Workshops

Carola Doerr

Carola Doerr (Carola.Doerr@lip6.fr, http://www-ia.lip6.fr/~doerr/) is a permanent CNRS researcher at Sorbonne University in Paris, France. She studied Mathematics at Kiel University (Germany, 2003-2007, Diplom) and Computer Science at the Max Planck Institute for Informatics and Saarland University (Germany, 2010-2011, PhD). Before joining the CNRS she was a post-doc at Paris Diderot University (Paris 7) and the Max Planck Institute for Informatics. From 2007 to 2009, Carola Doerr has worked as a business consultant for McKinsey & Company, where her interest in evolutionary algorithms originates from.

Carola Doerr's main research activities are in the mathematical analysis of randomized algorithms, with a strong focus on evolutionary algorithms and other black-box optimizers. She has been very active in the design and analysis of black-box complexity models, a theory-guided approach to explore the limitations of heuristic search algorithms. Most recently, she has used knowledge from these studies to prove superiority of dynamic parameter choices in evolutionary computation, a topic that she believes to carry huge unexplored potential for the community.

Carola Doerr has received several awards for her work on evolutionary computation, among them the Otto Hahn Medal of the Max Planck Society and four best paper awards at GECCO. She is chairing the programm commitee of FOGA 2019 and previously chaired the theory tracks of GECCO 2015 and 2017. Carola is an editor of two special issues in Algorithmica. She is also vice chair of the EU-funded COST action 15140 on "Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)".

Pietro S. Oliveto

Pietro S. Oliveto iHe is currently a Vice-Chancellor Fellow at the University of Sheffield, UK and has recently been awarded an EPSRC Early Career Fellowship which he will start in March 2015. He received the Laurea degree and PhD degree in computer science respectively from the University of Catania, Italy in 2005 and from the University of Birmingham, UK in 2009. From October 2007 to April 2008, he was a visiting researcher of the Ecient Algorithms and Complexity Theory Institute at the Department of Computer Science of the University of Dortmund where he collaborated with Prof. Ingo Wegener's research group.

His main research interest is the time complexity analysis of randomized search heuristics for combinatorial optimization problems. He has published several runtime analysis papers on Evolutionary Algorithms (EAs), Articial Immune Systems (AIS) and Ant Colony Optimization (ACO) algorithms for classical NP-Hard combinatorial optimization problems, a review paper of the field of time complexity analysis of EAs for combinatorial optimization problems and a book chapter containing a tutorial on the runtime analysis of EAs. He has won best paper awards at the GECCO08, ICARIS11 and GECCO14 conferences and got very close at CEC09 and at ECTA11 through best paper nominations.

Dr. Oliveto has given tutorials on the runtime complexity analysis of EAs at WCCI 2012, CEC 2013, GECCO 2013, WCCI 2014 and GECCO 2014. He is part of the Steering Committee of the annual workshop on Theory of Randomized Search Heuristics (ThRaSH), IEEE member and Chair of the IEEE CIS Task Force on Theoretical Foundations of Bio-inspired Computation.

Thomas Weise

Thomas Weise obtained the MSc in Computer Science in 2005 from the Chemnitz University of Technology and his PhD from the University of Kassel in 2009. He then joined the University of Science and Technology of China (USTC) as PostDoc and subsequently became Associate Professor at the USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI) at USTC. In 2016, he joined Hefei University as Full Professor to found the Institute of Applied Optimization at the Faculty of Computer Science and Technology. Prof. Weise has more than seven years of experience as a full time researcher in China, having contributed significantly both to fundamental as well as applied research. He has more than 80 scientific publications in international peer reviewed journals and conferences. His book "Global Optimization Algorithms – Theory and Application" has been cited more than 730 times. He has acted as reviewer, editor, or programme committee member at 70 different venues.

Ales Zamuda

Aleš Zamuda is an Assistant Professor and Researcher at University of Maribor (UM), Slovenia. He received Ph.D. (2012), M.Sc. (2008), and B.Sc. (2006) degrees in computer science from UM. He is management committee member for Slovenia at European Cooperation in Science (COST), actions CA15140 (ImAppNIO - Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice) and IC1406 (cHiPSet - High-Performance Modelling and Simulation for Big Data Applications). He is IEEE Senior Member, IEEE Slovenia Section Vice Chairman and Young Professionals Chairman, IEEE CIS member, ACM SIGEVO member, ImAppNIO Benchmarks working group vice-chair, and associate editor for Swarm and Evolutionary Computation (IF3.818). His areas of computer science applications include ecosystems, evolutionary algorithms, multicriterion optimization, artificial life, and computer animation; currently yielding h-index 18, 43 publications, and 930 citations on Scopus. He won IEEE R8 SPC 2007 award, IEEE CEC 2009 ECiDUE, 2016 Danubuius Young Scientist Award, and 1% top reviewer at 2017 and 2018 Publons Peer Review Awards, including reviews for over 40 journals and 85 conferences.

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) before joining Inria in November 2011 as a permanent researcher (first in its Lille - Nord Europe research center and since October 2016 in the Saclay - Ile-de-France center). 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 Tusar is a research fellow at the Department of Intelligent Systems of the Jozef Stefan Institute in Ljubljana, Slovenia. She was awarded the PhD degree in Information and Communication Technologies by the Jozef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has completed a one-year postdoctoral fellowship at Inria Lille in France where she worked on benchmarking multiobjective optimizers.

Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems.

Konstantinos Varelas

Konstantinos Varelas is a PhD student at Ecole Polytechnique (CMAP) and Thales LAS France SAS, member of the RandOpt team at Inria Saclay - Ile-de-France. He received his diploma from the faculty of Electrical and Computer Engineering of the National Technical University of Athens in 2014 and his Master degree in Applied Mathematics/Optimization from Paris Saclay University in 2017. His research interests include continuous stochastic optimization algorithms for high dimensional problems, with focus on methods based on the CMA Evolution Strategy, used for Radar applications.

David Camacho-Fernández

David Camacho is currently working as Associate Professor in the Computer Science Department at Universidad Autonoma de Madrid (Spain) and Head of the Applied Intelligence & Data Analysis group. He received a Ph.D. in Computer Science (2001) from Universidad Carlos III de Madrid, and a B.S. in Physics (1994) from Universidad Complutense de Madrid. His research interests includes Data Mining (Clustering), Evolutionary Computation and Swarm Intelligence, Data mining and big Data, Machine Learning, and Video games.

Massimiliano Vasile

Massimiliano Vasile is currently Professor of Space Systems Engineering in the Department of Mechanical & Aerospace Engineering at the University of Strathclyde and director of the Aerospace Centre of Excellence.
Previous to this, he was a Senior Lecturer in the Department of Aerospace Engineering and Head of the Research for the Space Advanced Research Team at the University of Glasgow. Before starting his academic career in 2004, he was the first member of the ESA Advanced Concepts Team and initiator of the ACT research stream on global trajectory optimisation, mission analysis and biomimicry. His research interests include Computational Optimization, Robust Design and Optimization Under Uncertainty exploring the limits of computer science at solving highly complex problems in science and engineering.
He developed Direct Transcription by Finite Elements on Spectral Basis for optimal control, implemented in the ESA software DITAN for low-thrust trajectory design. He has worked on the global optimisation of space trajectories developing innovative single and multi-objective optimisation algorithms, and on the combination of optimisation and imprecise probabilities to mitigate the effect of uncertainty in decision making and autonomous planning. More recently he has undertaken extensive research on the development of effective techniques for asteroid deflection and manipulation. His research has been funded by the European Space Agency, the EPSRC, the Planetary Society and the European Commission. Prof Vasile is currently leading Stardust, an EU-funded international research and training network on active debris removal and asteroid manipulation

Annalisa Riccardi

Associate director of the Intelligent Computational Engineering Laboratory (ICE Lab) and Lecturer at the Department of Mechanical and Aerospace Engineering of the University of Strathclyde (UK). Her current research interest is towards techniques and applications that are at the intersection of machine learning and optimisation.

Bilel Derbel

Bilel Derbel is an associate Professor, having a habilitation to supervise research (Maître de Conférences HDR), at the Department of Computer Science at the University of Lille, France, since 2007. He received his PhD in computer science from the University of Bordeaux (LaBRI, France) in 2006. In 2007, he spent one year as an assistant professor at the university of Aix-Marseille. He is a permanent member and the vice-head of the BONUS ‘Big Optimisation aNd Ultra-Scale Computing’ research group at Inria Lille-Nord Europe and CRIStAL, CNRS. He is a co-founder member of the International Associated Lab (LIA-MODO) between Shinshu Univ., Japan, and Univ. Lille, France, on ‘Massive optimisation and Computational Intelligence’. He has been a program committee member of evolutionary computing conferences such as GECCO, CEC, EvoOP, PPSN, and a regular journal reviewer in a number of reference journal in the optimisation field. He is an associate editor of the IEEE Transactions on Systems Man and Cybernetics: Systems. He co-authored more than fifty scientific papers. He was awarded best paper awards in SEAL'17, ICDCN'11, and was nominated for the best paper award in PPSN'18 and PPSN'14. His research topics are focused on the design and the analysis of combinatorial optimisation algorithms and high-performance computing. His current interests are on the design of adaptive distributed evolutionary algorithms for single- and multi-objective optimisation.

Ke Li

Ke Li is a Lecturer (Assistant Professor) in Data Analytics at the Department of Computer Science, University of Exeter. He earned his PhD from City University of Hong Kong. Afterwards, he spent a year as a postdoctoral research associate at Michigan State University. Then, he moved to the UK and took the post of research fellow at University of Birmingham. His current research interests include the evolutionary multi-objective optimization, automatic problem solving, machine learning and applications in water engineering and software engineering.

Xiaodong Li

Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. Currently, he is a full professor at the School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia. His research interests include evolutionary computation, neural networks, complex systems, multiobjective optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a Vice-chair of IEEE CIS Task Force of Multi-Modal Optimization, and a former Chair of IEEE CIS Task Force on Large Scale Global Optimization. He was the General Chair of SEAL'08, a Program Co-Chair AI'09, and a Program Co-Chair for IEEE CEC’2012. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award".

Saúl Zapotecas

Saúl Zapotecas is a visiting Professor at Department of Applied Mathematics and Systems, Division of Natural Sciences and Engineering, Autonomous Metropolitan University, Cuajimalpa Campus (UAM-C). Saúl Zapotecas received the B.Sc. in Computer Sciences from the Meritorious Autonomous University of Puebla (BUAP). His M.Sc. and PhD in computer sciences from the Center for Research and Advanced Studies of the National Polytechnic Institute of Mexico (CINVESTAV-IPN). His current research interests include evolutionary computation, multi/many-objective optimization via decomposition, and multi- objective evolutionary algorithms assisted by surrogate models.

Qingfu Zhang

Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. He is currently leading the Metaheuristic Optimization Research (MOP) Group in City University of Hong Kong. Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is on the list of the Thomson Reuters 2016 and 2017 highly cited researchers in computer science. He is an IEEE fellow.

Ozgur Akman

Ozgur Akman is a Senior Lecturer in Mathematics at the University of Exeter. He has a BSc in Mathematics and a MSc in Bioengineering from Imperial College London, and a PhD in Mathematics from the University of Manchester. His research interests lie in the interface between applied mathematics, computer science and biology, focusing on the development of computational methods to systematically construct and analyse quantitative models of biochemical and neural networks. His earlier research used nonlinear dynamics techniques to identify the molecular mechanisms underlying the development of neurobiological motor disorders. A particular recent area of interest is the use of evolutionary computing methods to optimise large-scale systems biology models to experimental time series data. This important real-word optimisation problem is characterised by intrinsic uncertainty in the design space - due to the potential presence of multiple optima yielding similar fitness scores - and also in the objective space - due to experimental noise. 

Khulood Alyahya

Khulood Alyahya is a Research Fellow at the University of Exeter. She was awarded a PhD degree in Computer Science in 2016 from the University of Birmingham. She also has an MSc degree in Intelligent Systems Engineering from the same University where she was awarded the best student prize. Her PhD studies were on the landscape analysis of NP-hard problems. Her current research focuses on optimisation under multiple sources of uncertainty in both theoretical and applied settings, with application in the field of Computational Systems Biology. Her research includes extending landscape analysis tools to study the landscapes of robust optimisation problems.

Jürgen Branke

Jürgen Branke is Professor of Operational Research and Systems at Warwick Business School, University of Warwick, UK. He has been an active researcher in the field of Evolutionary Computation for over 20 years, has published over 170 papers in peer-reviewed journals and conferences, resulting in an H-Index of 52 (Google Scholar). His main research interests include optimization under uncertainty, simulation-based optimization and multi-objective optimization. Jürgen is Area Editor for the Journal of Heuristics and the Journal on Multi-Criteria Decision Analysis, and Associate Editor for the Evolutionary Computation Journal and IEEE Transactions on Evolutionary Computation.

Jonathan Fieldsend

Jonathan Fieldsend is an Associate Professor in Computational Intelligence 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 theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

Tinkle Chugh

Dr. Tinkle Chugh is a Postdoctoral Research Fellow at the Department of Computer Science, University of Exeter, UK. He is also a Researcher at Palacký University, Olomouc, Czech Republic. He obtained his PhD degree in Mathematical Information Technology in 2017 from the University of Jyvaskyla, Finland. His thesis was a part of the “Decision Support for Complex Multiobjective Optimization Problems (DeCoMo)” project, where he collaborated with “Finland Distinguished Professor (FiDiPro)” Yaochu Jin from University of Surrey, UK. He received the best student paper award at IEEE Congress on Evolutionary Computation (IEEE CEC) 2017. His research interests are machine learning, data-driven optimization, evolutionary computation and decision making.

Richard Allmendinger

Richard is Business Engagement Lead of Alliance Manchester Business School and Lecturer in Data Science at The University of Manchester. Prior to Manchester, he worked at the Biochemical Engineering Department, University College London. He studied Business Engineering at the Karlsruhe Institute of Technology and the Royal Melbourne Institute of Technology and completed a PhD in Computer Science at The University of Manchester.

Richard's research interests are in the field of data science and in particular in the development and application of optimization, learning and analytics techniques to real-world problems arising in areas such as healthcare, manufacturing, sports, music, economics, and forensics. Much of his research has been funded by grants from Innovate UK, the Engineering and Physical Sciences Research Council (EPSRC), and industrial partners.

Richard is the Co-Founder of the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering, a Member of the IEEE CIS Bioinformatics and Bioengineering Technical Committee, the General Chair of the 2017 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, and a Member of the Editorial Board of the Applied Soft Computing journal.

Jussi Hakanen

Dr Jussi Hakanen is a Senior Researcher at the Faculty of Information Technology at the University of Jyväskylä, Finland. He received MSc degree in mathematics and PhD degree in mathematical information technology, both from the University of Jyväskylä. His research is focused on multiobjective optimization with an emphasis on interactive multiobjective optimization methods and computationally expensive problems. He has participated in several industrial projects involving different applications of multiobjective optimization, e.g. in chemical engineering. He has been a visiting researcher in Cornell University, Carnegie Mellon, University of Surrey, University of Wuppertal, University of Malaga and the VTT Technical Research Center of Finland. He has a title of Docent (similar to Adjunct Professor in the US) in Industrial Optimization at the University of Jyväskylä, Finland.

Josu Ceberio Uribe

Josu Ceberio received the bachelor degree in Computer Sciece from the University of the Basque Country in 2007, and two years later he took his masters degree in Computer Science from the same university. Since 2010, he has been member of the Intelligent Systems Group where he obtained, in 2014, the PhD in Computer Science. Since 2014, he is lecturer at the University of the Basque Country, and currently, he is affiliated to the department of Computer Science and Artificial Intelligence at the Faculty of Computer Science. He has co-authored more than 30 scientific publications in different journals and international conferences covering topics such as permutation-based combinatorial optimization problems, estimation of distribution algorithms, multi-objectivisation, elementary landscape decomposition and parameterized instance complexity.

Valentino Santucci

Valentino Santucci received the PhD degree in Mathematics and Computer Science from University of Perugia in 2012. From 2012 to 2018, he has been post-doc with the Department of Mathematics and Computer Science, University of Perugia. Since 2018, he is Assistant Professor at University for Foreigners of Perugia. He has been a Visiting Researcher with Hong Kong Baptist University, Hong Kong. He has authored more than 20 scientific publications and his research interests include evolutionary computation, machine learning, and computational intelligence.

Marco Baioletti

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.

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. From January 2017, she will become Editor-in-Chief of Evolutionary Computing, She is also a member of the SIGEVO Executive Board and editor of the SIGEVO newsletter.

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.

John R. Woodward

John R. Woodward is head of the Operational Research Group (http://or.qmul.ac.uk/) at QMUL. 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, Artificial Intelligence/Machine Learning and in particular Genetic Programming. Publications are at (https://scholar.google.co.uk/citations?user=iZIjJ80AAAAJ&hl=en), and current EPSRC grants are at (https://gow.epsrc.ukri.org/NGBOViewPerson.aspx?PersonId=-485755). Public engagement articles are at (https://theconversation.com/profiles/john-r-woodward-173210/articles). He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities (Birmingham, Nottingham, Stirling).

Koichi Nakayama

Koichi Nakayama is an Associate Professor at Saga University in Japan. He received his Ph.D from Kyoto University in 2005. He proposed a genetic algorithm to apply multi-agent systems. He was a researcher at ATR (Advanced Telecommunications Research Institute International) and NICT (National Institute of Information and Communications Technology). His research interest is to clear a cognitive process of an expert care giver by using evolutionary algorithms.

Chika Oshima

Chika Oshima is a visiting researcher at Saga University. She received her Ph.D from JAIST (Japan Advanced Institute of Science and Technology) in 2004. She was a researcher at ATR, NICT, and JSPS (Japan Society for the Promotion of Science). She analyzed a cognitive process that care givers decide an activity which suites for each dementia people in a care facility. Then, she developed a support system for elderly people to play the piano easily. She got the Best Paper Awards at ACM Multimedia 2004 and IARIA Global Health 2012.

Stefan Wagner

Stefan Wagner received his MSc in computer science in 2004 and his PhD in technical sciences in 2009, both from Johannes Kepler University Linz, Austria. From 2005 to 2009 he worked as associate professor for software project engineering and since 2009 as full professor for complex software systems at the Campus Hagenberg of the University of Applied Sciences Upper Austria. Dr. Wagner is one of the founders of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) and is project manager and head architect of the open-source optimization environment HeuristicLab. He works as project manager and key researcher in several R&D projects on production and logistics optimization and his research interests are in the area of combinatorial optimization, evolutionary algorithms, computational intelligence, and parallel and distributed computing.

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).

Eneko Osaba

Eneko Osaba works at TECNALIA as researcher in the ICT/OPTIMA area. He obtained his Ph.D. degree on Artificial Intelligence (Cum Laude) in 2015 in the University of Deusto. He has participated in the proposal, development and justification of more than 15 research projects.

He has contributed in the development of more than 80 papers, including more than 15 Q1. He has performed several stays in universities of United Kingdom, Italy and Malta. He served as a member of the program and/or organizing committee in more than 20 international conferences. At HAIS 2015 and IDC 2018, Eneko was also member of the organizing committee, and he organized several special sessions in conferences such CEC 2017, IDC 2018, IDEAL 2018 and PAAMS 2018. Besides this, he is member of the editorial board of International Journal of Artificial Intelligence and Journal of Advanced Transportation. Furthermore, he has acted as guess editor in journals such as Journal of Computational Science, Neurocomputing, Logic Journal of IGPL and IEEE ITS Magazine.

Javier Del Ser Lorente

Javier (Javi) Del Ser joined the Faculty of Engineering of the University of the Basque Country (Spain) to study Electrical Engineering, obtaining his combined B.S. and M.S. degree in May 2003. After finishing this degree, he became a recipient of the Fundacion de Centros Tecnologicos Inaki Goenaga doctoral grant. He received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude, Extraordinary Prize) from the University of Alcala, Spain, in 2013. Currently he is a Research Professor in Data Science and Optimization at TECNALIA (Spain), a visiting fellow at the Basque Centre for Applied Mathematics (BCAM) and an adjunct lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 260 scientific articles, co-supervised 8 Ph.D. theses (+ 7 ongoing), edited 6 books, co-authored 7 patents and participated/led more than 45 research projects. He has also been involved in the organization of various national and international conferences. He is a Senior Member of the IEEE, and a recipient of the Bizkaia Talent prize for his research career.

David Camacho-Fernández

David Camacho is currently working as Associate Professor in the Computer Science Department at Universidad Autonoma de Madrid (Spain) and Head of the Applied Intelligence & Data Analysis group. He received a Ph.D. in Computer Science (2001) from Universidad Carlos III de Madrid, and a B.S. in Physics (1994) from Universidad Complutense de Madrid. His research interests includes Data Mining (Clustering), Evolutionary Computation and Swarm Intelligence, Data mining and big Data, Machine Learning, and Video games.

Pascal Kerschke

Pascal Kerschke is a PostDoc at the group of Information Systems and Statistics at the University of Muenster (Germany). Prior to completing his PhD studies in 2017, he has received a M.Sc. degree in "Data Sciences" (2013) from the Faculty of Statistics at the TU Dortmund (Germany). His main research interests are algorithm selection (for continuous or TSP problems), as well as Exploratory Landscape Analysis (ELA) for single- and multi-objective, continuous (Black-Box) optimization problems. Furthermore, he is one of the developers of related R-packages, such as "flacco", "smoof" and "mlr".

Boris Naujoks

Boris Naujoks is a professor for Applied Mathematics at TH Köln - Cologne University of Applied Sciences (CUAS). He joint CUAs directly after he received his PhD from Dortmund Technical University in 2011. During his time in Dortmund, Boris worked as a research assistant in different projects and gained industrial experience working for different SMEs. Meanwhile, he enjoys the combination of teaching mathematics as well as computer science and exploring EC and CI techniques at the Campus Gummersbach of CUAS. He focuses on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, and the (industrial) applicability of the explored methods.

Tea Tušar

Tea Tusar is a research fellow at the Department of Intelligent Systems of the Jozef Stefan Institute in Ljubljana, Slovenia. She was awarded the PhD degree in Information and Communication Technologies by the Jozef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has completed a one-year postdoctoral fellowship at Inria Lille in France where she worked on benchmarking multiobjective optimizers.

Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems.

Vanessa Volz

Vanessa Volz is a post-doctoral research associate at Queen Mary University London, UK, with focus in computational intelligence in games. She received her PhD in 2019 from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. 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. Her current research focus is on employing surrogate-assisted evolutionary algorithms to obtain balance and robustness in systems with interacting human and artificial agents, especially in the context of games.

Anna I Esparcia-Alcázar

Anna I Esparcia-Alcázar holds a degree in Electrical Engineering from the Universitat Politècnica de València (UPV), Spain, and a PhD from the University of Glasgow, UK. She is a researcher at the PROS Centre of the UPV and an associate lecturer at the Control Department of the same university. She has ample experience both in industry and academia. For the past 10 years she has been actively involved in the organization of the two main conferences in the field of Evolutionary Computation, evostar and GECCO. She is Senior Member of the IEEE and Member of the ACM and was elect member of the Executive Committee of SIGEVO in the period 2009-2015.
In 2015 she was awarded the evo* Award for Outstanding Contribution to Evolutionary Computation in Europe.

Riyad Alshammari

Riyad Alshammari is an Associate Professor in Computer Science and Joint-Associate Professor in Health Informatics at the Department of Health Informatics at the College of Public Health and Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. Dr. Alshammari is specialized in Network security, Network analysis and forensic, and machine learning. Dr. Alshammari's research interest include but not limited to the areas of Data mining, Machine Learning, Classification, Clinical Informatics, e- Health, Computer Network and Homeland Security. Dr. Alshammari is the Chairman of the Health Informatics Department and Director of Center of Excellence in Health Informatics, College of Public Health and Health Informatics, King Saud Bin Abdulaziz University For Health Sciences, Riyadh, Saudi Arabia. Dr. Alshammari has been elected as the President of Saudi Association for Health Informatics (SAHI).

Erik Hemberg

Erik Hemberg is a Research Scientist in the AnyScale Learning
For All(ALFA) group at Massachusetts Institute of Technology Computer Science and Artificial Intelligence Lab, USA. He has a PhD in Computer Science from University College Dublin, Ireland and a MSc in Industrial Engineering and Applied Mathematics from Chalmers University of Technology, Sweden. He has 10 years of experience in EC focusing on the use of programs with grammatical representations, estimation of distribution and coevolution. His work has been applied to networks, tax avoidance, and Cyber Security.

Tokunbo Makanju

Tokunbo Makanju is Research Engineer with the Cybersecurity Laboratory at KDDI Research, Fuijimino-shi, Japan. His research interests are at the intersection of Big Data Analytics, Machine Learning, Network Management and Cybersecurity. Dr. Makanju is a member of the IEEE and the ACM.

Brad Alexander

Brad Alexander is a member of the Optimisation and Logistics Group at the University of Adelaide. His research interests include program optimisation, rewriting, genetic-programming (GP) - especially the discovery of recurrences and search-based-software-engineering. He is currently supervising projects in evolutionary art and in applications of search based software engineering to energy conservation and monitoring in mobile platforms. He has also supervised successful projects in the evolution of control algorithms for robots, the evolution of three-dimensional geological models, and the synthesis and optimisation of artificial water distribution networks, and using background optimisation to improve the performance of instruction set simulators (ISS)'s.

Saemundur O. Haraldsson

Saemundur O. Haraldsson is a senior research associate at Lancaster University. He has multiple publications on Genetic Improvement, including two that have received best paper awards; in last year's GI and ICTS4eHealth workshops. Additionally, he coauthored the first comprehensive survey on GI which was published in 2017. He has been invited to give talks on the subject in two Crest Open Workshops and for an industrial audience in Iceland. His PhD thesis (submitted in May 2017) details his work on the world's first live GI integration in an industrial application.

Markus Wagner

Markus Wagner is a Senior Lecturer at the School of Computer Science, University of Adelaide, Australia. He has done his PhD studies at the Max Planck Institute for Informatics in Saarbruecken, Germany and at the University of Adelaide, Australia. For the outcomes of his studies, he has received the university's Doctoral Research Medal - the first for this school.
His research topics range from mathematical runtime analysis of heuristic optimisation algorithms and theory-guided algorithm design to applications of heuristic methods to renewable energy production, professional team cycling and software engineering. So far, he has been a program committee member 30 times, and he has written over 70 articles with over 70 different co-authors. He has contributed to GECCO as Workshop Chair in 2016 and 2017. He has chaired several education-related committees within the IEEE CIS, was Co-Chair of ACALCI 2017 and General Chair of ACALCI 2018.

John R. Woodward

John R. Woodward is head of the Operational Research Group (http://or.qmul.ac.uk/) at QMUL. 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, Artificial Intelligence/Machine Learning and in particular Genetic Programming. Publications are at (https://scholar.google.co.uk/citations?user=iZIjJ80AAAAJ&hl=en), and current EPSRC grants are at (https://gow.epsrc.ukri.org/NGBOViewPerson.aspx?PersonId=-485755). Public engagement articles are at (https://theconversation.com/profiles/john-r-woodward-173210/articles). He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities (Birmingham, Nottingham, Stirling).

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 Department. 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 analytics, metaheuristics and swarm intelligence, and he has broad experience 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.

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.

Matt Johns

Dr Matt Johns is a Research Fellow in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter. He obtained a PhD in Computer Science from the University of Exeter developing methods for incorporating domain expertise into evolutionary algorithms. Following the submission of his PhD thesis, he worked as an Associate Research Fellow in the Centre for Water Systems developing decision support tools to aid in the optimal design of waste water treatment systems. He then went on to work on the Human-Computer Optimisation for Water Systems Planning and Management project, developing new approaches to the design and management of water systems by incorporating visual analytics, heuristic optimisation and machine learning. His research interests include evolutionary optimisation, water systems optimisation, human-computer interaction and interactive visualisation.

Nick Ross

Nick Ross is a Computer Science PhD student in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter. His interests lie in nature-inspired computation, gamification, and artificial intelligence.

Ed Keedwell

Prof. Keedwell is an Associate Professor and Director of Research for Computer Science. He has personal research interests in nature-inspired computing techniques (e.g. genetic algorithms, neural networks, cellular automata) and hyper-heuristics, exploring their application to a variety of difficult problems in bioinformatics and engineering. He leads the Nature Inspired computing research group focusing on applied optimisation and has been involved with successful funding applications totalling over £2 million from the EPSRC, Innovate UK, EU and industry.

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.

Anthony Stein

Anthony Stein is a research associate and Ph.D. candidate at the Department of Computer Science of the University of Augsburg, Germany. He received his B.Sc. in Business Information Systems from the University of Applied Sciences in Augsburg in 2012. He then switched to the University of Augsburg to proceed with his master's degree (M.Sc.) in computer science with a minor in information economics which he received in 2014. Within his master's thesis, he dived into the nature of Learning Classifier Systems for the first time. Since then, he is a passionate follower and contributor of ongoing research in this field. Besides his position in the Organic Computing Group at the University of Augsburg, he currently finishes his Ph.D. in computer science. His research focuses on the applicability of EML techniques in self-learning adaptive systems which are asked to act in real world environments that exhibit challenges such as data imbalance and non-stationarity. Therefore, in his work he investigates the utilization of interpolation and active learning methods to change the means of how classifiers are initialized, insufficiently covered problem space niches are filled, or adequate actions are selected. A further aspect he investigates is the question how Learning Classifier Systems can be enhanced toward a behavior of proactive knowledge construction.
Since 2017, he is an organizing committee member of the International Workshop on Learning Classifier Systems (IWLCS). For the third year now, he also co-organizes the Workshop Series on Autonomously Learning and Optimizing Systems (SAOS). Among others, he serves as reviewer for GECCO’s EML track, ACM's Transactions on Autonomous and Adapative Systems (TAAS) Journal, and several workshops.

Takato Tatsumi

Takato Tatsumi is a Ph.D. student in the University of Electro-Communications, Japan and the research fellow of Japan Society for the promotion of Science, Japan. He received B.Sc. and M.Sc. degrees in informatics from the University of Electro-Communications, Japan, in 2015 and 2017, respectively. His research interests are in data mining, evolutionary computation, reinforcement learning, more specifically, in learning classifier systems. He has authored more than 7 peer-reviewed LCS research papers, some of them in journals and conferences such as GECCO, CEC, and IWLCS. He received GECCO 2016 Student Travel Grant and Excellent Paper Award from the Japanese Symposium of Informatics (SSI) in 2017.

Nadarajen Veerapen

Nadarajen Veerapen is an Associate Professor (maître de conférences) at the University of Lille, France. Previously he was a research fellow at the University of Stirling in Scotland. He holds a PhD in Computing Science from the University of Angers, France, where he worked on adaptive operator selection. His research interests include local search, hybrid methods, search-based software engineering and visualisation. He is a Publicity Chair for GECCO 2019 and has served as Student Affairs Chair for GECCO 2017 and 2018. He has previously co-organised the workshop on Landscape-Aware Heuristic Search at PPSN 2016, GECCO 2017 and GECCO 2018.

Arnaud Liefooghe

Arnaud Liefooghe is an Associate Professor (Maître de Conférences) at the University of Lille (France) since 2010. He is a member of the CRIStAL research center (UMR 9189, Univ Lille, CNRS, EC Lille) and of the Inria Lille - Nord Europe research center. He is also the co-director of the International Associated Laboratory MODŌ between Shinshu University (Japan) and the University of Lille (France). He received his PhD in computer science from the University of Lille (France) in 2009. In 2010, he was a post-doctoral researcher at the University of Coimbra (Portugal). His main research activities deal with the foundations, the design and the analysis of stochastic local search heuristic algorithms, with a particular interest in multiobjective optimization. He co-authored more than fifty scientific papers in international journals, book chapters and international conferences. He was awarded a best paper award at EvoCOP 2011 and at GECCO 2015. He co-organized two summer schools on EC, the ThRaSH 2012 workshop, a special issue on EMO at EJOR, and special sessions or workshops at EURO 2012, MCDM 2013, LION 2013, CEC 2015 and 2017, as well as GECCO 2017. He has been a program committee member of more than twenty international conferences such as CEC, EMO, EvoCOP, GECCO, PPSN, and a regular journal reviewer from more than ten international journals. Recently, he served as a program chair for EvoCOP 2018 and 2019, and as a proceedings chair for GECCO 2018.

Sébastien Verel

Sébastien Verel is a professor in Computer Science at the Université du Littoral Côte d'Opale, Calais, France, and previously at the University of Nice Sophia-Antipolis, France, from 2006 to 2013. He received a PhD in computer science from the University of Nice Sophia-Antipolis, France, in 2005. His PhD work was related to fitness landscape analysis in combinatorial optimization. He was an invited researcher in DOLPHIN Team at INRIA Lille Nord Europe, France from 2009 to 2011. His research interests are in the theory of evolutionary computation, multiobjective optimization, adaptive search, and complex systems. A large part of his research is related to fitness landscape analysis. He co-authored of a number of scientific papers in international journals, book chapters, book on complex systems, and international conferences. He is also involved in the co-organization EC summer schools, conference tracks, workshops, a special issue on EMO at EJOR, as well as special sessions in indifferent international conferences.

Gabriela Ochoa

Gabriela Ochoa is a Professor in Computing Science at the University of Stirling, Scotland. She received a PhD in Computer Science from the University of Sussex, UK. She worked in industry for five years before joining academia and has held faculty and research positions at the University Simon Bolivar, Venezuela and the University of Nottingham, UK. Her research interests lie in the foundations and application of evolutionary algorithms and heuristic search methods, with emphasis on autonomous search, hyper-heuristics, fitness landscape analysis and visualisation. She is associate editor of the Evolutionary Computation Journal and the IEEE Transactions on Evolutionary Computation; and is a member of the SIGEvo executive board. She has served as an organiser and/or program chair for several international events such as GECCO, PPSN, FOGA, and IEEE CEC and was the Editor-in-Chief for GECCO 2017.

Stephen Smith

Stephen Smith is a full professor in the Department of Electronic Engineering at the University of York, UK. He received a BSc in Computer Science, an MSc in Electronics and a PhD in Electronic Engineering, all from the University of Kent, UK. Stephen's research uses genetic programming (a representation of Cartesian Genetic Programming) in the diagnosis and monitoring of Parkinson's disease, Alzheimer's disease and other neurodegenerative conditions. He has applied this work to clinical studies in the UK, USA, UAE, Australia, China and Singapore. The resulting technology is protected under 12 patent applications, of which 7 have been granted. A spinout company, ClearSky Medical Diagnostics (www.clearskymd.com), is marketing medical devices based on the technology and a recent co-authored publication detailing the clinical efficacy of this work won the Gold Humies award at GECCO 2018.

Stephen is co-founder and organizer of the MedGEC Workshop, which is now in its fourteenth year. He is also an associate editor for the journal Genetic Programming (Springer) and co-editor of a book on Medical Applications of Genetic and Evolutionary Computation (John Wiley, November 2010). Stephen has some 100 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).

William La Cava

Bill is a postdoctoral fellow at the University of Pennsylvania with the Institute for Biomedical Informatics. He received his Ph.D. from the University of Massachusetts Amherst under Professors Kourosh Danai and Lee Spector. His research focus is identifying causal models of disease from patient health records and genome wide association studies. His contributions in genetic programming include methods for local search, parent selection, and representation learning.

Randal Olson

Dr. Randal S. Olson is a Senior Data Scientist working with Prof. Jason H. Moore at the University of Pennsylvania Institute for Biomedical Informatics, where he develops state-of-the-art machine learning algorithms to solve biomedical problems. He specializes in artificial intelligence, machine learning, and data visualization, and regularly writes about his latest work on his personal blog, www.RandalOlson.com/blog. Dr. Olson has become known for computing optimal road trips around the world and solving "Where’s Waldo?," among other creative applications of machine learning, which have been featured all over the world and in the news, including the New York Times, Wired, FiveThirtyEight, and much more.

Dr. Olson works tirelessly to promote open and reproducible science, leading by example and openly publishing his work on GitHub and open access journals. He is also passionate about training the next generation of data scientists to be more efficient, effective, and collaborative in their work, and does so by writing online tutorials, recording video tutorials, teaching hands-on workshops, and mentoring local students in his research specialties.

Dr. Olson has been actively involved in GECCO for several years and won best paper awards at GECCO in 2014 and 2016 for his work in evolutionary agent-based modeling and automated machine learning.

Patryk Orzechowski

Dr. Patryk Orzechowski is a postdoctoral researcher in Artificial Intelligence at University of Pennsylvania. He obtained his Ph.D. in Computer Science and a Masters of Automation and Robotics from AGH University of Science and Technology in Krakow, Poland. His scientific interests are in the areas of machine learning, bioinformatics and artificial intelligence. He also specializes in data mining and mobile technologies.

Ryan Urbanowicz

Dr. Urbanowicz’s research is focused on bioinformatics, machine learning, epidemiology, data mining, and the development of a new learning classifier system that is maximally functional, accessible, and easier to use and interpret. He has written one of the most cited and regarded reviews of the Learning Classifier System research field as well as 12 additional peer-reviewed LCS research papers, has co-chaired the International Workshop on Learning Classifier Systems for the past 4 years, and has recently published and a new open source learning classifier system algorithm implemented in python, called ExSTraCS. He has also given several invited introductory lectures on LCS algorithms in addition to co-presenting this tutorial in 2013. Dr. Urbanowicz received a Bachelors and Masters of Biological Engineering from Cornell University, as well as a PhD in Genetics from Dartmouth College. Currently he is a post-doctoral researcher in the Geisel School of Medicine, about to transition to a new research position at the University of Pennsylvania, USA.

Akira Oyama

Akira Oyama is an associate professor at Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA) and the University of Tokyo in Japan. Previously, he worked for NASA Glenn research center in the U.S. from 2000 to 2003. His research interests include computational fluid dynamics and many/multi-objective design optimization in space engineering. He is the leader of "design innovation with multiobjective design exploration," one of the research topics of Japanese national supercomputer project "HPCI Strategic Programs for Innovative Research Field 4: Design Innovation" since 2010. He has published 265 conference papers and 33 refereed journal articles.

Koji Shimoyama

Koji Shimoyama is an associate professor in the Institute of Fluid Science, Tohoku University, Japan. He obtained his Ph.D. from the Department of Aeronautics and Astronautics, University of Tokyo, Japan, in 2006. Previously, he was a research assistant at JAXA, a research fellow at Tohoku University, a visiting scholar at Stanford University, United States, and an invited Professor at Ecole Centrale De Lyon, France. His research interests are multi-objective design exploration for engineering design, robust design optimization, and uncertainty quantification. He has performed collaborations with various industries in Japan regarding the application of EC and surrogate models for real-world product design and development.

Hemant Kumar Singh

Dr. Hemant Kumar Singh completed his Ph.D. from University of New South Wales (UNSW) Australia in 2011 and B.Tech in Mechanical Engineering from Indian Institute of Technology (IIT) Kanpur in 2007. Since 2013, he has worked with UNSW Australia as a Lecturer (2013-2017) and Senior Lecturer (2017-) in the School of Engineering and Information Technology. He also worked with GE Aviation at John F. Welch Technology Centre as a Lead Engineer during 2011-13. His research interests include development of evolutionary computation methods with a focus on engineering design optimization problems. He has over 50 refereed publications this area. He is the recipient of Australia Bicentennial Fellowship 2016, WCSMO Early Career Researcher Fellowship 2015 and The Australian Society for Defence Engineering Prize 2011 among others.

Kazuhisa Chiba

Kazuhisa Chiba is an associate professor in the graduate school of informatics and engineering, the University of Electro-Communications, Japan. Previously, he was a researcher at JAXA, a researcher at Mitsubishi Heavy Industries, and an associate professor at Hokkaido University of Science. His research interests are aerospace vehicles design via design informatics and multi/many-objective optimization.

Pramudita Satria Palar

Pramudita Satria Palar is a lecturer at Faculty of Mechanical and Aerospace Engineering, Bandung Institute of Technology, Indonesia. Previously, he was a research fellow at Tohoku University, Japan, from 2016-2018. He obtained his Ph.D. from the Department of Aeronautics and Astronautics, University of Tokyo, Japan, in 2015. During his doctoral study, he was also a visiting researcher at Engineering Design Center of University of Cambridge, United Kingdom, and wrote several collaborative papers with the Center. His research interests include aerodynamic design optimization, surrogate-assisted optimization, and uncertainty quantification. He has published several journal and conference papers on the development and application of evolutionary computations and surrogate models in the field of aerospace and biomedical engineering

Alma Rahat

Alma Rahat is a Lecturer in Computer Science at the University of Plymouth. He has a BEng in Electronic Engineering from the University of Southampton, and a PhD in Computer Science from the University of Exeter. He has been a product development engineer before starting his PhD, and has held post-doctoral research positions at the University of Exeter before starting his role in Plymouth. 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 computationally expensive 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 an Associate Professor in Computational Intelligence 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 theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). 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 PhD 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 in Computational Intelligence and Head of the Nature Inspired Computing and Engineering (NICE) Group, Department of Computer Science, University of Surrey, UK. He was a Finland Distinguished Professor and Changjiang Distinguished Professor. His research interests include data-driven evolutionary optimization, Bayesian optimization, secure and interpretable machine learning, evolutionary multi-objective learning, evolutionary developmental systems, and neural plasticity.

He is the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems and the Co-Editor-in-Chief of Complex & Intelligent Systems. He is also an Associate Editor of the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, and IEEE Transactions on NanoBioscience. He is an Editorial Board Member of Evolutionary Computation. He is an Invited Plenary / Keynote Speaker at over 30 international conferences. He is the recipient of several awards including the 2014 and 2016 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an IEEE Distinguished Lecturer.

Dr Jin is a Fellow of IEEE.

Pascal Kerschke

Pascal Kerschke is a PostDoc at the group of Information Systems and Statistics at the University of Muenster (Germany). Prior to completing his PhD studies in 2017, he has received a M.Sc. degree in "Data Sciences" (2013) from the Faculty of Statistics at the TU Dortmund (Germany). His main research interests are algorithm selection (for continuous or TSP problems), as well as Exploratory Landscape Analysis (ELA) for single- and multi-objective, continuous (Black-Box) optimization problems. Furthermore, he is one of the developers of related R-packages, such as "flacco", "smoof" and "mlr".

Marcus Gallagher

Dr Marcus Gallagher is an Associate Professor in the School of Information Technology and Electrical Engineering at the University of Queensland, Brisbane Australia. He was awarded his PhD from the University of Queensland in 2000. His main research interests are in the areas of Optimization, Metaheuristics, Evolutionary Computation and Machine Learning. More specifically, he is interested in the intersections between these areas, including the use of probabilistic models in black-box optimization algorithms (e.g. Estimation of Distribution Algorithms) and using machine learning and data-driven techniques to better understand the nature of optimization problems and algorithms.

Mike Preuss

Mike Preuss is Research Associate at ERCIS, University of Muenster, Germany. Previously, he was with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization. He is also active in computational intelligence methods for computer games, especially in procedural content generation (PGC) and realtime strategy games (RTS).

Olivier Teytaud

Olivier Teytaud (https://www.lri.fr/~teytaud/) is research scientist at Facebook. He has been working in numerical optimization in many real-world contexts - scheduling in power systems, in water management, hyperparameter optimization for computer vision and natural language processing, parameter optimization in reinforcement learning. He is currently maintainer of the open source derivative free optimization platform of Facebook AI Research (https://github.com/facebookresearch/nevergrad), containing various flavors of evolution strategies, Bayesian optimization, sequential quadratic programming, Cobyla, Nelder-Mead, differential evolution, particle swarm optimization, and a platform of testbeds including games, reinforcement learning, hyperparameter tuning and real-world engineering problems.

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 an Associate Professor in Computational Intelligence 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 theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

Fernando Lezama

Fernando Lezama received an M.Sc. degree (with Honors) in Electronic Engineering (2011), and a Ph.D. in ITCs (2014) both from the Monterrey Institute of Technology and Higher Education (ITESM), Mexico. Currently, he is a researcher at GECAD, Polytechnic of Porto, Portugal, where he works in the development of intelligent systems for optimization in smart grids. His research interests include computational intelligence, evolutionary computation, and optimization of smart grids and optical networks.

Joao Soares

João Soares has a BSc in computer science and a master in Electrical Engineering in Portugal, namely Polytechnic of Porto. He attained his PhD degree in Electrical and Computer Engineering at UTAD university. He his a researcher at GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development. His research interests include optimization in power and energy systems, including heuristic, hybrid and classical optimization.

Zita Vale

Zita Vale is full professor at the Polytechnic Institute of Porto and the director of the Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD). She received her diploma in Electrical Engineering in 1986 and her PhD in 1993, both from University of Porto. Zita Vale works in the area of Power and Energy Systems, with special interest in the application of Artificial Intelligence techniques. She has been involved in more than 50 funded projects related to the development and use of Knowledge-Based systems, Multi-Agent systems, Genetic Algorithms, Neural networks, Particle Swarm Intelligence, Constraint Logic Programming and Data Mining.
She published over 800 works, including more than 100 papers in international scientific journals, and more than 500 papers in international scientific conferences.