Kent McClymont
He 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.

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

Emma Hart
She 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.

Kevin Sim
He 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.

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 ³AI¹s 10 to Watch² 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.

Anne Auger
She is a permanent researcher at the French National Institute for Research in Computer Science and Control (INRIA). She received her diploma (2001) and PhD (2004) in mathematics from the Paris VI University. Before to join INRIA, she worked for two years (2004-2006) at ETH in Zurich. Her main research interest is stochastic continuous optimization including theoretical aspects and algorithm designs. She is a member of ACM-SIGECO executive committee and of the editorial board of Evolutionary Computation. She has been organizing the biannual Dagstuhl seminar "Theory of Evolutionary Algorithms" in 2008 and 2010 and the BBOB workshops in 2009, 2010 and 2012.

Bernd Bischl
He 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.

Dimo Brockhoff
He 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.

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

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

Petr Posík
He 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 evolutionary-computation field.

Heike Trautmann
She 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 2012.

Muhammad Iqbal
He 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).

Kamran Shafi
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).

Ryan Urbanowicz
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.

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).

David Walker
He 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.

Richard Everson
He is Associate Professor of Machine Learning at the University of Exeter. He has a degree in Physics from Cambridge University and a PhD in Applied Mathematics from Leeds University. He worked at Brown and Yale Universities on fluid mechanics and data analysis problems until moving to Rockefeller University, New York, to work on optical imaging and modelling of the visual cortex. After working at Imperial College, London, he joined the Computer Science department at Exeter University. His research interests lie 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.

Jonathan Fieldsend
He is Lecturer of Computer Science at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has held 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.

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, and GECCO-2011, he was also chair of all workshops for GECCO-2012.

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

Stefano Cagnoni
He graduated in Electronic Engineering at the University of Florence in 1988 where he has been a PhD student and a post-doc until 1997.
In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology.
Since 1997 he has been with the University of Parma, where he has been Associate Professor since 2004.
Recent research grants regard: co-management of a project funded by Italian Railway Network Society (RFI) aimed at developing an automatic inspection system for train pantographs; a "Marie Curie Initial Training Network" grant, for a four-year research training project in Medical Imaging using Bio-Inspired and Soft Computing; a grant from "Compagnia di S. Paolo" on "Bioinformatic and experimental dissection of the signalling pathways underlying dendritic spine function".
He has been Editor-in-chief of the "Journal of Artificial Evolution and Applications" from 2007 to 2010. Since 1999, he has been chairman of EvoIASP, an event dedicated to evolutionary computation for image analysis, 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).

Gisele L. Pappa
She received her PhD in Computer Science from the
University of Kent, Canterbury, UK, in 2007. She is currently
an Associate Professor at the Federal University of Minas
Gerais, Brazil. She is the author of a research-oriented book
on data mining and evolutionary algorithms, and her current
research interests are on data mining, bio-inspired
computational intelligence algorithms and social networks.

John Woodward
He has a BSc in Theoretical Physics, an MSc (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 Programming.

Jerry Swan
Prior to obtaining a PhD in computational group theory at
Nottingham, Jerry Swan has spent 20 years in industry as a
software developer. He was the owner of a computer games
company for most of the 1990s and has worked in areas as
diverse as logistics and generative music. His research
interests include hyper-heuristics, symbolic computation and
machine learning. He now works at the Computational Heuristics,
Operations Research and Decision Support (CHORDS)
Research Group at Stirling.

Krzysztof (Chris) Krawiec
He 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.

Alexandru-Adrian Tantar
He received his Ph.D. diploma in Computer Science in 2009 from the Universtiy of Lille. He was a member of INRIA Lille - Nord Europe (French National Institute for Research in Computer Science and Control, Lille branch) DOLPHIN Team, and of the Fundamental Computer Science Laboratory of Lille (LIFL). He was involved in the ANR Docking@GRID and the ANR CHOC projects (NSF French equivalent). From September 2009 until March 2010 he was a PostDoctoral Researcher in the Advanced Learning Evolutionary Algorithms (ALEA) Team, INRIA Bordeaux - Sud-Ouest, France, working on parallel and distributed techniques for interacting Markov chains based modeling and development. During his stay at INRIA Bordeaux - Sud-Ouest he co-organized the ALEA working group and he was a member of the organizing committee for the "Evolutionary Algorithms - Challenges in Theory and Practice" and the "Rare Events Simulation" Workshops, held in Bordeaux, in March, respectively November 2010. He addressed topics ranging from evolutionary computation and optimization, parallel and distributed algorithms and Monte Carlo based algorithms with applications in general optimization problems, bio-informatics and rare events simulation. He collaborated with the Atomic Energy Commission (CEA Life Sciences Division and CEA CESTA), the Biology Institute of Lille (IBL) and the Sea French Research Institute (IFREMER). Since the 1st of April 2010, Dr. TANTAR is a PostDoctoral Researcher at the Computer Science and Communications (CSC) Research Unit, University of Luxembourg (AFR Grant). He is currently involved in the GreenIT (FNR Core 2010-2012) project which aims at providing a holistic autonomic energy-efficient solution to manage, provision and administer the various resources of Cloud Computing / HPC centers. Dr. TANTAR also animates and co-organizes the GRIPHON working group at CSC which regroups several topics on green and energy efficient computing. He is also participating to the Carbon Neutral ICT Operations program at the University of Luxembourg, Luxembourg, an interdisciplinary project aiming at providing solutions for a carbon-free environment for the Belval Campus, to be constructed for the University of Luxembourg. His current research interests address the modeling and the optimization of large scale dynamic systems having energy efficiency as a main objective.

Emilia Tantar
She received her Diploma degree in 2003 and MsC in 2005 in the field of Computational Optimization, both from the Computer Science Faculty at the "Al. I. Cuza University" in Iasi, Romania. In 2005 she joined the French National Institute for Research in Computer Science and Control(INRIA) in Lille. She was awarded the PhD title for Landscape analysis in multi-objective optimization in 2009 at the University of Lille 1. Between 2007 and 2009 she hold a lecturer position at the same university. During her PhD she was also awarded an INRIA Explorateurs scholarship to the CWI, Amsterdam, Netherlands. She developed a strong interest on new challenging aspects regarding landscape analysis in multi-objective, but also the theoretical foundations of stochastic methods and their scaling to practical problems. Before joining the CSC research unit, at the University of Luxembourg, in October 2010, she was an INRIA post-doctoral researcher in the Advanced Learning Evolutionary Algorithms (ALEA) team, at INRIA Bordeaux, dealing with performance guarantees factors for multi-objective particle methods, such as evolutionary algorithms and rare event simulation techniques. Emilia was actively involved in the dissemination of research through the organization of the 1st Workshop on Evolutionary Algorithms - Challenges in Theory and Practice, supported by the EA association, France. It aimed at providing a unified view over the developments in evolutionary computation through different research fields, as computer science, mathematics and physics. Her main research interests concern the performance guarantee factors for online dynamic multi-objective optimization appearing in energy efficient optimization. This is motivated through the study of the stability of existing  approaches by means of evolutionary particle like paradigms. Emilia is currently co-authoring with Oliver Schutze a book in Springer series, dealing with performance guarantees and landscape analysis in multi-objective optimization.

Peter A.N. Bosman

He 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).

Miguel A. Vega-Rodríguez
He 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
He 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

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 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.