscape analysis in
Peter A.N. Bosman
is a senior researcher in the research
group Multi-agent and Adaptive Computation 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,
revenue management 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 50 publications in
the field of evolutionary computation. At the GECCO
conference, Dr. Bosman has previously been track (co-)chair
(EDA track, 2006, 2009), late-breaking-papers chair (2007)
and (co-)workshop organizer (OBUPM workshop, 2006; EvoDOP
workshop, 2007; GreenGEC workshop, 2012).
|11.- International Workshop on Evolulutionary Computation in Bioinformatics
Numerous problems encountered in Bioinformatics can be formulated as optimization problems, and thus lend themselves to the application of very diverse Evolutionary Computation techniques. In fact, Bioinformatics is one of the most exciting research areas in which Evolutionary Computation finds application.
In conclusion, we seek original, high-quality research papers, clearly focused on the application of Evolutionary Computation to any possible Bioinformatics problem. In particular, contributions are solicited on, but are not limited to, the following topics:
- Multiobjective Optimization in Bioinformatics.
- Parallel and Distributed Evolutionary Techniques in Bioinformatics.
- Swarm Intelligence in Bioinformatics.
- Metaheuristics in Bioinformatics.
- Evolutionary and Bio-inspired Algorithms in Bioinformatics.
- Artificial Immune Systems in Bioinformatics.
- Machine Learning and Data Mining in Bioinformatics.
- Fuzzy and Neural Systems in Bioinformatics.
- Classification and Decision Making in Bioinformatics.
With regard to the Bioinformatics problems, many different alternatives exist: biological sequence analysis, comparison and alignment; motif, gene and signal recognition/discovery; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and protein; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; microarray design and analysis; proteomics; functional genomics; molecular docking; design of DNA sequences for DNA computing; etc.
Miguel A. Vega-Rodríguez
received his Ph.D. degree in Computer Science from the University of Extremadura, Spain, in 2003.
He is currently an Associate Professor of computer architecture in the Department of Computer and Communications Technologies, University of Extremadura. He has authored or co-authored more than 450 publications including journal papers, book chapters, and peer-reviewed conference proceedings. His main research interests include evolutionary computation, bioinformatics, parallel and distributed computing, and reconfigurable computing.
Dr. Vega-Rodríguez is an editor and reviewer of diverse international JCR impact factor journals. Furthermore, he has been guest editor in 6 special issues in different international JCR impact factor journals. He is also member of the Program Committee of diverse international conferences, chairing sessions in different international conferences.
A more detailed biography can be found at: http://arco.unex.es/mavega
Jose M. Chaves-González
received his Ph.D. degree in Computer Science from the University of Extremadura, Spain, in 2011.
He is currently a Senior Research Fellow within the Computer Architecture and Logic Design research group at University of Extremadura. He has authored or co-authored more than 35 publications, including several JCR impact factor journals. Moreover, he is a reviewer of diverse international JCR journals. His research interest include evolutionary computation, multiobjective optimization, heuristic searches, hyper-heuristics, metaheuristics, parallel computing, and operational research. At present he is working in the development of reliable DNA sequences which can be applied to bio-molecular computing.
A more detailed biography can be found at: http://arco.unex.es/jm
David L. González-Álvarez
He is a final year Ph.D. candidate at the Department of Computer and Communications Technologies, University of Extremadura. He graduated in Computer Engineering from the University of Extremadura, Spain, in 2009, and obtained his MS degrees in 2010 and 2012 at the same University. His research interests are directly related to evolutionary and bio-inspired algorithms, multiobjective optimization, and parallel and distributed computing applied for solving important Bioinformatics problems. He has authored or co-authored more than 20 publications, including 3 Journal Citation Report (JCR) papers and has reviewed some manuscripts for over 5 different important journals, including the following ones: IEEE Transactions on Evolutionary Computation, IEEE Transactions on Systems, Man and Cybernetics, Part B - Cybernetics, or Engineering Applications of Artificial Intelligence.
A more detailed biography can be found at: http://arco.unex.es/dlga
Sergio Santander-Jiménez is currently a PhD candidate at the Department of Computer and Communications Technologies, University of Extremadura. He graduated in Computer Engineering from the University of Extremadura, Spain, in 2010, and received his MS degree in 2011. He has collaborated with international JCR impact factor journals, reviewing articles concerning bioinformatics and computational biology. His main research interests include evolutionary and bio-inspired computing, multiobjective optimization, parallel and distributed computing, and their applications to real-world biological problems.
A more detailed biography can be found at: http://arco.unex.es/sesaji
|12.- Stack-based Genetic Programming
Since its introduction in the early 1990's, stack-based Genetic Programming has grown into a significant
sub-field of GP through the independent work of multiple researchers. While generally characterized by
the explicit use of stacks to support the evolution and evaluation of programs, there is significant
diversity in the various stack-based approaches with respect to program expression, data typing, and
evolutionary manipulation. The intent of this workshop is to provide a collaborative forum and
discussion venue for practitioners of (and parties interested in) stack-based GP. In this first workshop,
we will explore the current state of the art through a combination of paper presentations and extended
discussions on subjects of interest to the stack-based GP community. Example topics may include the
use of single versus multiple stacks, approaches for handling data types and data structures, program
expression syntax, effectiveness of evolutionary methods, and problem space affinity. The forum will
encourage participants to consider advantages and disadvantages of dissimilar approaches and to share
examples of both failures and successes in the development, application, and advancement of stack-
is a Professor of Computer Science in the School of
Cognitive Science at Hampshire College in Amherst, Massachusetts,
and an adjunct professor in the Department of Computer Science at
the University of Massachusetts, Amherst. He received a B.A. in
Philosophy from Oberlin College in 1984 and a Ph.D. from the
Department of Computer Science at the University of Maryland in
His areas of teaching and research include genetic and evolutionary
computation, quantum computation, and a variety of intersections
between computer science, cognitive science, evolutionary biology,
and the arts. He is the Editor-in-Chief of the journal Genetic
Programming and Evolvable Machines (published by Springer) and a
member of the editorial board of Evolutionary Computation (published
by MIT Press). He is also a member of the SIGEVO executive
committee and he was named a Fellow of the International Society
for Genetic and Evolutionary Computation.
has been involved in Genetic Programming since 1994,
and has produced a Ph.D. thesis and numerous articles on the topic.
Maarten has been involved in the organization of the field with various
roles in EuroGP, GECCO and the evolutionary computation journals.
His main interests have
been in symbolic regression and strongly
typed genetic programming, with a strong focus on practical, scientific,
applications. He has worked on Standard GP, on Grammatical
Evolution and on stack based
approaches such as Push. Maarten
currently holds a position as senior director of product management
for decision analytics at Pegasystems Inc.
is an Institute Analyst at Southwest Research Institute
(SwRI) in San Antonio, Texas.
He works primarily in the Signal
Exploitation and Geolocation division where his principal areas of
interest include signal processing, distributed software design,
human computer interface design, data
analysis, and data mining.
He is the principal developer for the Genetic Programming
Fifth (GPE5), a stack-based genetic programming
system, and has published several papers on using
GPE5 to solve
real-world problems. He received a B.S. degree in Chemical
Engineering from the
University of Florida, and M.S. and Ph.D.
degrees in Computer Science from the University of Texas at
|13.- Student Workshop
*Open to all students, graduates and undergraduates
The student workshop at GECCO 2013 aims to encourage graduate and undergraduate students to submit and present their work at the GECCO (Genetic and Evolutionary Computation Conference) 2013 in Amsterdam, NL. This full day workshop will assist students with their research work and facilitate their inclusion in the EC community.
Students presenting their work will receive rich feedback on the quality of their work and presentation style. This will be assured by a question and answer period after each talk and a 'mentor' panel of established researchers.
In addition, it will be possible to:
... so, submit your work or encourage your students to do so!
- present the work to the whole conference audience at a poster session,
- discuss with other participants at a get together after the workshop, and,
- receive a best paper award
ACM and SIG-EVO is anxious to attract junior scientists to get in touch
with their research communities. This is documented by special rates for
students as well as associated workshops, which alleviate contacting the
Graduate and Undergraduate Student Workshop
Students have the possibility to present their work on a dedicated Student Workshop. For more information on the workshop itself, submission, and related deadlines see http://gecco2013studentws.tiddlyspace.com
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.
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 two SMEs. He always stayed in contact to academia and received a post-doc position at Cologne University of Applied Science (CUAS) directly 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 multi-objective (evolutionary) optimization, in particular hypervolume based algorithms, and (industrial) applicability of the explored methods.
|Notifcation of paper acceptance
|| March 14
|Workshop paper submission deadline
|Workshop paper decision notification sent
|Camera-ready submission deadline for accepted workshop papers
** REVIEWING **
Workshop proposals will be reviewed by the GECCO 2013
organizing committee, based on the GECCO attendees' likely interest
in them, the breadth and depth of the topic(s), and the expertise
and credentials of the instructor(s)/organiser(s).
Workshops Information and Submissions: Mike Preuss
Tutorials Information and Submissions: Gabriela Ochoa