Workshops

John R. Woodward

John R. Woodward s a lecturer at the University of Stirling, within the CHORDS group (http://chords.cs.stir.ac.uk/) and is employed on the DAASE project (http://daase.cs.ucl.ac.uk/), and for the previous four years was a lecturer with the University of Nottingham. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, particularly Search Based Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming. He has over 50 publications in Computer Science, Operations Research and Engineering which include both theoretical and empirical contributions, and given over 100 talks at International Conferences and as an invited speaker at Universities. He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities.

Daniel R. Tauritz

Daniel R. Tauritz is an Associate Professor in the Department of Computer Science at the Missouri University of Science and Technology (S&T), a contract scientist for Sandia National Laboratories, a former Guest Scientist at Los Alamos National Laboratory (LANL), the founding director of S&T's Natural Computation Laboratory, and founding academic director of the LANL/S&T Cyber Security Sciences Institute. He received his Ph.D. in 2002 from Leiden University for Adaptive Information Filtering employing a novel type of evolutionary algorithm. He served previously as GECCO 2010 Late Breaking Papers Chair, GECCO 2012 & 2013 GA Track Co-Chair, GECCO 2015 ECADA Workshop Co-Chair, GECCO 2015 MetaDeeP Workshop Co-Chair, GECCO 2015 Hyper-heuristics Tutorial co-instructor, and GECCO 2015 CBBOC Competition co-organizer. For several years he has served on the GECCO GA track program committee, the Congress on Evolutionary Computation program committee, and a variety of other international conference program committees. His research interests include the design of hyper-heuristics and self-configuring evolutionary algorithms and the application of computational intelligence techniques in cyber security, critical infrastructure protection, and program understanding. He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members.

Manuel López-Ibáñez

Dr. López-Ibáñez is a lecturer in the Decision and Cognitive Sciences Research Centre at the Alliance Manchester Business School, University of Manchester, UK. He received the M.S. degree in computer science from the University of Granada, Granada, Spain, in 2004, and the Ph.D. degree from Edinburgh Napier University, U.K., in 2009. He has published 17 journal papers, 6 book chapters and 36 papers in peer-reviewed proceedings of international conferences on diverse areas such as evolutionary algorithms, ant colony optimization, multi-objective optimization, pump scheduling and various combinatorial optimization problems. His current research interests are experimental analysis and the automatic configuration and design of stochastic optimization algorithms, for single and multi-objective problems. He is the lead developer and current maintainer of the irace software package for automatic algorithm configuration (http://iridia.ulb.ac.be/irace).

Maxim Buzdalov

He obtained a MS degree in Computer Science from the ITMO University (Saint-Petersburg, Russia) in 2011, and a PhD in Computer Science from the same university in 2014. He is currently an Associate Professor in the Department of Information Technologies and Programming of the ITMO University. His research interests include theory of evolutionary computation, search-based software engineering, multi-objective optimization, and applying all kinds of algorithms and data structures to enhance performance of evolutionary algorithms.

Anne Auger

Anne Auger 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 served as track chair for the theory and ES track in 2011, 2013 and 2014. Together with Benjamin Doerr, she is editor of the book "Theory of Randomized Search Heuristics".

Dimo Brockhoff

Dimo Brockhoff 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 on the benchmarking of blackbox algorithms in general.

Nikolaus Hansen

Nikolaus Hansen 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).

Tea Tušar

Tea Tušar is a postdoc in the Dolphin team at INRIA Lille - Nord Europe, France. She received her Ph.D. degree in Information and Communication Technologies from the Jožef Stefan International Postgraduate School, Ljubljana, Slovenia, in 2014. Before joining INRIA in 2015, she has worked at the Department of Intelligent Systems at the Jožef Stefan Institute since 2004, first as a research assistant and later as a postdoc. Her research interests include evolutionary algorithms for single- and multi-objective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems. She served as a workshops co-chair at PPSN 2014 and co-organized GECCO’s Student Workshop in years 2013-2015.

Dejan Tušar

Dejan Tusar is an engineer at Inria Lille - Nord Europe, France. He is working on the re-implementation of the Comparing continuous optimization benchmarking platform (COCO). He received his B.Sc. degree in Applied Mathematics in 2002 and his M.Sc. degree in Computer Science in 2007, both from University of Ljubljana, Slovenia. From 2004 to 2007 he worked at Adacta, a Slovene software company, where he was developing various back office applications. From 2007 to 2015 he worked at another Slovene software company called Marg, where he was implementing a document management system used by Slovene private companies and government institutions.

Tobias Wagner

Tobias Wagner obtained his diploma in computer science (Dipl.-Inf.) from the University of Dortmund, Germany in 2006. He received his PhD in mechanical engineering (Dr.-Ing.) from the Technische Universität Dortmund, Germany in 2013. Between June 2006 and September 2013 he held a scientific assistant position at the Institute of Machining Technology (ISF). Since October 2013 he works as a nonpermanent "Akademischer Rat" (academic councilor) at the ISF. His research is focused on surrogate-assisted single- and multiobjective optimization and sequential design techniques. He is particularly interested in industrial and technical applications of EMO methods. Methodically, he works on the use of performance indicators and preference information within sequential design techniques. In 2015, Tobias Wagner co-hosted the EMO Tutorial at GECCO 2015. Together with Dimo Brockhoff, Boris Naujoks, and Michael Emmerich, he currently organizes the Lorentz Center workshop "SAMCO: Surrogate-Assisted Multi-Criteria Optimization", which will be held in February 2016.

Una-May O'Reilly

Una-May O'Reilly is leader of the AnyScale Learning For All (ALFA) group at CSAIL. ALFA focuses on scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics. The group has projects in clinical medicine knowledge discovery: arterial blood pressure forecasting and pattern recognition, diuretics in the ICU; wind energy: turbine layout optimization, resource prediction, cable layout; and MOOC Technology: MoocDB, student persistence and resource usage analysis.

Her research is in the design of scalable Artificial Intelligence systems that execute on a range of hardware systems: GPUs, workstations, grids, clusters, clouds and volunteer compute networks. These systems include machine learning components such as evolutionary algorithms (e.g. genetic programming, genetic algorithms and learning classifiers), classification, non-linear regression, and forecasting algorithms. They span the interpretation and analysis of raw data, through inference on conditioned exemplar data, to the deployment and evaluation of learned “algorithmic machines” in the original application context.

Una-May received the EvoStar Award for Outstanding Achievements in Evolutionary Computation in Europe in 2013. She is a Junior Fellow (elected before age 40) of the International Society of Genetic and Evolutionary Computation, now ACM Sig-EVO. She now serves as Vice-Chair of ACM SigEVO. She served as chair of the largest international Evolutionary Computation Conference, GECCO, in 2005. She has served on the GECCO business committee, co-led the 2006 and 2009 Genetic Programming: Theory to Practice Workshops and co-chaired EuroGP, the largest conference devoted to Genetic Programming. IIn 2013, with Anna Esparcia, Anniko Ekart and Gabriela Ochoa she inaugurated the Women@GECCO meeting and chairs the group. She is the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines (Kluwer), and editor for Evolutionary Computation (MIT Press), and action editor for the Journal of Machine Learning Research.

Una-May has a patent for a original genetic algorithm technique applicable to internet-based name suggestions. She holds a B.Sc. from the University of Calgary, and a M.C.S. and Ph.D. (1995) from Carleton University, Ottawa, Canada. She joined the Artificial Intelligence Laboratory, MIT as a Post-Doctoral Associate in 1996.

Erik Hemberg

Erik Hemberg is a Post Doctoral Associate with the ALFA group at CSAIL at MIT. He received his Ph.D in Computer Science from University College Dublin, Ireland in 2010 and has a M.Sc from Chalmers University of Technology, Sweden.

In 2013, Hemberg and O'Reilly co-taught a 1 week, full time course on EC at Shantou University in China employing interactive learning activities and other means of engaging learners to gain a clear, tangible, experience-based abstract understanding of evolution at a level above and beyond textbook biology. They are developing and delivering a extension which is a small private online course (SPOC) course on Evolutionary Processes and Computation for Shantou University, China. email:hembergerik at csail dot mit dot edu

Nicole Hoffman

Nicole Hoffman is Project Assistant for ALFA group, CSAIL, MIT. Nicole graduated from Emmanuel College in Boston, MA with a BA in Sociology and American History. Nicole came on board at ALFA in the spring of 2015 to provide her organizational expertise to the group. She assists with organizational and coordination aspects of student, industry, and government contracted projects. Nicole has been vital in the coordination of the Evolution in Action! workshop at GECCO 2016, Denver. She was assistant teacher at Evolution in Action! at Charlestown High in Dec 2015.

Stéphane Doncieux

Stéphane Doncieux is Professeur des Universités (Professor) in Computer Sci- ence at Université Pierre et Marie Curie (UPMC, Paris, France). His research is mainly concerned with the use of evolutionary algorithms in the context of optimization or synthesis of robot controllers. He worked in a robotics context to design, for instance, controllers for flying robots, but also in the context of modeling where he worked on the use of multi-objective evolutionary algorithms to optimize and study computational models. More recently, he focused on the use of multi-objective approaches to tackle learning problems like premature convergence or generalization.

He is engineer of the ENSEA, a french electronic engineering school. He obtained a Master’s degree in Artificial Intelligence and Pattern Recognition in 1999. He pursued and defended a PhD in Computer Science in 2003. He was responsible, with Bruno Gas, of the SIMA research team since its creation in 2007 and up to 2011. Since then, he is the head of the AMAC (Architecture and Models of Adaptation and Cognition) research team with 11 permanent researchers, 3 post-doc students and 9 PhD students. Researchers of the team work on different aspects of learning in the context of motion control and cognition, both from a computational neuroscience perspective and a robotics perspective. He has published 10 journal papers and more than 30 articles in international conferences. He has organized several workshops on ER at conferences like GECCO or IEEE-IROS and has edited 2 books.

Joshua Auerbach

Dr. Joshua E. Auerbach is currently a senior postdoctoral researcher with the Laboratory of Intelligent Systems (LIS) at the École Polytechnique Fédérale de Lausanne (EPFL) funded under the European Union INSIGHT project. Prior to joining LIS he was a member of the Morphology, Evolution & Cognition Laboratory at the University of Vermont (United States) where he earned a Graduate Certificate in Complex Systems in 2009, and an interdisciplinary Ph.D. in Computer Science in 2013 for his work on "The Evolution of Complexity in Autonomous Robots." He is the lead developer for the RoboGen™ open source hardware and software platform for the joint evolution of robot bodies and brains, and conducts research into various questions related to the evolution of useful complexity, morphological computation, and how evolution can contribute to learning.

Richard Duro

Richard J. Duro received a M.S. degree in Physics from the University of Santiago de Compostela, Spain, in 1989, and a PhD in Physics from the same University in 1992. He is currently a Full Professor in the Department of Computer Science and head of the Integrated Group for Engineering Research at the University of A Coruna, Spain. His research interests include cognitive, autonomous and evolutionary robotics, higher order neural network structures and multidimensional signal processing.

Harold de Vladar

H.P. de Vladar studied Cell Biology and Statistical Physics later to become a theoretical evolutionary geneticist, following his PhD at the University of Groningen (2009). Most of de Vladar's work is on evolutionary biology, although often other subjects are also addressed. He currently works at Parmenides Foundation (Munich) for the consortium INSIGHT: Darwinian Neurodynamics, where his main goal is to understand aspects of cognition by using tools of evolutionary biology.

José Santos

José Santos obtained an MS degree in Physics (specialization in Electronics) from the University of Santiago de Compostela, Spain, in 1989, and a Ph.D. from the same University in 1996 (specialization in Artificial Intelligence). He is currently an Associate Professor, accredited as Full Professor, in the Department of Computer Science at the University of A Coruña (Spain). His research interests include artificial life, neural computation, evolutionary computation, autonomous robotics and computational biology. In the last years his research was focused on computational biology, applying all the knowledge acquired in the other research lines to the computational modeling of biological problems.

Julia Handl

Julia Handl obtained a Bsc (Hons) in Computer Science from Monash University in 2001, an MSc degree in Computer Science from the University of Erlangen-Nuremberg in 2003, and a PhD in Bioinformatics from the University of Manchester in 2006. From 2007 to 2011, she held an MRC Special Training Fellowship at the University of Manchester, and she is now a Lecturer in the Decision and Cognitive Sciences Group at the Manchester Business School. Her PhD work explored the use of multiobjective optimization in unsupervised and semi-supervised classification. She has developed multiobjective algorithms for clustering and feature selection tasks in these settings, and her work has highlighted some of the theoretical and empirical advantages of this approach.

Amarda Shehu

Dr. Shehu is an Associate Professor in the Department of Computer Science at George Mason University. She holds affiliated appointments in the School of Systems Biology and the Department of Bioengineering. She received her B.S. in Computer Science and Mathematics from Clarkson University in Potsdam, NY in 2002 and her Ph.D. in Computer Science from Rice University in Houston, TX in 2008, where she was an NIH fellow of the Nanobiology Training Program of the Gulf Coast Consortia. Shehu's research contributions are in computational structural biology, biophysics, and bioinformatics with a focus on issues concerning the relationship between biomolecular sequence, structure, dynamics, and function. Her research on probabilistic search and optimization algorithms for protein structure modeling is supported by various NSF programs, including Intelligent Information Systems, Computing Core Foundations, and Software Infrastructure. Shehu is also the recipient of an NSF CAREER award in 2012.

Stefan Wagner

Stefan Wagner 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

Michael Affenzeller 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).

Vanessa Volz

Vanessa Volz is a research assistant at TU Dortmund, Germany, with focus in computational intelligence. She holds B.Sc. degrees in Information Systems and in Computer Science from WWU Münster, Germany. She received an M.Sc. with distinction in Advanced Computing: Machine Learning, Data Mining and High Performance Computing from University of Bristol, UK in 2014 after completing a BigData internship at Brown University, RI, USA. Her current research focus is on machine learning for balancing and robustness in systems with interacting human and artificial agents, especially in the context of games.

Samadhi Nallaperuma

Samadhi Nallaperuma completed her PhD at University of Adelaide in 2015. She was a postdoctoral researcher at University of Adelaide, Australia until February 2016 and currently a postdoctoral researcher at University of Sheffield, UK.

She is a program committee member in the Special Session on Theoretical Foundations of Bio-inspired Computation in 2013-2016 at IEEE CEC and the theory track of GECCO 2016. She has co-organized the "Simulated Car Racing Championship" at GECCO 2015. She is also serving as a sub-reviewer for Evolutionary Computation Journal (MIT Press) and conferences including PPSN, SEA, TPNC. Her work focuses on understanding the influence of problem and algorithm parameters on the performance of randomized search heuristics.

Frank Moore

Frank Moore is Professor and Chair of the Computer Science & Engineering at the University of Alaska Anchorage. He has taught computer science and engineering for the past 18 years. He also has over six years of industry experience developing software for a wide range of military projects. His recent NASA-funded research (patent pending) used evolutionary computation to optimize transforms that outperform wavelets for lossy image compression and reconstruction. He has received over $750,000 in research funding and has published over 80 technical papers and reports. Dr. Moore is a Senior Member of ACM and a Member of IEEE.

gunes.kayacik

Gunes Kayacik is a Research Scientist at Qualcomm Research Silicon Valley, USA. His research interests have always been found in the middle ground between computer security and machine learning. Following the completion of a Ph.D. in computer science from Dalhousie University, he was awarded a Marie Curie Postdoctoral Fellowship. His postdoctoral research focused on mobile device security, specifically getting devices to recognize their users from sensor data. Prior to joining Qualcomm, Dr. Kayacik worked at Silicon Valley start-ups, developing machine learning methods for botnet detection and data leak prevention, which protected several thousand end users and hosts.

Westley Weimer

Westley Weimer is an associated professor at the University of Virginia. He received his PhD from the University of California at Berkeley. His research interests include reducing the costs associated with software development (particularly through automated program repair) as well as program analysis, formal verification, and human linguistic and visual interaction with software. He is a senior member of the Association for Computing Machinery and his work has led to over eight thousand citations and several awards, including three 'Humies' for his work on using Genetic Improvement for program improvement.

Justyna Petke

Justyna Petke has a doctorate in Computer Science from University of Oxford and is now at the Centre for Research on Evolution, Search and Testing (CREST) in University College London. She has published on applications of genetic improvement. Her work on the subject was awarded a 'Humie' at GECCO 2014 and an ACM SIGSOFT Distinguished Paper Award at ISSTA 2015. She also organised the first Genetic Improvement Workshop.

David R. White

David R. White is a researcher in the Department of Computer Science at UCL. He published some of the seminal papers on both creating new and improving existing software with respect to non-functional improvement, and his subsequent thesis was nominated for a BCS distinguished thesis award. He then worked as a SICSA Research Fellow at the University of Glasgow, before joining the EPSRC AnyScale project at Glasgow and the DAASE Project at UCL. He is on the steering committee of SSBSE and has won two best paper awards for work in evolutionary search.

Silvino Fernandez Alzueta

Silvino Fernández is an R&D Engineer at the Global R&D Department of ArcelorMittal for more than 10 years. He develops his activity in the ArcelorMittal R&D Centre of Asturias, in the framework of the Business and TechnoEconomic project Area. He has a Master Science degree in Computer Science, obtained at University of Oviedo in Spain, and also a Ph.D. in Engineering Project Management obtained in 2015. His main research interests are in analytics, metaheuristics and swarm intelligence, and he has broad experience in using these kind of techniques in industrial environment to optimize production processes. His paper ‘Scheduling a Galvanizing Line by Ant Colony Optimization‘ obtained the best paper award in the ANTS conference in 2014.

Pablo Valledor Pellicer

Pablo Valledor is an R&D engineer of the Global R&D Asturias Centre at ArcelorMittal (world's leading integrated steel and mining company), working at the Business & Technoeconomic area. He obtained his MS degree in Computer Science in 2006 and his PhD on Business Management in 2015, both from the University of Oviedo. He worked for the R&D department of CTIC Foundation (Centre for the Development of Information and Communication Technologies in Asturias) until February 2007, when he joined ArcelorMittal. His main research interests are metaheuristics, multi-objective optimization, analytics and operations research.

Thomas Stützle

Thomas Stützle is a senior research associate of the Belgian F.R.S.-FNRS working at the IRIDIA laboratory of Université libre de Bruxelles (ULB), Belgium. He received the Diplom (German equivalent of M.S. degree) in business engineering from the Universität Karlsruhe (TH), Karlsruhe, Germany in 1994, and his PhD and his habilitation in computer science both from the Computer Science Department of Technische Universität Darmstadt, Germany, in 1998 and 2004, respectively. He is the co-author of two books about ``Stochastic Local Search: Foundations and Applications and ``Ant Colony Optimization and he has extensively published in the wider area of metaheuristics including 20 edited proceedings or books, 8 journal special issues, and more than 190 journal, conference articles and book chapters, many of which are highly cited. He is associate editor of Computational Intelligence, Swarm Intelligence, and Applied Mathematics and Computation and on the editorial board of seven other journals including Evolutionary Computation and Journal of Artificial Intelligence Research. His main research interests are in metaheuristics, swarm intelligence, methodologies for engineering stochastic local search algorithms, multi-objective optimization, and automatic algorithm configuration. In fact, since more than a decade he is interested in automatic algorithm configuration and design methodologies and he has contributed to some effective algorithm configuration techniques such as F-race, Iterated F-race and ParamILS. His 2002 GECCO paper on "A Racing Algorithm For Configuring Metaheuristics" (joint work with M. Birattari, L. Paquete, and K. Varrentrapp) has received the 2012 SIGEVO impact award.

Kuber Karthik

Karthik Kuber received his PhD in 2014 from Syracuse University in Computer Science. His dissertation research was on studying evolutionary algorithms from a network perspective, mainly focusing on Genetic Algorithms, Particle Swarms, and Learning Classifier Systems. He worked on information theoretic fitness measures for Learning Classifier Systems during his MS thesis, also at Syracuse. Prior to graduate school, he worked at Tata Consultancy Services in Bangalore, and received a BE in Electronics and Communication Engineering from Visvesvaraya Technological University. He is currently working at Microsoft where his interests are in exploring and applying various machine learning, analysis and modelling techniques in the context of large-scale engineering systems.

Masaya Nakata

Masaya Nakata eceived the B.A. and M.Sc. degrees in informatics from the University of Electro- Communications, Chofu, Tokyo, Japan, in 2011 and 2013 respectively. He is the Ph.D. candidate in the University of Electro- Communications, the research fellow of Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo, Japan, and a visiting student of the School of Engineering and Computer Science in Victoria University of Wellington from 2014. He was a visiting student of the Department of Electronics and Information, Politecnico di Milano, Milan, Italy, in 2013, and of the Department of Computer Science, University of Bristol, Bristol, UK, in 2014. His research interests are in evolutionary computation, reinforcement learning, data mining, more specifically, in learning classifier systems. He has received the best paper award and the IEEE Computational Intelligence Society Japan Chapter Young Researcher Award from the Japanese Symposium of Evolutionary Computation 2012. He is a co-organizer of International Workshop on Learning Classifier Systems (IWLCS) for 2015-2016.

Shafi Kamran

Dr. Shafi holds a PhD in computer science, a M.Sc. in telecoms engineering and a B.Sc. in electrical engineering. Dr. Shafi is the organising member (elected) for the International Workshop on Learning Classifier Systems (IWLCS) 2013-14 and 2015-16. He was the chair of Computational Intelligence Day workshop held at the University of New South Wales (UNSW-Canberra) Australia in September 2013. He was the publicity chair for the 2012 World Congress on Computational Intelligence (WCCI 2012). He has been a program committee member and chair/co-chair of several workshops at GECCO and IEEE CEC conferences since 2005. His PhD thesis “An online and adaptive signature-based approach for intrusion detection using learning classifier systems (LCS)” 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.

Giovanni Squillero

Giovanni Squillero received his M.S. and Ph.D. in computer science in 1996 and 2001, respectively. He is an assistant professor in Politecnico di Torino, Torino, Italy. His research interests mix the whole spectrum of bio-inspired metaheuristics with electronic CAD, and selected topics in computational intelligence, games, and multi-agent systems. His activities focus on developing techniques able to achieve "good" solutions while requiring an "acceptable" amount of resources, with main applications in real, industrial problems. Squillero is a member of the *IEEE Computational Intelligence Society Games Technical Committee*. He organized the *EvoHOT* workshops on evolutionary hardware optimization techniques, and he is currently a member of the editorial board of *Genetic Programming and Evolvable Machines*. He is the coordinator of *EvoApplications* for 2016.
<http://www.cad.polito.it/~squillero/cv_squillero.pdf>

alberto.tonda

Alberto Tonda received his PhD in 2010, from Politecnico di Torino, Torino, Italy, with a thesis on real-world applications of evolutionary computation. After post-doctoral experiences on the same topics at the Institut des Systèmes Complexes of Paris and INRIA Saclay, France, he is now a permanent researcher at INRA, the French National Institute for Research in Agriculture and Agronomy. His current research topics include semi-supervised modeling of food processes, and stochastic optimization of processes for the industry.

Stephen L. Smith

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 reader in the Department of Electronics at the University of York, UK.

Stephen'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. Stephen 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. Stephen currently holds a Royal Academy of Engineering Enterprise Fellowship.

Stephen is co-founder and organizer of the MedGEC Workshop, which is now in its tenth year. He is also guest editor for a special issue of Genetic Programming and Evolvable Machines (Springer) on medical applications and co-editor of a book on the subject (John Wiley, November 2010).

Stephen 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.

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

Stefano Cagnoni

Stefano Cagnoni graduated in Electronic Engineering at the University of Florence, Italy, 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 include: 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 diS. 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 chair of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing, now a track of the EvoApplications conference. Since 2005, he has co-chaired 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. Member of the Editorial Board of the journals “Evolutionary Computation” and “Genetic Programming and Evolvable Machines”.

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

Robert M. Patton

Dr. Patton received his 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).

Peter A.N. Bosman

Peter A. N. Bosman is a senior researcher in the Life Sciences research group at the Centrum Wiskunde & Informatica (CWI) (Centre for Mathematics and Computer Science) located in Amsterdam, the Netherlands. Peter was formerly affiliated with the Department of Information and Computing Sciences at Utrecht University, where also he obtained both his MSc and PhD degrees in Computer Science, more specifically on the design and application of estimation-of-distribution algorithms (EDAs). Peter is best known for his status of active researcher in the area of EDAs since its upcoming and has (co-)authored over 80 refereed publications, studying both algorithmic design aspects and real-world applications of evolutionary algorithms. At the GECCO conference, Peter has previously been track (co-)chair (EDA track, 2006, 2009), late-breaking-papers chair (2007), (co-)workshop organizer (OBUPM workshop, 2006; EvoDOP workshop, 2007; GreenGEC workshop, 2012-2014) and (co-)local chair (2013).

John McCall

John McCall is a Professor of Computing in the IDEAS Research Institute at Robert Gordon University in Scotland. Originally a pure mathematician (algebraic topology), he has over twenty years research experience in naturally-inspired computing. Major themes of his research include the development and analysis of novel metaheuristics, particularly markov-network EDAs, and probabilistic modelling for optimisation and learning. Application areas of his research include medical decision support, drilling rig market analysis, analysis of biological sequences, staff rostering and scheduling, image analysis and bio-control. Algorithms developed from his research have been implemented as commercial software. Prof. McCall has over 90 publications in books, international journals and conferences and he chairs the IEEE ECTC Task Force in Evolutionary Algorithms based on Probabilistic Models.

David Walker

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

Richard Everson

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

His research interests lie 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. Current research is on surrogate methods for large optimisation problems, particularly computational fluid dynamics design optimisation.

Jonathan Fieldsend

Jonathan Fieldsend is Senior Lecturer in 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.

He has published widely on theoretical and applied aspects of evolutionary multi-objective optimisation, and also in the field of machine learning — and has ongoing interests on the interface between these areas. His previous work has included developing a many-surrogate algorithm for multi-modal problems, and is currently working on surrogate-assisted learning for costly industrial design problems.

Work in these fields has also 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.

Carola Doerr

Carola Doerr (Carola.Doerr@mpi-inf.mpg.de, http://www-ia.lip6.fr/~doerr/) is a CNRS researcher at the Université Pierre et Marie Curie (Paris 6). She studied mathematics at Kiel University (Germany, Diploma in 2007) andcomputer science at the Max Planck Institute for Informatics and Saarland University (Germany, PhD in 2011). From Dec. 2007 to Nov. 2009, Carola Doerr has worked as a business consultant for McKinsey & Company, mainly in the area of network optimization, where she has used randomized search heuristics to compute more efficient network layouts and schedules. Before joining the CNRS she was a post-doc at the Université Diderot (Paris 7) and the Max Planck Institute for Informatics.

Carola Doerr's main research interest is in the theory of randomized algorithms, both in the design of efficient algorithms as well as in finding a suitable complexity theory for randomized search heuristics. Most of her papers are on black-box complexities, a theory-guided approach to explore the limitations of heuristic search algorithms. She has contributed to the field of evolutionary computation also through results on the runtime analysis of evolutionary algorithms and drift analysis, as well as through the development of search heuristics for solving geometric discrepancy problems.

Julia Handl

Julia Handl obtained a Bsc (Hons) in Computer Science from Monash University in 2001, an MSc degree in Computer Science from the University of Erlangen-Nuremberg in 2003, and a PhD in Bioinformatics from the University of Manchester in 2006. From 2007 to 2011, she held an MRC Special Training Fellowship at the University of Manchester, and she is now a Lecturer in the Decision and Cognitive Sciences Group at the Manchester Business School. Her PhD work explored the use of multiobjective optimization in unsupervised and semi-supervised classification. She has developed multiobjective algorithms for clustering and feature selection tasks in these settings, and her work has highlighted some of the theoretical and empirical advantages of this approach.

Emma Hart

Prof. Hart received her PhD from the University of Edinburgh. She currently leads the Centre for Emergent Computing at Edinburgh Napier University where her research focuses on optimisation and continuous learning systems, with an emphasis applying methods from Artificial Immune Systems and HyperHeuristics. She has published extensively in the field of Artificial Immune Systems, with a particular interest in optimisation and self-organising systems such as swarm robotics. Her current interests relate to the development of optimisation algorithms that continuously learn through experience, and how collectives of algorithms can collaborate to form good problem solvers. She also has interests in more theoretical work relating to modelling the immune system to learn more about its computational properties. She is an Associate Editor of Evolutionary Computing, a member of the SIGEVO Executive Board and editor of the SIGEVO newsletter.

Gabriela Ochoa

Gabriela Ochoa is a Senior Lecturer in Computing Science at the University of Stirling, Scotland. She holds a PhD in Computing Science and Artificial Intelligence from the University of Sussex, UK. Her research interests lie in the foundations and application of evolutionary algorithms and heuristic search methods, with emphasis on autonomous (self-*) search, hyper-heuristics, fitness landscape analysis, and applications to combinatorial optimisation, healthcare, and software engineering. She has published over 90 scholarly papers and serves various program committees. She is associate editor of Evolutionary Computation (MIT Press), was involved in founding the Self-* Search track in 2011, and served as the tutorial chair at GECCO in 2012, 2013. She proposed the first Cross-domain Heuristic Search Challenge (CHeSC 2011) and was chair of EvoCOP 2014, EvoCOP 2015, FOGA 2015, and id serving as program chair for PPSN 2016.

Amarda Shehu

Dr. Shehu is an Associate Professor in the Department of Computer Science at George Mason University. She holds affiliated appointments in the School of Systems Biology and the Department of Bioengineering. She received her B.S. in Computer Science and Mathematics from Clarkson University in Potsdam, NY in 2002 and her Ph.D. in Computer Science from Rice University in Houston, TX in 2008, where she was an NIH fellow of the Nanobiology Training Program of the Gulf Coast Consortia. Shehu's research contributions are in computational structural biology, biophysics, and bioinformatics with a focus on issues concerning the relationship between biomolecular sequence, structure, dynamics, and function. Her research on probabilistic search and optimization algorithms for protein structure modeling is supported by various NSF programs, including Intelligent Information Systems, Computing Core Foundations, and Software Infrastructure. Shehu is also the recipient of an NSF CAREER award in 2012.

Tea Tušar

Tea Tušar is a postdoc in the Dolphin team at INRIA Lille - Nord Europe, France. She received her Ph.D. degree in Information and Communication Technologies from the Jožef Stefan International Postgraduate School, Ljubljana, Slovenia, in 2014. Before joining INRIA in 2015, she has worked at the Department of Intelligent Systems at the Jožef Stefan Institute since 2004, first as a research assistant and later as a postdoc. Her research interests include evolutionary algorithms for single- and multi-objective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems. She served as a workshops co-chair at PPSN 2014 and co-organized GECCO’s Student Workshop in years 2013-2015.

Christine Zarges

Christine Zarges received her degree and PhD from the TU Dortmund, Germany, in 2007 and 2011, respectively. Afterwards, she held a postdoctoral research position at the University of Warwick, UK. She currently is a Birmingham Fellow and Lecturer in the School of Computer Science at the University of Birmingham, UK. Her PhD topic was "Theoretical Foundations of Artificial Immune Systems" and her current research focuses on the theoretical analysis of all kinds of randomised search heuristics. She is also interested in computational and theoretical aspects of immunology. She has given tutorials on "Artificial Immune Systems for Optimisation" at previous GECCOs and was co-chair of the AIS track at GECCO 2014. She is member of the editorial board of Evolutionary Computation (MIT Press) and co-organiser of FOGA 2015.

Nur Zincir-Heywood

Nur Zincir-Heywood is a Professor of Computer Science at Dalhousie University, Canada. She received her PhD in 1998 in Computer Science and Engineering from Ege University, Turkey. Prior to moving to Dalhousie in 2000, Dr. Zincir-Heywood had been a researcher at Sussex University, UK and Karlsruhe University, Germany as well as working as an instructor at the Internet Society Network Management workshops. She has published over 150 papers in network management, security , information systems and computational intelligence fields. She has substantial experience of industrial research in systems security and network management related topics with Raytheon, RUAG, Gtech, Palomino, Genieknows, and Public Safety Canada. Dr. Zincir-Heywood is a member of the IEEE and ACM.

alma.rahat

Alma Rahat is a Research Fellow at the University of Exeter, UK. He has a degree in Electronic Engineering from the University of Southampton, and a PhD in Computer Science from the University of Exeter. He has worked in the electronics industry as a product development engineer before starting his PhD. His research interests lie in fast hybrid optimisation methods, real-world problems and machine learning. Current research is on the use of surrogate-assisted optimisation approaches for expensive computational fluid dynamics design problems.

Richard Everson

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

His research interests lie 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. Current research is on surrogate methods for large optimisation problems, particularly computational fluid dynamics design optimisation.

Jonathan Fieldsend

Jonathan Fieldsend is Senior Lecturer in 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.

He has published widely on theoretical and applied aspects of evolutionary multi-objective optimisation, and also in the field of machine learning — and has ongoing interests on the interface between these areas. His previous work has included developing a many-surrogate algorithm for multi-modal problems, and is currently working on surrogate-assisted learning for costly industrial design problems.

Work in these fields has also 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.

Handing Wang

1. Handing Wang received the B.Eng. and Ph.D. degrees from Xidian University, Xi'an, China, in 2010 and 2015. She is currently a research follow with the Department of Computer Science, University of Surrey, Guildford, UK. Her research interests include nature-inspired computation, multi- and many-objective optimization, multiple criteria decision making, and real-world problems. She has published over 10 papers in international journal, including IEEE Transactions on Evolutionary Computation (TEVC), IEEE Transactions on Cybernetics (TCYB), and Evolutionary Computation (ECJ).

Yaochu Jin

Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. Degree from Ruhr University Bochum, Germany, in 2001. He is currently a Professor of Computational Intelligence and Head of the Nature Inspired Computing and Engineering (NICE) Group, Department of Computing, University of Surrey, UK. His research interests include understanding evolution, learning and development in biology and bio-inspired approached to solving engineering problems.

He is an Associate Editor of BioSystems, the IEEE Transactions on Cybernetics, IEEE Transactions on NanoBioscience and the IEEE Computational Intelligence Magazine. He is also an Editorial Board Member of Evolutionary Computation. He is an Invited Plenary / Keynote Speaker on several international conferences on various topics, including multi-objective machine learning, computational modeling of neural development, morphogenetic robotics and evolutionary aerodynamic design optimization. He is the General Chair of the 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology and Program Chair of 2013 IEEE Congress on Evolutionary Computation. Dr Jin is Vice President for Technical Activities and an IEEE Distinguished Lecturer of the IEEE Computational Intelligence Society. He is Fellow of BCS and Senior Member of IEEE.