William B. Langdon
William B. Langdon has been working with genetic programming since 1993. His current research uses GP to genetically improve existing software, CUDA, search based software engineering and Bioinformatics. Indeed GI has been used to significantly improve a widely used bioinformatics tool, nVidia software running on graphics hardware and a GPU kernel for NMR medical imaging registration. He co-organised the computational intelligence on GPUs (CIGPU) series of workshops, the first EvoPAR track in the European conference on applications of evolutionary computation and last year was co-chair of the GECCO genetic programming track. His books include A Field Guide to Genetic Programming, Foundations of Genetic Programming and Advances in Genetic Programming 3. He also maintains the genetic programming bibliography.

David R. White
He is a researcher in the School of Computing Science at the University of Glasgow. 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. He is on the steering committee of SSBSE and has won two best paper awards for work in evolutionary search.

Justyna Petke
Dr. Petke has a DPhil 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 applications of genetic improvement. One of her recent papers was awarded a HUMIE at this year's GECCO in Vancouver. Dr. Petke is co-organising the First North American Search Based Software Engineering Symposium (to be held in February). 

Frank Moore
He 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 17 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.

Nur Zincir-Heywood
Dr. 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.

José Santos
He 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
She 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 research includes theoretical and empirical work related to the development and use of data-mining and optimization approaches in a variety of application areas including computational biology. She has a particular interest in improving the optimization approaches employed in fragment-assembly approaches to protein structure.

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.

David Walker            
He 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.

Karthik Kuber
He 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.

John R. Woodward
He is 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
He is an Associate Professor in the Department of Computer Science at the Missouri University of Science and Technology (S&T), on sabbatical at Sandia National Laboratories for the 2014-2015 academic year, a former Guest Scientist at Los Alamos National Laboratory (LANL), the founding director of S&T's Natural Computation Laboratory (http://web.mst.edu/~tauritzd/nc-lab/), 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, COMPSAC 2011 Doctoral Symposium Chair, GECCO 2012 GA Track Co-Chair, and GECCO 2013 GA Track Co-Chair. 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 search-based software engineering. 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.

Stefan Wagner
He received his MSc in computer science in 2004 and his PhD in technical sciences in 2009, both from the Johannes Kepler University Linz, Austria. From 2005 to 2009 he worked as an associate professor for software project engineering and since 2009 as a full professor for complex software systems at the University of Applied Sciences Upper Austria, Campus Hagenberg, Austria. Dr. Wagner is one of the founders of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) and is the project manager and head developer of the HeuristicLab optimization environment.

Michael Affenzeller
He has published several papers, journal articles and books dealing with theoretical and practical aspects of evolutionary computation, genetic algorithms, and meta-heuristics in general. In 2001 he received his PhD in engineering sciences and in 2004 he received his habilitation in applied systems engineering, both from the Johannes Kepler University Linz, Austria. Michael Affenzeller is professor at the University of Applied Sciences Upper Austria, Campus Hagenberg, and head of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL).

Youhei Akimoto
He is an assistant professor at Shinshu University, Japan. He received his diploma (2007) in computer science and his master degree (2008) and PhD (2011) in computational intelligence and systems science from Tokyo Institute of Technology, Japan. He was a research fellow of Japan Society for the Promotion of Science for one year (2010-2011) at Tokyo Institute of Technology. Afterwords, he was a post-doctoral research fellow at INRIA Saclay Ile-de-France (2011-2013) and started working at Shinshu University in April 2013. His research interests include design principles and theoretical analysis of stochastic search heuristics, especially the Covariance Matrix Adaptation Evolution Strategy.

Anne Auger
She is a permanent researcher at the French National Institute for Research in Computer Science and Control (INRIA). She received her diploma (2001) and PhD (2004) in mathematics from the Paris VI  University. Before to join INRIA, she worked for two years (2004-2006) at ETH in Zurich. Her main research interest is stochastic continuous optimization including theoretical aspects and algorithm designs. She is a member of ACM-SIGECO executive committee and of the editorial board of Evolutionary Computation. She has been organizing the biannual Dagstuhl seminar "Theory of Evolutionary Algorithms" in 2008 and 2010 and the BBOB workshops in 2009, 2010, 2012, and 2013.

Dimo Brockhoff
He received his diploma in computer science from University of Dortmund, Germany in 2005 and his PhD (Dr. sc. ETH) from ETH Zurich, Switzerland in 2009. Afterwards, he held two postdoctoral research positions in France at INRIA Saclay Ile-de-France (2009-2010) and at Ecole Polytechnique (2010-2011). Since November 2011, he has been a permanent researcher at INRIA Lille - Nord Europe, France. His research interests are focused on evolutionary multiobjective optimization (EMO), in particular on theoretical aspects of indicator-based search and on the benchmarking of blackbox algorithms in general. Dimo already served as co-organizer of the BBOB workshop in 2013.

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

Olaf Mersmann
He recieved his BSc and MSc in Statistics from the TU Dortmund. He is currently pursuing a PhD in Statistics (expected in early 2015) with work based on his Bachelor Thesis on the design and analysis of benchmark experiments. Part of this work has been presented at conferences and is currently under review for publication. Using statistical and machine learning methods on large benchmark databases to gain insight into the structure of the algorithm choice problem is one of his current research interests. Olaf already co-organized the BBOB workshop in 2010 and 2012.

Petr Pošik
He received his Diploma degree in Technical Cybernetics in 2001 and Ph.D. in Artificial Intelligence and Biocybernetics in 2007, both from the Czech Technical University in Prague, Czech Republic. From 2001 to 2004 he also worked as statistician, analyst and lecturer for StatSoft, Czech Republic. Since 2005 he works as a researcher in the Intelligent Data Analysis Lab, Department of Cybernetics at the Czech Technical University. Being on the boundary of optimization, statistics and machine learning, his research interests are aimed at improving the characteristics of evolutionary algorithms with techniques of statistical machine learning. He also serves as a reviewer for several journals and conferences in the evolutionary-computation field.

Tea Tušar
She is a research assistant at the Department of Intelligent Systems at the Jožef Stefan Institute in Ljubljana, Slovenia. She received her PhD degree from the Jožef Stefan International Postgraduate School in 2014. Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with applications in optimization of metallurgical production processes and design of alternative energy supply systems, and machine learning methods for text processing and outlier detection. Recently, she has been researching visualization techniques for viewing multidimensional Pareto front approximations found by multiobjective optimizers.

Boris Naujoks
He is a professor for Applied Mathematics at 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 focused on multiobjective (evolutionary) optimization, in particular hypervolume based algorithms, and the (industrial) applicability of the explored methods.

Abraham Prieto Garcia
He is an Associate Professor at the University of A Coruña, Spain. He is a member of the Integrated Group for Engineering Research (GII) of the same university and leads the Collective Systems Section within the GII. He graduated in 2002 and obtained his Master Degree in 2004 in Industrial Engineering. In 2009 he obtained a Magna Cum Laude for his PhD Thesis in the field of optimization techniques for distributed problems in engineering. He started his collaboration with the GII in 2004 developing projects related with Intelligent Processing of images and signals and with the optimization of distributed systems. In 2005 he became Assistant Professor and then in 2010 he gained the Associate Professor position. Regarding his research work he is author of several papers in journals, international conferences and book chapters. He has participated in numerous national and regional research projects from public calls, many of them in collaboration with private companies. His research covers the following fields: bio-inspired techniques for distributed problems, evolutionary robotics and image and signal intelligent processing.

Nicolas Bredeche
He is Professeur des Universites (Professor) at Universite Pierre et Marie Curie (UPMC, Paris, France), His research is done in the team Architectures and Models for Adaptation and Cognition, within the Institute of Intelligent Systems and Robotics (ISIR, CNRS). His field of research is mostly concerned with Evolutionary Computation and Complex Systems (self-adaptive collective robotic systems, generative and developmental systems, evolutionary robotics). In particular, part of his work is about (a) evolutionary design and/or adaptation of group of embodied agents and (b) artificial ontogeny such as multi-cellular developmental systems and self-regulated networks.

Nicolas Bredeche is author of more than 30 papers in journals and major conferences in the field. He has (or currently is) advisor or co-advisor of six PhD students and has served in program committees and/or as track chair of the major conferences in the field of Evolutionary Computation (incl. GECCO track chair for the Artificial Life and Robotics track 2012/2013). He is also a member of the french Evolution Artificielle association and has been regularly co-organizing the french Artificial Evolution one-day seminar (JET) since 2009. He has also organized several international workshops in Evolution, Robotics, and Development of Artificial Neural Networks.

Evert Haasdijk
He is assistant professor in the Computational Intelligence group at VU. He has been with the computational intelligence group at VU since 2008, researching on-line evolution in robots. Before that, he was research assistant at Tilburg University, researching social learning in populations of software agents. Dr Haasdijk has ample experience in evolutionary computation, stretching back to the successful PAPAGENA project in 1992, where he participated as an industry partner. He has served as member of program committees of well-established conferences in the field of evolutionary computation (CEC, GECCO), was local chair for GECCO 2013 and (co-)organised various workshops and track such as the EvoROBOT track at EvoSTAR conferences and the International Workshop on the Evolution of Physical Systems at ECAL and ALIFE conferences. Dr Haasdijk was guest editor for the Special Issue on Evolutionary Robotics of the Evolutionary Intelligence journal and invited speaker at the PPSN XIII Workshop on Nature-Inspired Techniques for Robotics.

My current research focuses on  developing algorithms that continuously learn and improve through  the experience gained from solving many problems over time. In this talk, I will discuss some of the scientific ideas behind this research, but also describe the process of arriving at this point in my academic life, through a journey that has involved life-long learning on my own part, moving from Chemistry to Evolutionary Computing to Artificial Immune Systems to something that brings together all three fields in naby  respects.

Bio:
Emma Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh in 1996. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems.

She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a Chair in 2008 in Natural Computation. She leads the Centre for Algorithms, Visualisation and Evolving Systems with the Institute for Informatics&  Digital Innovation at Edinburgh Napier. Her research focuses on the development of novel bio-inspired techniques for solving a range of real-world optimisation and classification problems, as well as exploring the fundamental properties of immune-inspired computing through modelling and simulation. Her current research interests are focused on systems that can continue learning over their lifetime, with applications in optimisation and robotics. This brings together keys ideas from the AIS and Evolutionary Computing communities. In addition to academic research, she is also involved in Knowledge Transfer and Commercial activities, applying her research to real problems in industry.

Carola Doerr
,  http://www-ia.lip6.fr/~doerr/
Carola is a permanent researcher with the CNRS and the Université Pierre et Marie Curie (Paris 6). She studied mathematics at Kiel University (Diploma in 2007) and computer science at the Max Planck Institute for Informatics and Saarland University (PhD in 2011). From Dec. 2007 to Nov. 2009, Carola Doerr has worked as a business consultant for McKinsey & Company, mainly in the area of network optimization. She was a post-doc at the Université 7 in Paris and the Max Planck Institute for Informatics in Saarbrücken. Carola Doerr's main research interest is in the theory of randomized algorithms, both in the design of efficient algorithms as well as in randomized query complexities. She has published several papers in the field of evolutionary computation.

Anna Esparcia

Affiliation: Universitat Politècnica de València, Spain
Anna has the role of vice chair of the Women@GECCO group. She is chair of GECCO 2015 so will take a modest role this year in organizating the meeting.

Gabriela Ochoa

Affiliation: Computing Science and Mathematics, School of Natural Sciences, University of Stirling, Scotland.
Gabriela Ochoa is a Lecturer in Computing Science at the University of Stirling in Scotland. Her research interests lie in the foundations and application of evolutionary algorithms and heuristic search methods, with emphasis on autonomous (self-*) search and fitness landscape analysis. She has published over 70 international peer reviewed papers. She is associate editor of Evolutionary Computation (MIT Press), was involved in founding the Self-* Search track at GECCO, co-chaired EvoCOP 2014 and has organised several special sessions at international conferences. She serves as secretary of the Women@GECCO group.

Una-May O'Reilly

Affiliation: Massachusetts Institute of Technology
Una-May is a Fellow of the International Society of Genetic and Evolutionary Computation, now ACM Sig-EVO.  In 2013 she received the EVO-Star award recognizing her contributions to evolutionary computation. At MIT CSAIL, Una-May leads the AnyScale Learning For All (ALFA) research group.  She has chaired or co-chaired GECCO, EuroGP and GPTP. She serves as vice-chair of ACM SIGEVO, the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines, an editorial board member of Evolutionary Computation, and action editor for the Journal of Machine Learning Research. In 2013, with Anna Esparcia, Anniko Ekart and Gabriela Ochoa she inaugurated the Women@GECCO meeting and chairs the group.

Nur Zincir-Heywood
Dr. 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 was 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.

Christine Zarges 
Christine is a Birmingham Fellow and Lecturer in the School of Computer Science at the University of Birmingham, UK. She 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. 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.

Chris Simons
He spent many years as an industrial software development practitioner in a variety of roles including technical architect, agile development methodology consultant and trainer. Now a senior lecturer, his research interests lie in human-centred bio-inspired metaheuristics and Search-Based Software Engineering (SBSE).

Daniel Tauritz
He is an Associate Professor of Computer Science at the Missouri University of Science and Technology (S&T). He served previously as GECCO 2010 Late Breaking Papers Chair and GECCO 2012 & 2013 GA Track Co-Chair. 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 search-based software engineering.

Jim Smith
He has been researching meta-heuristics since 1994.  He has published extensively on formalisms of metaheuristic components and design patterns, especially for adaptive and self-adaptive evolutionary and memetic systems.

Colin Johnson
He is a Reader in the School of Computing at the University of Kent. He has been active in bio-inspired computing research for the last 15 years, and in recent years has been particularly focused on genetic programming, with a substantial publication record in the area.  He has been active in conferences within the computational intelligence area, including membership of the programme committee for a number of conferences in the area, and would bring this experience to the organisation of this workshop.

Krzysztof Krawiec
He is an associate professor in the Institute of Computing Science, Poznan University of Technology, Poland, pursuing research in several branches of computational intelligence, primarily evolutionary computation, machine learning, and pattern recognition. He has been publishing in the GP field for about 13 years, and since 2008 he is actively developing various GP methods that involve program semantics. He served as a co-chair of the European Conference of Genetic Programing (EuroGP) in 2012 and 2013, and is an associate editor of Genetic Programming and Evolvable Machines journal.

Michael O’Neill
He is the ICON Chair of Business Analytics in the UCD School of Business, is a founding Director of the UCD Natural Computing Research & Applications group, and is Director of the UCD Complex & Adaptive Systems Laboratory (The CASL Institute). He has published in excess of 250 peer-reviewed publications. Michael was Local Chair of GECCO 2011, which was held in Dublin.  In the past he has served as Chair of the Genetic Programming Track (GECCO 2010), Chair of the Real World Applications Track (GECCO 2009), and was also Chair of the Grammatical Evolution Workshops (GEWS 2002-2004), and Chair of the SIGEVO Graduate Student Workshop 2005. He has co-authored a number of successful funding applications with a total value over €9 Million. In recent years his team has conducted research focusing on syntax and semantics in genetic programming.

Stephen L. Smith
He received a BSc in Computer Science and then an MSc and PhD in Electronic Engineering from the University of Kent, UK. He is currently a 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. 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. He has some 75 refereed publications, is a Chartered Engineer and a fellow of the British Computer Society.