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Workshops


List of Workshops

TitleOrganizers
20th International Workshop on Evolutionary Rule-Based Machine Learning (formerly the International Workshop on Learning Classifier Systems)
  • Ryan Urbanowicz University of Pennsylvania, USA
  • Kuber Karthik Microsoft, Redmond, Washington, US
  • Danilo Vasconcellos Vargas Kyushu University
2nd Workshop on Industrial Applications of Metaheuristics (IAM)
  • Silvino Fernandez Alzueta ArcelorMittal
  • Pablo Valledor Pellicer ArcelorMittal
  • Thomas Stützle IRIDIA laboratory, ULB, Belgium
7th 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
Black Box Optimization Benchmarking 2017 (BBOB 2017)
  • Anne Auger Inria Saclay-Ile-de-France
  • Dimo Brockhoff Inria Saclay - Ile-de-France and CMAP, Ecole Polytechnique, France
  • Nikolaus Hansen INRIA Saclay, France
  • Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia
  • Dejan Tušar Inria Saclay
Evolution in Cognition (Second edition)
  • 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 Biology
  • José Santos University of A Coruña, Spain
  • Julia Handl University of Manchester, UK
  • Amarda Shehu George Mason University, Fairfax, VA
  • Mostafa Ellabaan Technical University of Denmark, Denmark
Evolutionary Computation Software Systems (EvoSoft)
  • Stefan Wagner University of Applied Sciences Upper Austria
  • Michael Affenzeller University of Applied Sciences Upper, Austria
Evolutionary Methods for Smart Grid Applications
  • Frank Neumann University of Adelaide, Australia
  • Markus Wagner University of Adelaide
  • Paul Kaufmann Paderborn University
  • Oliver Kramer University of Oldenburg, Germany
Exploration of Inaccessible Environments through Hardware/Software Co-evolution
  • P.G.M. Baltus Eindhoven University of Technology
  • Giovanni Iacca RWTH Aachen University
  • M.N. Andraud TU Eindhoven - KU Leuven
Funding Sources (focus on Europe)
  • Markus Wagner University of Adelaide
GECCO Student Workshop
  • Vanessa Volz TU Dortmund University
  • Boris Naujoks Cologne University of Applied Sciences, Germany
Genetic and Evolutionary Computation in Defense, Security and Risk Management
  • Frank Moore University of Alaska Anchorage, USA
  • Gunes Kayacik Qualcomm Research Silicon Valley, USA
  • Nur Zincir-Heywood Dalhousie University, Canada
  • Anna I Esparcia-Alcázar Universitat Politècnica de València, Spain
Genetic Improvement Workshop
  • Westley Weimer University of Virginia
  • Justyna Petke University College, London, UK
  • David R. White University College, London, UK
  • William B. Langdon University College, London, UK
Landscape-Aware Heuristic Search
  • Nadarajen Veerapen University of Stirling, UK
  • Fabio Daolio University of Stirling, UK
  • Arnaud Liefooghe Université de Lille, France
  • Sébastien Verel Univ. Littoral Côte d'Opale
  • Gabriela Ochoa University of Stirling, UK
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)
  • John McCall
  • Dirk Thierens Utrecht University, The Netherlands
New Standards for Benchmarking in Evolutionary Computation Research
  • William La Cava University of Massachusetts Amherst
  • Ryan Urbanowicz University of Pennsylvania, USA
  • Randal Olson University of Pennsylvania
  • Patryk Orzechowski University of Pennsylvania
Parallel and Distributed Evolutionary Inspired Methods
  • Ernesto Tarantino National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
  • Ivanoe De Falco National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
  • Antonio Della Cioppa Natural Computation Lab, DIEM, University of Salerno
  • Umberto Scafuri -
Second Workshop on Evolving Collective Behaviors in Robotics (ECBR)
  • Nicolas Bredeche Université Pierre et Marie Curie
  • Evert Haasdijk Vrije University, Amsterdam
  • Abraham Prieto Garcia University of A Coruña, Spain
  • Heiko Hamann University of Paderborn
Simulation in Evolutionary Robotics
  • Jared Moore School of Computing and Information Systems, Grand Valley State University
  • Anthony Clark Missouri State University
Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2017)
  • David Walker University of Exeter, UK
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
  • Bogdan Filipic Jozef Stefan Institute, Slovenia
  • Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia
Women@GECCO Workshop
  • Amarda Shehu George Mason University, Fairfax, VA
  • Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia
Workshop on Surrogate-Assisted Evolutionary Optimisation (SAEOpt 2017)
  • Alma Rahat University of Exeter
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
  • Handing Wang University of Surrey
  • Yaochu Jin

20th International Workshop on Evolutionary Rule-Based Machine Learning (formerly the International Workshop on Learning Classifier Systems)

http://itslab.inf.kyushu-u.ac.jp/~vargas/erbml_2017/

Summary

In the context of evolutionary machine learning, rule-based machine learning (RBML) algorithms are an often overlooked class of algorithms with flexible features employing an alternative paradigm of piece-wise modeling that sets them apart from other strategies, particularly with respect to modeling complexity and human interpretability. Since John Holland’s formalization of the Genetic Algorithm (GA) and his conceptualization of the first RBML, i.e. the Learning Classifier System (LCS) in the 1970’s, the LCS paradigm has broadened greatly into a framework encompassing many algorithmic architectures, knowledge representations, rule discovery mechanisms, credit assignment schemes, and additional integrated heuristics. LCSs combine the global search of evolutionary algorithms with the local optimization of reinforcement or supervised learning. LCS algorithms uniquely distribute learned patterns over a collaborative population of individually interpretable (IF: THEN) rules. This allows the algorithm to flexibly and effectively describe complex and diverse problem spaces found in behavior modeling, online-control, function approximation, classification, prediction, and data mining. These systems uniquely benefit from their adaptability, flexibility, minimal assumptions, and interpretability. Topics that have been central to RBML for many years, such as human interpretability of the generated models, are now becoming of high interest to other machine learning communities. This workshop serves as a critical spotlight to disseminate the long experience of RBML in these areas, to attract new interest, and expose the machine learning community to an alternate advantageous modeling paradigm.

Topics of interests include but are not limited to:

  • Paradigms of LCS (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, ...)
  • LCS for Cognitive Control (architectures, emergent behaviors, ...)
  • Applications (data mining, medical domains, bioinformatics, intelligence in games ...)
  • Optimizations and parallel implementations (GPU, matching algorithms, …)
  • Other rule-based machine learning methods/topics (association rule learning, artificial immune systems, hybrid systems,…)

Biographies

Ryan Urbanowicz

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

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.

Danilo Vasconcellos Vargas

Danilo Vasconcellos Vargas is an Assistant Professor at the Faculty of Information Science and Electrical Engineering, Kyushu University. He received the M. Eng. and Ph.D degrees from Kyushu University. His thesis was about employing a new concept of fitness to both machine learning and optimization. His current research interests focus on general learning systems which include research in evolutionary algorithms, neural networks, learning classifier systems and their applications.
In his last work, he developed an unified neural model integrating most neural network features from the literature into one representation. With this powerful representation it was possible to evolve the topology and weights of the network to learn a wide variety of problem classes.

2nd Workshop on 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.

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.

7th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)

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

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, the work described in 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. Recently, the design of Multi-Objective Evolutionary Algorithm components was automated.

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. Another avenue for research is investigating the potential performance improvements obtained through the use of asynchronous parallel evolution to exploit the typical large variation in fitness evaluation times when executing hyper-heuristics.

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).

Black Box Optimization Benchmarking 2017 (BBOB 2017)

http://numbbo.github.io/workshops/BBOB-2017/

Summary

Quantifying and comparing the performance of optimization algorithms is a difficult and tedious task to achieve---but ubiquitous when designing and applying numerical optimization algorithms.

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. A substantial portion of its success can be attributed to the Comparing Continuous Optimization benchmarking platform (COCO) that automatically allows algorithms to be benchmarked and performance data to be visualized effortlessly.

Within this BBOB workshop series, we are looking forward to any submission related to black-box optimization benchmarking of continuous optimizers in
the widest sense, for example papers that:

  • describe and benchmark new or not-so-new algorithms on one of the provided COCO testbeds (see below),
  • compare new or existing algorithms from our COCO/BBOB database,
  • analyze the data obtained in previous editions of BBOB, or
  • discuss, compare, and improve upon any benchmarking methodology for continuous optimizers such as design of experiments, performance measures, presentation methods, benchmarking frameworks, test functions, ...


We encourage particularly submissions related to expensive optimization (where only a limited budget is affordable, e.g., (meta-)model assisted algorithms) and also algorithms from outside the evolutionary computation community.

In addition to three previously established test suites, we provide in 2017 a new, extended bi-objective test suite, resulting in four supported test suites overall:

  • bbob testbed with 24 noiseless single-objective functions
  • bbob-noisy with 30 noisy single-objective functions
  • bbob-biobj, the 2016 testbed with 55 noiseless bi-objective functions
  • bbob-biobj-ext, an extended testbed of bbob-biobj with 92 noiseless, bi-objective functions


All test functions continue to be unconstrained or maximally bound-constrained.
Like for the previous editions of the workshop, we provide source code in various languages (C/C++, Matlab/Octave, 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).

To be notified about further releases of the COCO code and information
related to the workshop, please register at http://numbbo.github.io/register.

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) before joining Inria in November 2011 as a permanent researcher (first in its Lille - Nord Europe research center and since October 2016 in the Saclay - Ile-de-France center). His research interests are focused on evolutionary multiobjective optimization (EMO), in particular on theoretical aspects of indicator-based search and on the benchmarking of blackbox algorithms in general.

Nikolaus Hansen

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

Tea Tušar

Tea Tušar is a postdoctoral researcher at the Department of Intelligent Systems of the Jožef Stefan Institute in Ljubljana, Slovenia. She received the BSc degree in Applied Mathematics and the MSc degree in Computer and Information Science from the University of Ljubljana. She was awarded the PhD degree in Information and Communication Technologies by the Jožef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has recently completed a one-year postdoctoral fellowship at Inria Lille in France where she worked on benchmarking multiobjective optimizers. Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems.

She was involved in the organization of a number of workshops at previous GECCOs (Student Workshop, Black-Box-Optimization-Benchmarking Workshop and Women@GECCO) and held a tutorial on Visualization in Multiobjective Optimization at GECCO 2016.

Dejan Tušar

Dejan Tusar is an engineer at Inria Saclay - Île-de-France, 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.

Evolution in Cognition (Second edition)

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, in silico computation 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 Biology

http://eccsb2017.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. The field of computational biology includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. The focus of this workshop is the use of nature-inspired approaches to central problems in computational biology, including optimization methods under the umbrella of evolutionary computation

One of the main objectives of the workshop is focused on computational structural biology. 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.

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. Although computational structural biology is one on the main areas, other work in sequence and systems computational biology that prompts the design of novel evolutionary computation approaches is welcome.
Following the previous editions in GECCO 2016 and GECCO 2015, those focused on computational structural biology, 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, with the aim of bridging computational biology and evolutionary computation.

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.

Mostafa Ellabaan

Mostafa Ellabaan obtained his Ph.D. in computer science and engineering at
Nanyang Technological University. He is currently a researcher at the Novo
Nordisk Foundation Center for Biosustainability at Technical University of
Denmark.. His research covers a broad range of topics in the area of
evolutionary and memetic optimization in biomolecular systems. His current
research also includes large-scale data analysis of microbial gene exchange
network and microbial strain and community engineering.

Evolutionary Computation Software Systems (EvoSoft)

http://dev.heuristiclab.com/trac.fcgi/wiki/EvoSoft

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 enables EC researchers to exchange their ideas on how to develop and apply generic and reusable EC software systems and to present open and freely available solutions on which others can build their work on. Furthermore, the workshop should help to identify common efforts in the development of EC software systems and should highlight cooperation potentials and synergies between different research groups. It 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).

Evolutionary Methods for Smart Grid Applications

http://ci4energy.uni-paderborn.de/smartEA/

Summary

Sustainability is of great importance due to increasing demands and limited resources worldwide. In particular, in the field of energy production and consumption, methods are required that allow to phase generation and load efficiently. The vast extension of renewable and distributed energy sources and the growing information infrastructure enable a fine screening of producers and consumers, but require the development of tools for the analysis and understanding of large datasets about the energy grid. Key technologies in future ecological, economical and reliable energy systems are energy prediction of renewable resources, prediction of consumption as well as efficient planning and control strategies for network stability.
To enable financially and ecologically viable projects, optimization methods
have taken over a key role for planning, optimizing and forecasting sustainable systems. Typically, these approaches make use of domain knowledge in order to achieve the required goal. Even in the case that explicit domain knowledge is not available, specialized methods can also handle large raw numerical sensory data directly, process them, generate reliable and just-in-time responses, and have high fault tolerance.

The main goal of this workshop is to promote the research on evolutionary
algorithms in smart grids. We are seeking innovative research articles including, but not limited to the following areas:

  • Energy generation and load forecasting
  • Monitoring and simulation
  • Communication and control
  • Demand side and smart home energy management
  • Distributed energy resources
  • Methods and algorithms for real-time analysis and control
  • Open access datasets and tools
  • Electric drive vehicles
  • Renewable energy
  • Smart micro-grids
  • Smart sensing
  • Virtual power plants

Submitted work should put an emphasis on modeling of solution spaces, on
finding optimal representations and operators for evolutionary algorithms, and on employing and developing advanced evolutionary heuristics, e.g., for step size control, constraint handling, dynamic solution spaces, and multiple conflictive objectives.

Biographies

Frank Neumann

Frank Neumann received his diploma and Ph.D. from the Christian-Albrechts-University of Kiel in 2002 and 2006, respectively. He is a professor and leader of the Optimisation and Logistics Group at the School of Computer Science, The University of Adelaide, Australia. Frank has been the general chair of the ACM GECCO 2016. With Kenneth De Jong he organised ACM FOGA 2013 in Adelaide and together with Carsten Witt he has written the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity" published by Springer. He is an Associate Editor of the journals "Evolutionary Computation" (MIT Press) and "IEEE Transactions on Evolutionary Computation" (IEEE). In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of renewable energy, logistics, and mining.

Markus Wagner

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

Paul Kaufmann

Paul Kaufmann is a Postdoctoral Research Fellow at the University of Paderborn. His main research interests are evolutionary algorithms, signal classification, and their application to adaptive and reconfigurable hardware systems. After receiving a Ph.D. in Evolvable Hardware (2013) from the University of Paderborn, he stayed at the Fraunhofer Institute for Wind Energy and Energy System Technology and the Energy Management and Power System Operation Group at the University of Kassel from 2012 to 2013. He is organizing the annual EvoENERGY Workshop at EvoStar, heading the IEEE CIS Educational Material subcommittee, has co-founded and is heading the IEEE Task Force on Computational Intelligence in the Energy Domain, and is member of the IEEE Task Force on Evolvable Hardware.

Oliver Kramer

Oliver Kramer is Assistant Professor (Juniorprofessor) for Computational Intelligence at the University of Oldenburg in Germany. His main research interests are machine learning, evolutionary optimization, and their application to real-world domains. He received a PhD from the University of Paderborn, Germany, in 2008. After a postdoc stay at the TU Dortmund, Germany, from 2007 to 2009, and the International Computer Science Institute in Berkeley (USA) in 2010, he became Juniorprofessor at the Bauhaus University Weimar, later Juniorprofessor at the Department of Computing Science at the University of Oldenburg, where he finished his habilitation in 2013.

Exploration of Inaccessible Environments through Hardware/Software Co-evolution

Summary

This workshop focuses on the application of evolutionary methodologies to the development of intelligent, miniaturized, extremely resource limited, self-adapting sensor swarms, and the hardware realizations thereof. While a relevant body of literature exists on the application of evolutionary algorithms and swarm intelligence in Sensor Networks, little research has been devoted so far to the (co-)evolution of hardware and software of sensor systems with severe restrictions on e.g. size and power. However, recent advances in hardware design and miniaturization make now possible unprecedented applications of evolutionary algorithms with sensor hardware in the loop.

This workshop, organized under the aegis of the H2020 FET-OPEN project ìPHOENIX:
Exploring the Unknown through Reincarnation and Co-evolutionî, will disseminate the preliminary results of the project and include a restricted number of invited & interactive papers. Furthermore, the workshop is open to high-quality contributions dealing with innovative combinations of evolving physical and simulated sensor systems, and multidisciplinary
approaches combining hardware design, evolutionary computation, and knowledge-based systems. In addition to the papers, the workshop will include posters and a demo of the current Phoenix prototype, as well as room for demonstrations of related projects from other research groups.

Topics include but are not limited to

  • Evolution of physical sensors and sensor agents
  • Co-evolution of sensors software and hardware
  • Evolution of environment models through sensor adaptation
  • Emergence of swarm intelligence in sensor systems
  • Self-adapting localization techniques in sensor systems
  • Incorporation of domain knowledge in evolving sensors systems

Biographies

P.G.M. Baltus

Peter Baltus was born on July 5th 1960 in Sittard and received his masters degree in Electrical Engineering from Eindhoven University of Technology in 1985, and his PhD degree from the same university in 2004. He worked for 22 years at Philips and later NXP in Eindhoven, Nijmegen, Tokyo and Sunnyvale in various functions, including research scientist, program manager, architect, domain manager, group leader and fellow in the areas of data converters, microcontroller architecture, digital design, software, and RF circuits and systems. In 2007 he started his current job at the Eindhoven University of Technology as professor in high-frequency electronics and chair of the mixed-signal micro-electronics group. He co-authored more than 100 papers and holds 16 US patents

Giovanni Iacca

Dr. Giovanni Iacca holds a MSc in Computer Engineering (2006, cum laude) from Politecnico di Bari (IT), with a major in intelligent systems. From 2006 to 2009, he was a software engineer within the Italian National Research Council, where he worked on real-time systems for robotics and CNC applications. In 2011, Iacca earned a Ph.D. in Mathematical Information Technology from the University of Jyväskylä (FI), with a thesis on optimization algorithms for embedded systems. From 2012 to 2016, Iacca has held a position as scientific researcher at INCAS³, an independent research institute in Assen (NL), where he has focused his research on wireless networks and distributed intelligent systems. From 2013 to 2016, Iacca has also held a joint postdoctoral position at the École Polytechnique Fédérale de Lausanne and the Université de Lausanne (CH), where he worked on the application of evolutionary computation to the study of collective behavior. Currently he is affiliated with the RWTH Aachen University (DE) on the H2020-FETOPEN project “PHOENIX: Exploring the Unknown through Reincarnation and Co-evolution”. To date, Iacca is coauthor of more 55 peer-reviewed publications in the areas of evolutionary computation, swarm intelligence, memetic computing, robotics, embedded systems, wireless networks and distributed computing.​ In the same fields, Iacca serves regularly as reviewer for several journals and he is involved in a number of scientific committees.

M.N. Andraud

Martin Andraud received the Diploma in Engineering with specialty in microelectronics from Telecom Physics Strasbourg, France, in 2012, the M.S degree in micro- and nano-electronics from Strasbourg University, France, in 2012, and the Ph.D. degree in micro- and nano-electronics from University of Grenoble Alpes, TIMA Laboratory, France, in 2016. His thesis work focused on developing an adaptive calibration methodology for Radio-Frequency circuits able to compensate for process variations. Since January 2016, he is a postdoctoral researcher at TU Eindhoven and KU Leuven in the context of the Phoenix H2020 FET-OPEN project. His current research interests are the development of adaptive hardware techniques for analog and mixed-signal circuit designs.

Funding Sources (focus on Europe)

Summary

Speakers:
1) Carlos Galvez <Carlos.GALVEZ@ec.europa.eu>
2) Carola Doerr <carola.doerr@mpi-inf.mpg.de>

Presented material:
1) The European Research Council: Funding opportunities
http://cs.adelaide.edu.au/~markus/temp/2017-07-19_ERCEA-GECCO_CG.pptx
2) Marie Sklodowska-Curie Actions
http://cs.adelaide.edu.au/~markus/temp/2017-07-19_REA-GECCO_CG.ppt
3) COST Action CA15140 ImAppNio, Improving the Applicability of Nature-Inspired Optimisation by Joining Theory and Practice
http://cs.adelaide.edu.au/~markus/temp/2017-07-19_COST.pdf

About COST action Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO): http://imappnio.dcs.aber.ac.uk/about-imappnio
Training School, 18th-24th October 2017, Paris: http://imappnio.dcs.aber.ac.uk/cost-training-school

Biographies

Markus Wagner

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

GECCO Student Workshop

http://gecco-2017.sigevo.org/index.html/Student+Workshop

Summary

The goal of the Student Workshop is to support students with their scientific publications on all GECCO-related topics 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 for 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.

For further and up-to-date information, check our website and follow us on twitter @GECCOsws.

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 employing surrogate-assisted evolutionary algorithms to obtain balance and robustness in systems with interacting human and artificial agents, especially in the context of games.

Boris Naujoks

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

Genetic and Evolutionary Computation in Defense, Security and Risk Management

https://projects.cs.dal.ca/projectx/secdef2017/index.html

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.
In the last three years, we have been organizing the SecDef workshop under GECCO to 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.

The workshop invites both completed and ongoing work, with the aim to encourage communication between active researchers and practitioners to better understand the current scope of efforts within this domain. The ultimate goal is to understand, discuss, and help set future directions for computational intelligence in security and defense problems.

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 from Dorover $!ontributir lossy i

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    M.N. AndSébdhodgraVSrel> Markus WSébdhodgraVSrelpr ocentiEditor or for Appl (N Science from WWUniversity of Alaé du LetsearchCôr od'Opa B cCalaiorkin 2016.laing sp13, r ofon, ctiorceniversity of Alaska ANppNISophra-Aublpoliorkin 2016./lmatri06ent 2p13eived a PhD fram Dormat science and engitmundersity of Alaska ANppNISophra-Aublpoliorkin 2016. Duri05rrerch Doru acad Doropics and fi systelandsche of heurimat sc of toiirchtion, in par/ froftwarn to prese assistae pontDOLPHIN Ting ³, NRIA Letee Nssdy Researkin 201./lmatri09ent 2p11rrerch topics rs are the ted tog 18 ad Practionary algorithcon and theioective (evolutition, in parti hardwareopics , nse scilex with in. Allblan r histicrch topics rsDoropics and fi systelandsche of heur/ frocs. He hads.inginnutimesstiic publicatid holddomional Computer and hee,gbooketidptvrn,gbookeon scilex with in,is 16ional ComputerableEs. ard/ fror ocover in a number 18 s-ng thezn parlEC">Sumce anComps,ew>

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    M.N. AndGabriopi OnCoa> Markus WGabriopi OnCoanior Lecturer at the S (N Scienil, 18om WWUniversity of Alaska ASodclol,,tScotlandlds B.Sc. de C Dor (N Scienil, 18om WWUnal Al agents, Ince in securtmundersity of Alaska ASussex2014ceent topics rs are the lid tog 18 fco if Genetications of Genptionary algorithms and theory-gc methods opics rto rene,groadeemp a i appl utonomtion(ptin-*)ropics , to ho-c methodsn, fi systelandsche of heur,lications of Genetnd sc of toiirchtion, sn parti>M.lthca w, aare engineering. So far, hved osed by Sprinnvp-t90 anCorvoe pnd holds 16egulrddvarctio committee member sceivedearciEditor odinwhaiternary Computation in Defen(MIT d mas) mwtrialn a numberfco iSecDef worin-* Sat TUlinae q During,ds 16egulrd a ooredtutoiirch the mniv Cseeke Durin2, 2p13eiivedpomeosD. oredfirstaCross-The ulrH methods Sat TUlCs often U(CHeSC" is1) ds 16mwtricthe Compe HCOPurin4,pe HCOPurin5,aFOGAerin5,as 16i16egulrtunlf committeethe mork PPSNis the

    GECCO StMeasu farso-daPtymo farsD in 2011. Dunary ComputatAs and the

    Summary

    With theD in gking trytidraino nior Lcorbetttyisor snion iencary Comp. Ontext of grnter-nary algorithtion, in parl. < Envee ted ;x=gumentor nrindwell lae quf d in 2011:CO-reccushormats and comp, monuthe crow. oredvnithversi defentog 18 opics r>nCorvo new ck.

    tes usuthe label docialt 18 nxymortog“pras wctioof in gking”, Renivis,eowintindws, ringinem design tnd s in gkingtndp> Scaps di. workeitcua oibt studess and s in gkuan ic oredfirsta;x=ceIn the lSc publicatlicerawctioof tcho ogeucatioelysi pubrto rennive/> -depindwstgc methodsd/ foweuca,eowinEC"y. Last, hodmpulae or Land ati,sion anheuginImfitteffic aodsorch aim to e.

    / An Enverarien aninnov andytok.

    Biographies

    Giovanni iovanni Squeteerop> Markus Wurtuiovanni Squeteerop a PhD frfromM.S.so-daPMathemat science and engiio 1996so-da i01, rly inte is / fror ocnnt in differ for Applio aco de Bars th Td hao, Td hao, Italirrerch topics rs are the mix ored hindiy intruour sbio- piods. ,ds 16m goa-espec with in. erchtvolur /> al UniversiC member */ frong thezerreTas*narHOT*ew> SEEE*und EvolPommittmhe Unal e HardwareLearnins*/ fror oext ofordof toiSEEE*e HAons of Gene*yess s the the l<ecco-20wwwcaad.polito.it/~squeteero/cv_squeteero.pdf&g
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    GiovanniAlhe nsaTonda> Markus WAlhe nsaTondad a PhD frfrom Dormatcca0A.tmundaco de Bars th Td hao, Td hao, Italitiroader ork fococh ati-w> lations of Genetic nary algorithcon and the/ Areceioral- researchce working applicatsersind facichool oiohodtuaon s and è> aCscilex>

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    GECCO StMedversiAons of Genetic and Evolutionary Computation and thei(MedGEC)

    Summary

    With thew ck"> etrchadincning set f.lthca wIn the lSu (evosclete tat are (leasareoibt limpresent)tions of Genetic aECent:p>TopicsCyber-crMedversimprearyi> Military,edversiapplrch. < Envaryi> Military,edversit-lii of heuri> MilitaryCfw elopmeiagnoeuris 16enseap i> MilitaryDrnshtrory amedversiprnsh; tructirdri> MilitaryCfw elopmce woc with ini> Military, dellhe Unal simulf Genptiomedversi. < Enveei> MilitaryDrus,nnscriptems,nsf heuri> MilitaryGndomic-stems.cfw elopmstudieci> IT SecurPaodgrt-isisryp ca wi> IT She workrainingAlthotghlications of GenptioaECenthadincningi oibt new,eowinre ovearyi>ininghreats,ffic aendter undtion s wil framory"varctio es canastrnal the lifw elopmableEs. ardwarea jomsweniv claaarsenmnenir. Alsodbetwit the l invites nivCseektippliridoctfueanneededesturops rrmedversiropics the lions of Genetic EC,libt to_thupplirhe Un.cf alSdefw eteaching 18 so oei>ininghreicatirtt leasalso studertytok. ers to bettest S,hundaECem hre ibti>iningbormrids e ulridefCic ne wocalurastex working/p>

    Biographies

    Naujoks SmoadeStephhn 135/a>

    GiovanniStephhn L. Smoade> Markus WurtStephhn L. Smoade a PhD framBScl (N Science from WWUn 16enss,ns MScso-daPMDtrical EngoBar ing from Eindhoveersity of Ala ringKPows 14ceen nt researcrcentdefde pontext nt of Computinal EngoBarsUniversity of Alaska AYh the14cp>Topics ieStephhn'chearch assistans are the ted togng softwareibt ot ation style. e tionary algorithms and theomr hypervoe proaderons of Genptok.

    etrchadincninrrent , orait researcrceyterirmenapti18 x agnoeurihreaturnive/ersipyst, incluicus of heuristitammmmittd.eStephhnoftwapommitteethe mork owinEuao acrok"> Topics iStephhnoit ro-fco ip-to-datg theze mhreicatMedGECk"> Topics iStephhnoit ciEditor odinwhaiork owin and heeand EvolPommittmhe Unal e HardwareLearninssembeomf the IEEE Tasdinwhairchbo> Sork owinional ComputerJand heetin ScienceetrchHf.lthca wsembeNen ien Scienil, nal Aons of Generp>Topics iStephhno osesomso75d heEs.ithc by of Gene,ptdoctChr hirmening fromsembeomf the lEEE TasBnd ishN Science fond 11/p>

    v>
    Naujoks Cagnoni Stefano 12y/a>

    GiovanniStefano Cagnoni> Markus WurtStefano Cagnoniftraduailigencal EngoBar ing from Einniversity of Alaska AFlos. ar mItalitirworked o a program PMDtstudPower Syrhoral- re nalyl 19971, Ia1994. softwarhvisipary">, prograUniversireltakernCe beespBiomedversiIpreary r of on and theiLaboroualyUniversiMia-earusetto Inhodtuasoa Al Uninive4ceiaing 1997ed o a progra aim to ety of Alaska APaSystirworked o a prograAiEditor oo and Chailaing spe4rp>Topics iRhisisa on wirele anhe but are: s-mt and Poweteragand inclesearchbafItalins RhrewayeNeabilitSond 11.(RFI)pncoreset:ng softwareann utomtwvoliny inteos with iSork orarchpanhs

    s;rag"lodowstions I etersiTrchool, Neabili"le anh,iork atfcor-e alS interestnrchool, and incliui,edversiIpreary ortanBio-I piods.r ofSCo-n Scienil,;rage anhndhove" Sciagnia x S.APaniv"eon "Bioics in Saalutionx wor Powopmeissinteos ing 18 spplrclical rthway po ip-lyfarsdendnd icty ningt, inclu"rp>Topics iHen a prograEinwha-in-arngf ing 18 "Jand heetinAl agents, nary Comp nal Aons of Gene"./lmatri07ent 2p10ceiaing 1999,ed o a progracthe Compe HIASP,r nri mondsodbetwitha> SEEEowin and hesg“nary Computation and the” s 16“and EvolPommittmhe Unal e HardwareLearnins”rp>Topics iHen a prograrp> D. ored"Earhis eri09e/p> ",trch acog eteaching 18 mrhinoutiscus Eincions will crablnary Computation in Defe/p>
    v>
    Naujoks Patt crRohe n 183/a>

    M.N. AndRohe n M.APatt c> Markus WDr.APatt cd a PhD frfrom Dathemat Science ing from Einroadeemp a i applSCo-evoluing at the Utmundersity of Alaska ACsisrrchFlosiday Duri021, Iacc03,ed ojheoD. oredAciencesSCo-evoluing at the URon wirele copCburgak RidespNintelligLaboroualyUnarch assistae . WDr.APatt cdpm > Topiv> v>

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    GECCO StM del-Borithnary ComputatAs and the (MBEA)

    Summary

    With theGnd Evolutionary algorithms and theo(GEAs)a senssirhorpulf Genptioccushormats and compnsae gived tion, in parl.

    ortanowonta iceo Grouals: (1) jolee haslutio(2)"varcd the/ Solee haslencroduardwen anssu enndp> Sh hr-qu theits and comtirworkas"varcd the steiansonx lordteaching 18 sTopics iHoweuca,efnal ,k.

    -ml tdeion terp>Topics iOnsoftyenthaakenvarcd the o Groualsei 100owevefullow uslexiblckurian re;x=ceeowintrathe coblnvarcd the oioaEAmpbyp>Topics i1.y, dellhe Ukeyndeion tetic fand comhaenivinflukingtnrids qu thei,rnal the l21,Gnd atnil, noats,orpulf Genptioccushormats and comp ortano18 mrdelprn uninx wcddteachingi Appldl fand co qu thei

    For furtreenno18 mrdelprdwen aobdwtimatiion s wilpartisueannary algorithms and theored ammmsrceytheede thhystems-of-ion s wilparhms and theo(EDAs) kshopbut arecisueants and theorom BIL, UMDA,gCGA,gECGA,gEBNA,gLFDA,gBOA,ghBOA,g BIL_C,lEGNA,gEMNA,gDEUM, AMaLGaM, CMA-ES, ACOFor furtEDAsmberfactr mlongpnsae broaip-tedia_sstit del-stems.nary algorithms and theo(MBEA)haenivlumsisor agtoreei 100and atioe Protectnsueant nfw "res,nvarcdblcks rk owat o Grourqualste thle_sstit delh. Exana Be but are LTGAeivedDSMGA(-II)iwhiTU dolibt appeProtwen aobdwtimhods t deleiiueants and theohaldlclud.oterarienr undti 100aobustytokchmilismber.

    td, culf Gen,haakrtano18ndand atircei 100att ers veha>

    For furtCs in sgly6.laing nrids nterestnrc incoorks hardsoposmeoD. by nx lsi tit delh,it del-stems.ms and theored i 100amenaompensaad Pr Evothtstudy leresearchionroathtetsueant nrun-lysi of heur/ Ud, discushe gaeoD. worke a slummentha 100orleriplms.ms and theenappl, ton, cedgjolee haslic uid reee ation style. e turrgnd atialgorithbeyotionmpirvothtb. ah> For furtraim owineushe EEE TasEDAlinae q Duoredand atioCseektableEs. ar,g 18 fcurope)pMBEAi.oterariee becomss scattirmenacross x SMEs. Meinae new ck.urdess EEE Tnt , orites bsurworking nd aolirieaity qu 3orku tnd ionurosp>TopicsCyber-cr hisisnain hardwaret del-stems.nary algorithms and thei> IT Securlengtd Pr Evothtationmpirvothtionults,i> IT Securions of Genetic t del-stems.nary algorithms and the,i> IT Securcross-fwocalain parlbetwograThe ulris 16es at the lp sei> Military ao alwarediree hass3ork futectn topics .i> IT She workrainingIn studertyEEE Tass goaps,eowintg theze sclete ta prescomp-knigh assistae seRenivted ers veh Duoredenapplicus ons of Genptiot del-stems.nary algorithms and theoiodaissirhtal q DuoredMBEAi, orites. Weclete coverythe3ork and atioeubemfter ea aim Duoredabpldlscsea/ Ahpanel ionurosclue alhtb. but aredtnd ionuroslclud difncesfutectntiot del-stems.nary algorithms and the/p>

    Biographies

    Markus WJohn McCthe3> Markus Woohn McCthe3rdweno and Chaitin Scienaryl (NowiniDEASURon wirelInhodtuasonivRohe n Gordtnnty in 2011. DuScotlandldOrige ptrcentpectnysthen Saains (ms ebrarc v>

    Vanessa Dirk kshEs. s> Markus WDirk kshEs. s3rdwefftimtwith aim owinnt of Computinion Systems r of on anil, 18om WWsUnivUtreehtnty in 2011,eowinNerworlandstirworked obsureearnirsc urev applnary Computation and theio-datnrConal intelligInce in secu. Hen a prograaln a numbergnd Evolu design tatopics rlaing 1990rrent research interests are the ted e ul_thon devenapti18 xnapplicus ons of Genptiot del lumsiil,ots can playtoko Appldlnsen algorith at TU1WDirk nt ( a progr)octf the IEEE TasEinwhairchBo> SEEEowin and hesgnary Computation and the,gnary ComputatInce. She ho, Snse sl s wil frollowinng thezn parlingciv atioCseektableEs. ars: , orites s-sever (cc03,espe4),einae q( s-)sever (cc04,espe6, 2p14) membeEinwha-in-Crngf (cc07)rp>Topiv> v>

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    GECCO StNew Sscus> s3ork B. ah>

    Summary

    With B. ah> photo_only phtcentpelr)MD.. the to reo Grg sumlt Snse sl s

    p> Scapsation and the” s 16“anieldo1rs_8]"raull-l s cref th. Exage i the/ f= .or s TasStephhn ons ofnaeEsnO-reted qu aobdwsuormats a:3/a> MilitaryAeaity tInel135"Es. ssonmp usia_heading submary">Summary Summarmputation in Defe/p> MilitaryT3 claox="stelandsche si> (i) ubemfter e>

    the/ f= .or s TasSa oored ton, opicc narysia_h ue aaps diithoutntog 1wo Co ssoeve pa00 pary algorr, o isnly phot(ii) mmaryongpnsdiv clton, .or s TasSsia_headi,r nrieesearce pa0 ube ,eowin wh. Exflandgapclass= the/ f= .or s TasSnri pa0, opiccseveral MedGEa ooredryAtcurcrosstintio ao_only phow"dl219?ha> glae qufmav> . s> Markus WJohn McCthe3> Markus Woohn McCthe3rdweno and Chaitin Scienaryl (NowiniDEASURon wirelInhodtuasoniv am La Cavaimhods t dellol, fhaitBiarmcus l sofss="mediapplmodelol, fi systelandschePennsylvaenem a progr)>

    tersiTrchool s-and atdl278?disp54y" class=8&y7920"al Aonsidty in 2only photod/div> the l 57?displsiTrchooi s-and atiovothtionults,iO-reted qu T Securputepclass=8&am>Vanessa Dirk kshEs. s> Markus WDirk kshEs. s3rdwefftimtwith aim owinnt of Computinion Systems r of on anil, 18om W12roader ork fococh ati-w> lations of Genetic nary algorithcon and the/ Areceio12roader orion and theio-datnrConal intelligInce in secu. Hen Urbanowicz Rof on and theiLaboroualyUniversiMia-earusett f nt iblecorShe rPoweer at TUe Scho NRA,eowinFs. ah NintelligInhodtuasoo-Freon wirelced gRydwaUrbanowicznawcti- piods.cScienil,Urbanowicz’iv class="mediGEVO ACMhbob ri05,ed o attirnaeEsnO-reted q, f= “pscff0,enhe bpmable,pyst, ino ammern mie-mbnail/d=-reted quuasoog546r"al Aons aninnoirnximeur,yivCseekcteers ofoogblgoaesperor dastoctioe d on the apieldo6" /> 09eu12 pants andpeer- > pa> <4ol,,tial" positihnoit ro-lf committphoto_l/d= uCDMel<=-reted quuasoog546r"al Aonsoutntog 1wimg src="dl2ee ayoorly "res,nExSTraCS. ember 3ins of ectets="mediawith in,msitblckwin ile del-strmaLCSdm TUlinae q 09eunnvp-t90 ssouca, Df thouthtions I .ieC owin and emcus l sofuterableEs. ard/ frof ScapsGeulll S Gabr1lurM awritu hredands/// ants e pto_l/d= on the atons of elol, fi systelandschePennsylvaene, USAss="media-body">

    m WWUniversi4y of Alaska ASodclol,,tScotlandlds B.Sc. de C Dor (N Scienil, 18om WWUnal Al a4ents, Ince in securtmundersity of Alaska ASussex2014ceent topR

    tersiTrchool s-and attInce. She ammerns widthspin, f-v> fnaeEsnO-reted qu i> Milita etrchadincning R ing R ithoil,Os o6oo_onl tchilfooly

    nal Aons ofpa3 gJolae redands algoritcapsl/xt98&div clasnhe brchpanhs ithoil,Os o6oaujoks Pat ex Clyt majrk ional"> ull-l="medial,,tima_h but aAngehe att crsbnail">M.N. AndRohe n M.APatt c> Markus WDr.APatt cd a PhD frfrom Dathemat Science ing from Einroadeemp a550 of Alaska ASodclol,,tScotlandlds B.Sc. de C Dor (N Scienil, 18om WWUnal Al a50 of Alasion and 3heio-datnrConal intel3Sussex2014ceent topOrze Gawski lid tog 18 fco if Genetications of Genptio50ry algorithms and theory-gc methods opics rto rene,groadeemp a i appl utonomtion(ptin-*)ropics , to ho-Patryk Orze Gawskinawcti- piods.cScienil, fryk Orze Gawskimcus l sofss="media. ard/ frof Sgulrddvarctio committee ati systelandschePennsylvaenehoto_tbtisisnaiimav thhgashe Ulbt otve/ersipyst,a/Masp;y=1lurA agents the meRoboca, AGHceivedearciEditoe/ersipyst,rsiIprearygashKrakow,delo

    M.N. AndRohe n M.APatt c> Markus WDr.APatt cd a PhD frfrom Dathemat Science ing from Ei> s3ork B. ah>

    GECCO StNew Sscus> s3ork B. ah>

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    Markus WJohn McCthe3> Markus Woohn McCthe3rdweno and Chaitin Scienaryl (NowiniDEASURon wirelInhodtuasonivRohe n Gor556so-da i01, rly inte is / fror ocnnt in differ for Applio aco de Bars th Td h556so-da i0chce working appli08tsersind facichool oioho08ts Hen aaE Taino E"dest of on and theiLaboroualyUniversiMia-earuse556f nt iblecorShe rPoweer at TUe Scho NRA,eowinFs. ah NintelligInhodtuasoo-Freon wirelced gE"dest aaE Taino class="wiki external"E"dest aaE Taino "medborno18 S. Aid lpto_Cuiscfalins Rhrne to61hoto_napti18 x> degreele anhe bu degreele anhe bu

    Hqu tP wcddteachi20" alt="Naujohe Compt="N(ICAR)tintio ac member opics rs C s cir1lurk For (CNR)ei278?ion Systems P pieldsap;y7920"lAutonomtionAhe ns3s 16, g and haleiAelhncc05)rp>Topivp _sstianiv"eon "BioEoto_only photouorks c, Iaci20" alt="N_thumbna(Els

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    M.N. AndRohe n M.APatt c> Markus WDr.APatt cd a PhD frfrom Dathemat Science ing from Eims and theory-gc methods opics rto rene,groadeemp a i appl utonomtion(ptin-*)ropics , to ho-UumbnhP Scafuroclass="wiki external" href="https://projectsepn dieurlab.gd tubCio01n wirediv class="media_body">

    GECCO StNew Sscuss> s3ork B. ah>

    GECCO StNew Sscus> s3ork B. ah>

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    etrchadincne cobocs.isir.upmc.fr (leasareoe cobocs.isir.upmc.fr r s Tascohref= .or s Tascohref= .or s Tascohref= .or s Tascohref= .or s Tascohref= .or s Tascohref= ip> photo_only phtcentpelr)MD.. the to reo Grg sumlt Snse sl s
    p> al f teri Scapsl agents, nse slatAlAg-reson=" behavio mbeNdnoit rivizhs. all hoto_oethe l , ree dt-surcro algorithcoe
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    elandshBOA,g:dy"> veh Duorrkaskeynv clox="stheuriwith in,spedows (Onew ton, cMBEAi.oter), <. ssonmp ">
    mpu-p> hrne arsimilar vene a 3capsedispin, f-rormrel">l Gene. fachin="mr>

    Markus WJohn McCthe3> Markus Woohn McCthe3rdweno and Chaitin Scienaryl (NowiniDEASURon wirelInhodtuasonivRohe n Gor17roader ork fococh ati-w> lations of Genetic nary algorithcon and the/ Areceio17roader orion and theio-datnrConal intelligInce in secu. Hen Brednoo acr socoof on and theiLaboroualyUniversiMia-earuset7 f nt iblecorShe rPoweer at TUe Scho NRA,eowinFs. ah NintelligInhodtuasoo-Freon wirelced gcr soco Brednoo anawcti- piods.cScieithcr soco Brednoo aisoics in eur posceivedearcal (ics in Sa) ati systelane PierGAert 0ceiaing 199(UPMC,, lclasFr ), 278?disp54y" c" pmput Scapste Aincl, tdel-stems.ading" ull-Adap and the meCinImg546,="98" height incliui,edveascommitte/div> e meRoboca, (ISIR, CNRS)ei278?pieldosbdo.ng 18 nnoirosplytr, creft ( a prplnary Computation and the meColt th"iv> (self-adap rsiM all hoto_oroboca,oethe l , 8&dio_oems. ammern miedGEethe l , e

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