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Workshops



Two days of free tutorials and workshops (included with conference registration) presented by some of the world's foremost experts in topics of interest to genetic and evolutionary computation researchers and practitioners.

Preliminary Schedule for Workshops

Paper submission deadline: April 3, 2016
Decision notification: April 23, 2016
Camera ready articles due: May 11, 2016

List of Workshops

TitleOrganizers
6th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)
  • John R. Woodward University of Stirling, UK
  • Daniel R. Tauritz Missouri University of Science and Technology
  • Manuel López-Ibáñez University of Manchester, UK
Algorithms and Data Structures for Evolutionary Computation
  • Maxim Buzdalov ITMO University
Bi-Objective Black Box Optimization Benchmarking 2016 (BO-BBOB 2016)
  • Anne Auger Inria Saclay-Ile-de-France
  • Dimo Brockhoff INRIA Lille - Nord Europe, France
  • Nikolaus Hansen INRIA Saclay, France
  • Tea Tušar INRIA Lille – Nord Europe, France
  • Dejan Tušar Inria Lille
  • Tobias Wagner Technische Universität Dortmund
Evolution in Action!
  • Una-May O'Reilly Massachusetts Institute of Technology, US
  • Erik Hemberg
  • Nicole Hoffman Massachusetts Institute of Technology
Evolution in Cognition
  • Stéphane Doncieux Université Pierre et Marie Curie
  • Joshua Auerbach École Polytechnique Fédérale de Lausanne (EPFL)
  • Richard Duro Universidade da Coruna, Spain
  • Harold de Vladar Parmenides Foundation
Evolutionary Computation in Computational Structural Biology
  • José Santos University of A Coruña, Spain
  • Julia Handl University of Manchester, UK
  • Amarda Shehu George Mason University, Fairfax, VA
Evolutionary Computation Software Systems (EvoSoft)
  • Stefan Wagner University of Applied Sciences Upper Austria
  • Michael Affenzeller University of Applied Sciences Upper, Austria
GECCO Student Workshop
  • Vanessa Volz TU Dortmund University
  • Samadhi Nallaperuma University of Sheffield, UK
Genetic and Evolutionary Computation in Defense Security and Risk Management
  • Frank Moore University of Alaska Anchorage, USA
  • gunes.kayacik Qualcomm Research Silicon Valley, USA
Genetic Improvement Workshop
  • Westley Weimer University of Virginia
  • Justyna Petke University College, London, UK
  • David R. White University of Glasgow, UK
Industrial Applications of Metaheuristics (IAM)
  • Silvino Fernandez Alzueta ArcelorMittal
  • Pablo Valledor Pellicer
  • Thomas Stützle IRIDIA laboratory, ULB, Belgium
International Workshop on Evolutionary Rule-based Machine Learning (Former International Workshop on Learning Classifier Systems)
  • Kuber Karthik Microsoft, Redmond, Washington, US
  • Masaya Nakata The University of Electro-Communications, Japan
  • Shafi Kamran University of New South Wales, Australia
Measuring and Promoting Diversity in Evolutionary Algorithms
  • Giovanni Squillero Politecnico di Torino
  • alberto.tonda UMR 782 GMPA, INRA, Thiverval-Grignon, France
Medical Applications of Genetic and Evolutionary Computation (MedGEC)
  • Stephen L. Smith University of York, UK
  • Stefano Cagnoni Universita' degli Studi di Parma, Italy
  • Robert M. Patton Oak Ridge National Laboratory, USA
Model-Based Evolutionary Algorithms (MBEA)
  • Peter A.N. Bosman Centre for Mathematics and Computer Science, The Netherlands
  • John McCall
Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2016)
  • David Walker University of Exeter, UK
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
Women@GECCO Workshop
  • Carola Doerr CNRS & Universite Pierre et Marie Curie (Paris 6), France
  • Julia Handl University of Manchester, UK
  • Emma Hart Edinburgh Napier University
  • Gabriela Ochoa Computing Science and Mathematics, School of Natural Sciences, University of Stirling, Scotland
  • Amarda Shehu George Mason University, Fairfax, VA
  • Tea Tušar INRIA Lille – Nord Europe, France
  • Christine Zarges University of Birmingham, UK
  • Nur Zincir-Heywood Dalhousie University, Canada
Workshop on Surrogate-Assisted Evolutionary Optimisation (SAEOpt 2016)
  • alma.rahat University of Exeter
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
  • Handing Wang University of Surrey
  • Yaochu Jin

6th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)

http://web.mst.edu/~tauritzd/ECADA/GECCO2016/

Summary

The main objective of this workshop is to discuss hyper-heuristics and related methods, including but not limited to evolutionary computation methods, for generating and improving algorithms with the goal of producing solutions (algorithms) that are applicable to multiple instances of a problem domain. The areas of application of these methods include optimization, data mining and machine learning.

Automatically generating and improving algorithms by means of other algorithms has been the goal of several research fields, including Artificial Intelligence in the early 1950s, Genetic Programming in the early 1990s, and more recently automated algorithm configuration and hyper-heuristics. The term hyper-heuristics generally describes meta-heuristics applied to a space of algorithms. While Genetic Programming has most famously been used to this end, other evolutionary algorithms and meta-heuristics have successfully been used to automatically design novel (components of) algorithms. Automated algorithm configuration grew from the necessity of tuning the parameter settings of meta-heuristics and it has produced several powerful (hyper-heuristic) methods capable of designing new algorithms by either selecting components from a flexible algorithmic framework or recombining them following a grammar description.

Although most Evolutionary Computation techniques are designed to generate specific solutions to a given instance of a problem, one of the defining goals of hyper-heuristics is to produce solutions that solve more generic problems. For instance, while there are many examples of Evolutionary Algorithms for evolving classification models in data mining and machine learning, there is research which employed a hyper-heuristic using Genetic Programming to create a generic classification algorithm which in turn generates a specific classification model for any given classification dataset, in any given application domain. In other words, the hyper-heuristic is operating at a higher level of abstraction compared to how most search methodologies are currently employed; i.e., it is searching the space of algorithms as opposed to directly searching in the problem solution space, raising the level of generality of the solutions produced by the hyper-heuristic evolutionary algorithm. In contrast to standard Genetic Programming, which attempts to build programs from scratch from a typically small set of atomic functions, hyper-heuristic methods specify an appropriate set of primitives (e.g., algorithmic components) and allow evolution to combine them in novel ways as appropriate for the targeted problem class. While this allows searches in constrained search spaces based on problem knowledge, it does not in any way limit the generality of this approach as the primitive set can be selected to be Turing-complete. Typically, however, the initial algorithmic primitive set is composed of primitive components of existing high-performing algorithms for the problems being targeted; this more targeted approach very significantly reduces the initial search space, resulting in a practical approach rather than a mere theoretical curiosity. Iterative refining of the primitives allows for gradual and directed enlarging of the search space until convergence.

As meta-heuristics are themselves a type of algorithm, they too can be automatically designed employing hyper-heuristics. For instance, in 2007, Genetic Programming was used to evolve mate selection in evolutionary algorithms; in 2011, Linear Genetic Programming was used to evolve crossover operators; more recently, Genetic Programming was used to evolve complete black-box search algorithms. Moreover, hyper-heuristics may be applied before deploying an algorithm (offline) or while problems are being solved (online), or even continuously learn by solving new problems (life-long). Offline and life-long hyper-heuristics are particularly useful for real-world problem solving where one can afford a large amount of a priori computational time to subsequently solve many problem instances drawn from a specified problem domain, thus amortizing the a priori computational time over repeated problem solving.

Very little is known yet about the foundations of hyper-heuristics, such as the impact of the meta-heuristic exploring algorithm space on the performance of the thus automatically designed algorithm. An initial study compared the performance of algorithms generated by hyper-heuristics powered by five major types of Genetic Programming.

Biographies

John R. Woodward

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

Daniel R. Tauritz

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

Manuel López-Ibáñez

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

Algorithms and Data Structures for Evolutionary Computation

http://genome.ifmo.ru/evo-algo-ds/gecco16/

Summary

In a sense, evolutionary computation is a rather mature field now. One of the signs is that, unlike early years, for optimization in many domains we use algorithms which have many non-trivial and complicated parts. Examples of such algorithms, just to name a few, are CMA-ES and NSGA-II. It is no more obvious how to implement these efficiently, and which algorithms and data structures to use to obtain better performance.

This workshop is dedicated to algorithms and data structures which can be used in implementations of evolutionary algorithms to enhance their efficiency without changing their behavior. We seek to merge the latest advances from classic computer science into evolutionary computation, as well as all the way backwards - to force development of certain new computer science directions motivated by needs of evolutionary computation.

Scopes of interest include, but of course are not limited, to:

  • efficient algorithms for processing multi- and many-objective solution sets;
  • data structures which support incremental updates and enable efficient steady-state evolutionary algorithms;
  • algorithms and data structures for parallel and asynchronous evolutionary computation;
  • efficient evolutionary operators and efficient sampling of various distributions;
  • algorithms and data structures for enhancing domain-specific evolutionary algorithms.

Biographies

Maxim Buzdalov

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

Bi-Objective Black Box Optimization Benchmarking 2016 (BO-BBOB 2016)

Summary

The Black-Box-Optimization Benchmarking (BBOB) methodology associated to the BBOB-GECCO workshops has become a well-established standard for benchmarking stochastic and deterministic continuous optimization algorithms in recent years (http://coco.gforge.inria.fr/). So far, the BBOB-GECCO workshops have covered benchmarking of blackbox optimization algorithms for single-objective, unconstrained problems in exact and noisy, as well as expensive and non-expensive scenarios. A substantial portion of the success can be attributed to the Comparing Continuous Optimization benchmarking platform (COCO) that builds the basis for all BBOB-GECCO workshops and that automatically allows algorithms to be benchmarked and performance data to be visualized effortlessly.

As the next natural step, we propose an extension of COCO to problems with more than one objective function. For the first time, we want to hold a BBOB-GECCO workshop that covers a testbed containing bi-objective continuous optimization problems. This planned bi-objective BBOB workshop aims at benchmarking both stochastic and deterministic continuous optimization algorithms in an anytime scenario for unconstrained bi-objective optimization problems under (i) usual budgets and (ii) possibly in expensive settings where only a limited budget is affordable (e.g. (meta-)model assisted algorithms).

Like for the previous editions of the workshop, we will provide source code in various languages (planned are C, Matlab, Java, and Python) to benchmark algorithms, as well as for postprocessing data and comparing one algorithm performance to others (up to already prepared LaTeX templates for writing papers).

Interested participants of the workshop are invited to submit a paper with the results of any multiobjective black-box optimization algorithm of their choice on the provided testbed. We encourage particularly submissions related to expensive optimization (with a limited budget) and also algorithms from outside the evolutionary computation community. Please note that submissions related to the existing single-objective BBOB testbeds are still welcome although the focus will be on the new bi-objective testbed.

Biographies

Anne Auger

Anne Auger is a permanent researcher at the French National Institute for Research in Computer Science and Control (INRIA). She received her diploma (2001) and PhD (2004) in mathematics from the Paris VI University. Before to join INRIA, she worked for two years (2004-2006) at ETH in Zurich. Her main research interest is stochastic continuous optimization including theoretical aspects and algorithm designs. She is a member of ACM-SIGECO executive committee and of the editorial board of Evolutionary Computation. She has been organizing the biannual Dagstuhl seminar "Theory of Evolutionary Algorithms" in 2008 and 2010 and served as track chair for the theory and ES track in 2011, 2013 and 2014. Together with Benjamin Doerr, she is editor of the book "Theory of Randomized Search Heuristics".

Dimo Brockhoff

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

Nikolaus Hansen

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

Tea Tušar

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

Dejan Tušar

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

Tobias Wagner

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

Evolution in Action!

http://evolutioninaction.mit.edu/events

Summary

Taught by Una-May O’Reilly, PhD and Erik Hemberg, PhD; Evolution in Action! is an active learning workshop introducing computational thinking for kids 10-14. The exercises teach the fundamentals of computational thinking by having the kids or act out algorithms in the classroom. This workshop encourages kids to think logically and computationally, teaching how to problem-solve without using a computer!

The Evolution in Action! workshop offers kids a fun way to explore concepts related to computational thinking by participating in open-ended, group exercises that integrate concepts from game theory and evolution. Evolution in Action! exercises administer simple but profound ways for kids to think about the procedural aspects of processes and systems they see in the world. We believe there's a lot of computation going on in the world and our goal is to develop with each child an implicit awareness of this in subtle, kid-friendly ways.

The Evolution in Action! workshop exercises are a great example of how computer science and computational thinking can be introduced and integrated with different facets of learning. To promote success in the modern world, we must foster innovation, invention, and problem solving. To do this our kids need to learn to think in a computational way, acquiring the ability to tackle open ended problems which are so common in the world.

All children must be accompanied by an adult. All children must be registered for this workshop to attend. Email us at evoinact at mit.edu to register. Registration deadline for Evolution in Action! is July 10.

Biographies

Una-May O'Reilly

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

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

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

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

Erik Hemberg

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

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

Nicole Hoffman

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

Evolution in Cognition

http://lis2.epfl.ch/events/workshops/GECCO2016/

Summary

Evolution by natural selection has shaped life over billions of years leading to the emergence of complex organism capable of exceptional cognitive abilities. These natural evolutionary processes have inspired the development of Evolutionary Algorithms (EAs), which are optimization algorithms widely popular due to their efficiency and robustness. Beyond their ability to optimize, EAs have also proven to be creative and efficient at generating innovative solutions to novel problems. The combination of these two abilities makes them a tool of choice for the resolution of complex problems.
Even though there is evidence that the principle of selection on variation is at play in the human brain, as proposed in Changeux’s and Edelman’s models of Neuronal Darwinism, and more recently expanded in the theory of Darwinian Neurodynamics by Szathmáry, Fernando and others, not much attention has been paid to the possible interaction between evolutionary processes and cognition over physiological time scales. Since the development of human cognition requires years of maturation, it can be expected that artificial cognitive agents will also require months if not years of learning and adaptation. It is in this context that the optimizing and creative abilities of EAs could become an ideal framework that complement, aid in understanding, and facilitate the implementation of cognitive processes. Additionally, a better understanding of how evolution can be implemented as part of an artificial cognitive architecture can lead to new insights into cognition in humans and other animals.

The goals of the workshop are to depict the current state of the art of evolution in cognition and to sketch the main challenges and future directions. In particular, we aim at bringing together the different theoretical and empirical approaches that can potentially contribute to the understanding of how evolution and cognition can act together in an algorithmic way in order to solve complex problems. In this workshop we welcome approaches that contribute to an improved understanding of evolution in cognition using robotic agents, i​n silico c​omputation as well as mathematical models.

Biographies

Stéphane Doncieux

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

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

Joshua Auerbach

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

Richard Duro

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

Harold de Vladar

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

Evolutionary Computation in Computational Structural Biology

http://eccsb2016.irlab.org/

Summary

In the last two decades, many computer scientists in Artificial Intelligence have made significant contributions to modeling biological systems as a means of understanding the molecular basis of mechanisms in the healthy and diseased cell. In particular, the field of computational structural biology is now highly populated by researchers in evolutionary computation. Great progress is being made by these researchers on novel and powerful algorithms to solve exceptionally challenging computational structural biology problems at the heart of molecular biology, such as structure prediction, analysis, and design of biological macromolecules (proteins, RNA). These problems pose difficult search and optimization tasks on modular systems with vast, high-dimensional, continuous search spaces often underlined by non-linear multimodal energy surfaces.

The focus of this workshop is the use of nature-inspired approaches to central problems in computational structural biology, including optimization methods under the umbrella of evolutionary computation. A particular emphasis will be on progress in the application of evolutionary computation to problems related to any aspects of protein structure modeling, characterization, and analysis. The workshop will allow for a broader focus on all structure-related problems that necessitate the design of novel evolutionary computation approaches. These may include broader structure modeling settings beyond de novo structure prediction, such as mapping of protein and peptide energy landscapes, structure analysis, design, docking, and other emerging problems in computational structural biology.

Following the previous edition in GECCO 2015, one of the objectives of this workshop is to aid evolutionary computation researchers to disseminate recent findings and progress. The workshop will provide a meeting point for authors and attendants of the GECCO conference who have a current or developing interest in computational biology. We believe the workshop will additionally attract computational biology researchers that will further add to the attendance and GECCO community and possibly spur novel collaborations. We hope this workshop will stimulate the free exchange and discussion of novel ideas and results related to structure-central problems bridging computational biology and evolutionary computation.

Areas of interest include (but are not restricted to):
• Use of artificial life models like cellular automata or Lindenmayer systems in the modeling of biological problems.
• Study and analysis of properties of biological systems like self-organization, emergent behavior or morphogenesis.
• Multi-objective approaches in the modeling of computational biology problems.
• Use of natural and evolutionary computation algorithms in protein structure classification and prediction (secondary and tertiary).
• Mapping of protein and peptide energy landscapes.
• Modeling of temporal folding of proteins.
• Protein design.
• Protein-ligand and protein-protein docking.
• Evolutionary search strategies to assist cryo-electron microscopy and other experimental techniques in model building.

Biographies

José Santos

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

Julia Handl

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

Amarda Shehu

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

Evolutionary Computation Software Systems (EvoSoft)

http://evosoft.heuristiclab.com

Summary

Evolutionary computation (EC) methods are applied in many different domains. Therefore soundly engineered, reusable, flexible, user-friendly, and interoperable software systems are more than ever required to bridge the gap between theoretical research and practical application. However, due to the heterogeneity of the application domains and the large number of EC methods, the development of such systems is both, time consuming and complex. Consequently many EC researchers still implement individual and highly specialized software which is often developed from scratch, concentrates on a specific research question, and does not follow state of the art software engineering practices. By this means the chance to reuse existing systems and to provide systems for others to build their work on is not sufficiently seized within the EC community. In many cases the developed systems are not even publicly released, which makes the comparability and traceability of research results very hard. This workshop concentrates on the importance of high-quality software systems and professional software engineering in the field of EC and provides a platform for EC researchers to discuss the following and other related topics:

  • development and application of generic and reusable EC software systems
  • architectural and design patterns for EC software systems
  • software modeling of EC algorithms and problems
  • open-source EC software systems
  • expandability, interoperability, and standardization
  • comparability and traceability of research results
  • graphical user interfaces and visualization
  • comprehensive statistical and graphical results analysis
  • parallelism and performance
  • usability and automation
  • comparison and evaluation of EC software systems

Biographies

Stefan Wagner

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

Michael Affenzeller

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

GECCO Student Workshop

https://ls11-www.cs.tu-dortmund.de/people/volz/gecco2016studentWS.html

Summary

The goal of the Student Workshop is to support students with their first scientific publication and facilitate their inclusion in the research community. Students will receive valuable feedback on the quality of their work and their presentation style. This will be assured by having discussions after each talk led by a mentor panel of established researchers. Students are encouraged to use this opportunity also to get guidance regarding future research directions. In addition, the contributing students are invited to present their work as a poster at the poster session - an excellent opportunity to discuss their work with a broader audience and to network with academic as well as industrial members of the community. Last, but not least, the best contributions will compete for a Best Student Paper Award.

Biographies

Vanessa Volz

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

Samadhi Nallaperuma

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

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

Genetic and Evolutionary Computation in Defense Security and Risk Management

http://kayacik.ca/~secdefworkshop/

Summary

With the constant appearance of new threats, research in the areas of defense, security and risk management has acquired an increasing importance over the past few years. These new challenges often require innovative solutions and Computational Intelligence techniques can play a significant role in finding them.

We seek both theoretical developments and applications of Genetic and Evolutionary Computation and their hybrids to the following (and other related) topics:
• Cyber-crime and cyber-defense : anomaly detection systems, attack prevention and defense, threat forecasting systems, anti-spam, antivirus systems, cyber warfare, cyber fraud
• IT Security: Intrusion detection, behavior monitoring, network traffic analysis
• Risk management: identification, prevention, monitoring and handling of risks, risk impact and probability estimation systems, contingency plans, real time risk management
• Critical Infrastructure Protection (CIP)
• Military, counter-terrorism and other defense-related aspects

Biographies

Frank Moore

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

gunes.kayacik

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

Genetic Improvement Workshop

http://geneticimprovementofsoftware.com

Summary

The growth in GI echoes a wider trend in research on the use of evolutionary and genetic search in optimising aspects of software engineering. For example, since 2002 there has been a track on Search Based Software Engineering at GECCO. There exists the dedicated SSBSE conference, and we now see the inauguration of regional conferences and workshops featuring or even dedicated to SBSE (in Brazil, China and recently the USA). In 2015 the inaugural Genetic Improvement Workshop was held in conjunction with GECCO. The workshop was a tremendous success.

Genetic Improvement is one of the most exciting and growing applications of evolutionary search. Including "to appear", since 2000, there have been more than 70 papers in this area and interest is growing. GI research has won three GECCO Human Competitive Awards (Gold, Silver and Bronze) and two best papers, including at the International Conference on Software Engineering and GECCO. Furthermore, a special issue on Genetic Improvement in the Genetic Programming and Evolvable Machines journal is due to appear in the coming months.

Whilst SBSE has traditionally been applied to software engineering problems there has been great interest in using it, particularly genetic programming, on software itself.

Genetic Improvement (GI) uses computational search to improve software while retaining its partial functionality. The technique was first applied to parallelise programs and optimise and find compromises between non-functional properties of software, such as execution time and power consumption. This work led on to automated bug fixing in commercial software. More recently, it has been shown that GP can use human written software as a feed stock for GP and is able to evolve mutant software dedicated to solving particular problems. Another interesting area is grow and graft GP, where software is incubated outside its target human written code and subsequently grafted into it via GP.

Biographies

Westley Weimer

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

Justyna Petke

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

David R. White

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

Industrial Applications of Metaheuristics (IAM)

Summary

Metaheuristics have been applied successfully to many aspects of applied mathematics and science, showing their capabilities to deal effectively with problems that are complex and otherwise difficult to solve. There are a number of factors that make the usage of metaheuristics in industrial applications more and more interesting. These factors include the flexibility of these techniques, the increased availability of high-performing algorithmic techniques, the increased knowledge of their particular strengths and weaknesses, the ever increasing computing power, and the adoption of computational methods in applications. In fact, metaheuristics have become a powerful tool to solve a large number of real-life optimization problems in different fields and, of course, also in many industrial applications such as production scheduling, distribution planning, and inventory management.

This workshop proposes to present and debate about the current achievements of applying these techniques to solve real-world problems in industry and the future challenges, focusing on the (always) critical step from the laboratory to the shop floor. A special focus will be given to the discussion of which elements can be transferred from academic research to industrial applications and how industrial applications may open new ideas and directions for academic research.

Topics:
Areas of interest include (but are not restricted to):

  • Success stories for industrial applications of metaheuristics
  • Pitfalls of industrial applications of metaheuristics.
  • Metaheuristics to optimize dynamic industrial problems.
  • Multi-objective optimization in real-world industrial problems.
  • Meta-heuristics in very constraint industrial optimization problems: assuring feasibility, constraint-handling techniques.
  • Reduction of computing times through parameter tuning and surrogate modelling.
  • Parallelism and/or distributed design to accelerate computations.
  • Algorithm selection and configuration for complex problem solving.
  • Advantages and disadvantages of metaheuristics when compared to other techniques such as integer programming or constraint programming.
  • New research topics for academic research inspired by real (algorithmic) needs in industrial applications.

Biographies

Silvino Fernandez Alzueta

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

Pablo Valledor Pellicer

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

Thomas Stützle

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

International Workshop on Evolutionary Rule-based Machine Learning (Former International Workshop on Learning Classifier Systems)

Summary

Learning Classifier Systems (LCSs), introduced by John Holland 1 as a way of combining evolutionary computation with rule-based machine learning, have been widely applied from data mining to automated innovation and on-line control. Since then, a paradigm of evolutionary rule-based machine (ERML) learning including LCSs has been an integral part of the field of evolutionary computation almost since its very beginnings. So, this workshop is very interesting not only for the ERML community, but also the broader Genetic and Evolutionary Computation (GEC) field because it shares many common research topics with such as linkage learning, niching techniques, variable-length representations, etc. Therefore, it can attract a broader audience besides ERML practitioners. This would be the nineteenth edition of the workshop, which was initiated in 1992, held at the NASA Johnson Space Center in Houston, Texas. Since 1999, the workshop has been held yearly in conjunction with PPSN in 2000 and 2002, with GECCO in 1999, 2001, and from 2003 to 2015.

Scopes of interests include but are not limited to:
- Paradigms of LCS and ERML (Michigan, Pittsburgh, ...)
- Theoretical developments (behavior, scalability and learning bounds, ...)
- Representations (binary, real-valued, oblique, non-linear, fuzzy, ...)
- Types of target problems (single-step, multiple-step, regression/function
approximation, ...)
- System enhancements (competent operators, problem structure identification
and linkage learning, ...)
- ERML and LCS for Cognitive Control (architectures, emergent behaviors, ...)
- Applications (data mining, medical domains, bioinformatics, intelligence in
games ...)
- Optimizations and parallel implementations (GPU, matching algorithms, ...)

Biographies

Kuber Karthik

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

Masaya Nakata

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

Shafi Kamran

Dr. Shafi holds a PhD in computer science, a M.Sc. in telecoms engineering and a B.Sc. in electrical engineering. Dr. Shafi is the organising member (elected) for the International Workshop on Learning Classifier Systems (IWLCS) 2013-14 and 2015-16. He was the chair of Computational Intelligence Day workshop held at the University of New South Wales (UNSW-Canberra) Australia in September 2013. He was the publicity chair for the 2012 World Congress on Computational Intelligence (WCCI 2012). He has been a program committee member and chair/co-chair of several workshops at GECCO and IEEE CEC conferences since 2005. His PhD thesis “An online and adaptive signature-based approach for intrusion detection using learning classifier systems (LCS)” received the Stephen Fester Award for the most outstanding thesis on an information technology topic by a postgraduate research student in the School of ITEE at UNSW Canberra. His other major research achievements in the field of LCS research include the development of an LCS based scenario mining approach in the context of free- flight air traffic control concept and development of an LCS based multi-objective hyper-heuristic framework for the defence logistics problem.

Measuring and Promoting Diversity in Evolutionary Algorithms

Summary

Divergence of character is a cornerstone of natural evolution. On the contrary, evolutionary optimization processes are plagued by an endemic lack of diversity: all candidate solutions eventually crowd the very same areas in the search space. This situation has different effects on the different search algorithms, but almost all are quite deleterious. Such a “lack of speciation” has been pointed out in the seminal work of Holland in 1975, and nowadays is well known among scholars. The problem is usually labeled with the oxymoron “premature convergence”, that is, the tendency of an algorithm to convergence toward a point where it was not supposed to converge to in the first place.
Scientific literature contains several efficient methodologies for promoting diversity, that range from general techniques to problem-dependent heuristics. However, the EC community still lacks a general, comprehensive framework to deal with the problem. An essential prerequisite to promote diversity is being able to measure it, and while such a computation might be trivial for some EAs, consensus on a universal solution able to handle complex genomes, such as those used in GP and LGP, has not been reached yet. While new techniques for promoting diversity are constantly developed, novel solutions to measure diversity in convoluted representations are called for.

Biographies

Giovanni Squillero

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

alberto.tonda

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

Medical Applications of Genetic and Evolutionary Computation (MedGEC)

Summary

The Workshop focuses on the application of genetic and evolutionary
computation (GEC) to problems in medicine and healthcare.
Subjects will include (but are not limited to) applications of GEC to:

  • Medical imaging
  • Medical signal processing
  • Medical text analysis
  • Clinical diagnosis and therapy
  • Data mining medical data and records
  • Clinical expert systems
  • Modelling and simulation of medical processes
  • Drug description analysis
  • Genomic-based clinical studies
  • Patient-centric care


Although the application of GEC to medicine is not new, the reporting
of new work tends to be distributed among various technical and
clinical conferences in a somewhat disparate manner. A dedicated
workshop at GECCO provides a much needed focus for medical related
applications of EC, not only providing a clear definition of the state
of the art, but also support to practitioners for whom GEC might not
be their main area of expertise or experience.

Biographies

Stephen L. Smith

Stephen L. Smith received a BSc in Computer Science and then an MSc and PhD in Electronic Engineering from the University of Kent, UK. He is currently a reader in the Department of Electronics at the University of York, UK.

Stephen's main research interests are in developing novel representations of evolutionary algorithms particularly with application to problems in medicine. His work is currently centered on the diagnosis of neurological dysfunction and analysis of mammograms. Stephen was program chair for the Euromicro Workshop on Medical Informatics, program chair and local organizer for the Sixth International Conference on Information Processing in Cells and Tissues (IPCAT) and guest editor for the subsequent special issue of BioSystems journal. More recently he was tutorial chair for the IEEE Congress on Evolutionary Computation (CEC) in 2009, local organiser for the International Conference on Evolvable Systems (ICES) in 2010 and co-general chair for the Ninth International Conference on Information Processing in Cells and Tissues (IPCAT) in April 2012. Stephen currently holds a Royal Academy of Engineering Enterprise Fellowship.

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

Stephen is associate editor for the journal Genetic Programming and Evolvable Machines and a member of the editorial board for the International Journal of Computers in Healthcare and Neural Computing and Applications.

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

Stefano Cagnoni

Stefano Cagnoni graduated in Electronic Engineering at the University of Florence, Italy, where he has been a PhD student and a post-doc until 1997. In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology. Since 1997 he has been with the University of Parma, where he has been Associate Professor since 2004.

Recent research grants include: co-management of a project funded by Italian Railway Network Society (RFI) aimed at developing an automatic inspection system for train pantographs; a "Marie Curie Initial Training Network" grant, for a four-year research training project in Medical Imaging using Bio-Inspired and Soft Computing; a grant from "Compagnia diS. Paolo" on "Bioinformatic and experimental dissection of the signalling pathways underlying dendritic spine function".

He has been Editor-in-chief of the "Journal of Artificial Evolution and Applications" from 2007 to 2010. Since 1999, he has been chair of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing, now a track of the EvoApplications conference. Since 2005, he has co-chaired MedGEC, workshop on medical applications of evolutionary computation at GECCO. Co-editor of special issues of journals dedicated to Evolutionary Computation for Image Analysis and Signal Processing. Member of the Editorial Board of the journals “Evolutionary Computation” and “Genetic Programming and Evolvable Machines”.

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

Robert M. Patton

Dr. Patton received his Ph.D. in Computer Engineering with emphasis on Software Engineering from the University of Central Florida in 2002. In 2003, he joined the Applied Software Engineering Research group of Oak Ridge National Laboratory as a researcher. Dr. Patton primary research interests include data and event analytics, intelligent agents, computational intelligence, and nature-inspired computing. He currently is investigating novel approaches of evolutionary computation to the analysis of mammograms, abdominal aortic aneurysms, and traumatic brain injuries. In 2005, he served as a member of the organizing committee for the workshop on Ambient Intelligence - Agents for Ubiquitous Environments in conjunction with the 2005 Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005).

Model-Based Evolutionary Algorithms (MBEA)

http://www.cwi.nl/~bosman/mbea2016/

Summary

Genetic and evolutionary algorithms (GEAs) evolve a population of candidate
solutions to a given optimization problem using two basic operators: (1)
selection and (2) variation. Selection introduces a pressure toward high-quality
solutions, whereas variation ensures exploration of the space of all potential
solutions. Two variation operators are common in genetic and evolutionary
computation: (1) crossover, and (2) mutation. Crossover creates new candidate
solutions by combining bits and pieces of promising solutions, whereas mutation introduces slight perturbations to promising solutions to explore their immediate neighborhood.

However, fixed, problem-independent variation operators often fail to
effectively exploit important features of high-quality selected solutions,
potentially leading to inefficient optimization in cases where a performance
advantage can be gained by using variation operators that are informed by
learnable problem features.

One way to make variation operators more powerful and flexible is to replace the traditional variation of GEAs by

1. Modelling key features of solutions that influence their quality, and
2. Generate a new population of candidate solutions using the model in the
expectation of improved solution quality.

When the model is a probability distribution, such evolutionary algorithms are
commonly called estimation-of-distribution algorithms (EDAs). This includes such algorithms as PBIL, UMDA, CGA, ECGA, EBNA, LFDA, BOA, hBOA, PBIL_C, EGNA, EMNA, DEUM, AMaLGaM, CMA-ES, ACO and natural-gradient-based optimization algorithms, including NES and xNES.

EDAs in fact belong to a broader class of model-based evolutionary algorithms
(MBEA) that learn and store more general structure such as linkage, variable
dependency structures and hypergraphs or that operate on ensembles of models. Examples include LTGA and DSMGA(-II) which do not construct a probabilistic model. Such algorithms have the potential to be more robust to changes in problem formulation, making them generally more attractive to solve black-box optimization problems.

Conversely, since their search trajectories are determined by explicit models,
model-based algorithms are more amenable to theoretical study including
approaches such as run-time analysis. Understanding gained here can lead to more principled algorithm design, informed selection of suitable representations and generalisation beyond empirical benchmark testing.

With the ending of the EDA track in the general GECCO conference, the focus on MBEA potentially becomes scattered across different tracks. The purpose of this workshop is therefore to provide a unique forum to discuss

- recent advances in model-based evolutionary algorithms
- new theoretical and empirical results,
- applications of model-based evolutionary algorithms,
- cross-fertilization between domains and techniques, and
- promising directions for future research.

In support of these goals, the organizers will invite well-known researchers
that are active in the design and application of model-based evolutionary
algorithms to give a talk in the MBEA workshop. Moreover, a panel discussion
will be organized to discuss the unified future of model-based evolutionary
algorithms.

Biographies

Peter A.N. Bosman

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

John McCall

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

Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2016)

Summary

Building on workshops held annually since 2010, the sixth annual workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC), to be held at GECCO 2016, is intended to explore, evaluate and promote current visualisation developments in the area of genetic and evolutionary computation (GEC). Visualisation is a crucial tool in this area, providing vital insight and understanding into algorithm operation and problem landscapes as well as enabling the use of GEC methods on data mining tasks. Particular topics of interest are:

* visualisation of the evolution of a synthetic genetic population
* visualisation of algorithm operation
* visualisation of problem landscapes
* visualisation of multi-objective trade-off surfaces
* the use of genetic and evolutionary techniques for visualising data
* novel technologies for visualisation within genetic and evolutionary computation
* facilitating human steer of algorithms
* non-visual techniques for presenting results (e.g. audio and audio-visual)

As well as allowing us to observe how individuals interact, visualising the evolution of a synthetic genetic population over time facilitates the analysis of how individuals change during evolution, allowing the observation of undesirable traits such as premature convergence and stagnation within the population. In addition to this, by visualising the problem landscape we can explore the distribution of solutions generated with a GEC method to ensure that the landscape has been fully explored. In the case of multi- and many-objective optimisation problems this is enhanced by the visualisation of the trade-off between objectives, a non-trivial task for problems comprising four or more objectives, where it is necessary to provide an intuitive visualisation of the Pareto front to a decision maker. All of these areas are drawn together in the field of interactive evolutionary computation, where decision makers need to be provided with as much information as possible since they are required to interact with the GEC method in an efficient manner, in order to generate and understand good solutions quickly.

In addition to visualising the solutions generated by a GEC process, we can also visualise the processes themselves. It can be useful, for example, to investigate which evolutionary operators are most commonly applied by an algorithm, as well as how they are applied, in order to gain an understanding of how the process can be most effectively tuned to solve the problem at hand. Advances in animation and the prevalence of digital display, rather than relying on the paper-based presentation of a visualisation, mean that it is possible to use visualisation methods so that aspects of an algorithm's performance can be evaluated online.

GEC methods have also recently been applied to the visualisation of data. As the amount of data available in areas such as bioinformatics increases rapidly, it is necessary to develop methods that can visualise large quantities of data; evolutionary methods can, and have, been used for this. Work on visualising the results of evolutionary data mining is also now appearing.

All of these methods benefit greatly from developments in high-powered graphics cards and work on 3D visualisation, largely driven by the computer games community. A workshop provides a good environment for the demonstration of such methods.

Biographies

David Walker

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

Richard Everson

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

His research interests lie in statistical pattern recognition, multi-objective optimisation and the links between them. Recent interests include the optimisation of the performance of classifiers, which can be viewed as a many-objective optimisation problem requiring novel methods for visualisation. Research on the construction of league tables has led to publications exploring the multi-objective nature and methods of visualising league tables. Current research is on surrogate methods for large optimisation problems, particularly computational fluid dynamics design optimisation.

Jonathan Fieldsend

Jonathan Fieldsend is Senior Lecturer in Computer Science at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has held postdoctoral positions as a research fellow (working on the interface of Bayesian modelling and optimisation) and as a business fellow (focusing on knowledge transfer to industry) prior to his appointment to an academic position at Exeter.

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

Work in these fields has also led to an interest in visualisation, which in turn has led to peer reviewed work on the application and comparison of existing visualisation techniques to new domains, and the investigation of novel visualisation techniques. He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

Women@GECCO Workshop

http://sig.sigevo.org/index.html/tiki-index.php?page=Women%20at%20GECCO%202016

Summary

Women form an under-represented cohort in evolutionary computation, whether the cohort is examined in industry, academics or both. The broad objective of this workshop is to bring women attending GECCO together to share ways that will generate, encourage and support academic, professional and social opportunities for women in evolutionary computation. The workshop will foster, sustain and impart role models and offer the opportunity to interact with others “in the same boat.” We encourage participation by all faculty, professionals and students interested in Evolutionary Computation who identify as female, who consider themselves underrepresented minorities with similar issues, or are male and supportive of the issues.

Biographies

Carola Doerr

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

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

Julia Handl

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

Emma Hart

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

Gabriela Ochoa

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

Amarda Shehu

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

Tea Tušar

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

Christine Zarges

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

Nur Zincir-Heywood

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

Workshop on Surrogate-Assisted Evolutionary Optimisation (SAEOpt 2016)

Summary

In many real world optimisation problems evaluating the objective function(s) is expensive, perhaps requiring days of computation for a single evaluation. Surrogate-assisted optimisation attempts to alleviate this problem by employing computationally cheap 'surrogate' models to estimate the objective function(s) or the ranking relationships of the candidate solutions.

Surrogate-assisted approaches have been widely used across the field of evolutionary optimisation, including continuous and discrete variable problems, although little work has been done on combinatorial problems. Surrogates have been employed in solving a variety of optimization problems, such as multi-objective optimisation, dynamic optimisation, and robust optimisation. Surrogate-assisted methods have also found successful applications to aerodynamic design optimisation, structural design optimisation, data-driven optimisation, chip design, drug design, robotics and many more. Most interestingly, the need for on-line learning of the surrogates has led to a fruitful crossover between the machine learning and evolutionary optimisation communities, where advanced learning techniques such as ensemble learning, active learning, semi-supervised learning and transfer learning have been employed in surrogate construction.

Despite recent successes in using surrogate-assisted evolutionary optimisation, there remain many challenges. This workshop aims to promote the research on surrogate assisted evolutionary optimization including the synergies between evolutionary optimisation and learning. Thus, this workshop will be of interest to a wide range of GECCO participants. Particular topics of interest include (but are not limited to):

  • Learning approaches for constructing surrogates
  • Model management in surrogate-assisted optimisation
  • Multi-level, multi-fidelity surrogates
  • Complexity and efficiency of surrogate-assisted methods
  • Surrogates-assisted evolutionary optimization of computationally expensive problems #
  • Data-driven optimization
  • Model approximation in dynamic, robust and multi-modal optimisation
  • Model approximation in multi- and many-objective optimisation
  • Comparison of different modelling methods in surrogate construction
  • Surrogate-assisted identification of the feasible region
  • Comparison of evolutionary and non-evolutionary approaches with surrogate models
  • Performance improvement techniques in surrogate-assisted evolutionary computation

Biographies

alma.rahat

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

Richard Everson

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

His research interests lie in statistical pattern recognition, multi-objective optimisation and the links between them. Recent interests include the optimisation of the performance of classifiers, which can be viewed as a many-objective optimisation problem requiring novel methods for visualisation. Research on the construction of league tables has led to publications exploring the multi-objective nature and methods of visualising league tables. Current research is on surrogate methods for large optimisation problems, particularly computational fluid dynamics design optimisation.

Jonathan Fieldsend

Jonathan Fieldsend is Senior Lecturer in Computer Science at the University of Exeter. He has a degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has held postdoctoral positions as a research fellow (working on the interface of Bayesian modelling and optimisation) and as a business fellow (focusing on knowledge transfer to industry) prior to his appointment to an academic position at Exeter.

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

Work in these fields has also led to an interest in visualisation, which in turn has led to peer reviewed work on the application and comparison of existing visualisation techniques to new domains, and the investigation of novel visualisation techniques. He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

Handing Wang

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

Yaochu Jin

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

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

Important dates - Workshops

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