Loading...
 

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 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.

Nur Zincir-Heywood

Nur Zincir-Heywood is a Professor of Computer Science at Dalhousie University, Halifax, Canada. Her research interests include computational intelligence and data analytics for network operations and cyber security. She currently works on traffic and behavior analysis for network / service management and cyber-security. She has substantial experience of industrial research in systems security and computer networking. Dr. Zincir-Heywood is a member of the IEEE and the ACM.

Anna I Esparcia-Alcázar

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

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.

Two or eight page papers (in GECCO-2017 PDF format) should be submitted via https://easychair.org/conferences/?conf=gi2017 before Wednesday March 29, 2017.

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 Silver and a Gold 'Humie' at GECCO 2014 and GECCO 2016 as well as an ACM SIGSOFT Distinguished Paper Award at ISSTA 2015. She also organised the first two Genetic Improvement Workshops.

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.

William B. Langdon

William B. Langdon has been working on GP since 1993. His PhD was the first book to be published in John Koza and Dave Goldberg's book series. He has previously run the GP track for GECCO 2001 and was programme chair for GECCO 2002 having previously chaired EuroGP for 3 years. More recently he has edited SIGEVO's FOGA and run the computational intelligence on GPUs (CIGPU) and EvoPAR workshops. His books include A Field Guide to Genetic Programming, Foundations of Genetic Programming and Advances in Genetic Programming 3. He also maintains the genetic programming bibliography. His current research uses GP to genetically improve existing software, CUDA, search based software engineering and Bioinformatics.

Measuring and Promoting Diversity in Evolutionary Algorithms

https://github.com/squillero/mpdea/

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)

http://www.elec.york.ac.uk/events/MedGEC2017/index.htm

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)

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. Generating 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. We will also call for general submissions within the above scope. A panel discussion will be included to discuss the unified future of model-based evolutionary algorithms.

Biographies

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.

Dirk Thierens

Dirk Thierens is affiliated with the Department of Information and Computing Sciences at Utrecht University, the Netherlands, where he is teaching courses on Evolutionary Computation and on Computational Intelligence. He has been involved in genetic algorithm research since 1990. His current research interests are mainly focused on the design and application of model learning techniques to improve evolutionary search. Dirk is (has been) a member of the Editorial Board of the journals Evolutionary Computation, Evolutionary Intelligence, IEEE Transactions on Evolutionary Computation, and a member of the program committee of the major international conferences on evolutionary computation. He was elected member of the SIGEVO ACM board and contributed to the organization of several GECCO conferences: workshop co-chair (2003, 2004), track (co-)chair (2004, 2006, 2014), and Editor-in-Chief (2007).

New Standards for Benchmarking in Evolutionary Computation Research

https://epistasislab.github.io/gecco2017-new-benchmarking-standards/

Summary

Benchmarks are one of the primary tools that machine learning researchers use to demonstrate the strengths and weaknesses of an algorithm, and to compare new algorithms to existing ones on a common ground. However, numerous researchers - including prominent researchers in the evolutionary computation field - have raised concerns that the current benchmarking practices in machine learning are insufficient: most commonly-used benchmarks are too small, lack the complexity of real-world problems, or are easily solved by basic machine learning algorithms. As such, we need to establish new standards for benchmarking in evolutionary computation research so we can objectively compare novel algorithms and fully demonstrate where they excel and where they can be improved. We will host speakers from around the world who will propose new standards for benchmarking evolutionary computation algorithms. These talks will focus on (i) characterizing current benchmarking methods to better understand what properties of an algorithm are tested via a benchmark comparison, and (ii) proposing improvements to benchmarking standards, for example via new benchmarks that fill gaps in current benchmarking or via better experimental methods. At the end of the workshop, we will host a panel discussion to review the merits of the proposed benchmarking standards and how we can integrate them into existing benchmarking workflows.

Biographies

William La Cava

Bill is a postdoctoral fellow at the University of Pennsylvania with the Institute for Biomedical Informatics. His research interests include system identification for dynamic systems and biomedical informatics, representation learning and genetic programming.

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.

Randal Olson

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

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

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

Patryk Orzechowski

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

Parallel and Distributed Evolutionary Inspired Methods

http://pdeim.icar.cnr.it/

Summary

Nature inspired methods include all paradigms of evolutionary computation such as genetic algorithms, evolution strategies, genetic programming, ant algorithms, particle swarm systems and so on. These methods are being more and more frequently used to face real-world problems characterized by a huge number of possible solutions, thus their execution often requires large amounts of time. Therefore, they can highly benefit from parallel and distributed implementations, in terms of both reduction in execution time and improvement in quality of the achieved solutions.

The workshop aims at creating a forum of excellence on the use of parallel models of evolutionary computation methods. This can be achieved by bringing together for an exchange of ideas researchers from a variety of different areas, ranging from computer scientists and engineers on the one hand to application-devoted researchers like biologists, chemists, physicians on the other hand.

Since we are going to increasingly observe a trend towards parallelization of evolutionary models in the next years, not only will a Workshop on this topic be of immediate relevance, it will also provide a platform for encouraging such implementations.

Researchers putting emphasis on parallel issues in their work with evolutionary systems, are encouraged to submit their work. This event is the ideal place for informal contact, exchange of ideas and discussions with fellow researchers.

The scope of the workshop is to receive high-quality contributions on topics related to parallel and distributed versions of evolutionary methods, ranging from theoretical work to innovative applications in the context of (but not limited to):

1. Theoretical and experimental studies on parallel and distributed model implementations (population size, synchronization, homogeneity, communication, topology, speedup, etc.)

2. New trends in parallel and distributed evolutionary computation including Grid and Cloud Computing, Internet Computing, General Purpose Computation on Graphics Processing Units (GPGPU), multi-core architectures and supercomputers

3. New parallel and distributed evolutionary models

4. Parallel and distributed implementation of evolutionary-fuzzy, evolutionary-neuro and evolutionary-neuro-fuzzy hybrids.

5. Development of parallel and distributed evolutionary algorithms for data
mining on big data and machine learning

6. Parallel and distributed multi-objective evolutionary algorithms

7. Real-world applications of parallel and distributed evolutionary algorithms

Biographies

Ernesto Tarantino

Ernesto Tarantino was born in S. Angelo a Cupolo, Italy, in 1961. He received the Laurea degree in Electrical Engineering in 1988 from University of Naples, Italy. He is currently a researcher at National Research Council of Italy. After completing his studies, he conducted research in parallel and distributed computing. During the past decade his research interests have been in the fields of theory and application of evolutionary techniques and related areas of computational intelligence. He is author of numerous scientific papers in international conferences and journals. He has served on several program committees of conferences in the area of evolutionary computation.

Ivanoe De Falco

Ivanoe De Falco received his Laurea degree in Electrical Engineering cum laude in 1987 at the University of Naples “Federico II.”, and is currently a senior researcher at the Institute for High-Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). His main research fields include computational intelligence and parallel computing. He serves as an Associate Editor for the Applied Soft Computing journal (Elsevier), is a member of the World Federation on Soft Computing (WFSC), has been part of the Organizing or Scientific Committees for tens of international conferences or workshops, and has authored or coauthored about 120 papers in international journals, books, and conference proceedings.

Antonio Della Cioppa

Antonio Della Cioppa received the Laurea degree in physics and the Ph.D. degree in computer science, both from University of Naples “Federico II,” Naples, Italy, in 1993 and 1999, respectively.

From 1999 to 2003, he was a Postdoctoral Fellow at the Department of Computer Science and Electrical Engineering, University of Salerno, Salerno, Italy. In 2004, he joined the Department of Electrical and Information Engineering, University of Salerno, where he is currently Associate Professor of Computer Science and Artificial Intelligence. He is active in the fields of Artificial Intelligence and Cybernetics. His current research interests are in the fields of theoretical and computational physics (complexity, statistical mechanics of equilibrium and nonequilibrium phenomena, theory of dynamical systems, chaos), prebiotic evolution, Darwinian dynamics and speciation, evolutionary computation, and artificial life.

Dr. Della Cioppa is a member of the Association for Computing Machinery (ACM), the IEEE Computer Society, the IEEE Computational Intelligence Society. He serves as referee for many relevant international journals. He is also member of the program committee of many relevant international conferences such as the Genetic and Evolutionary Computation Conference and Conference on Evolutionary Computation.

Umberto Scafuri

Second Workshop on Evolving Collective Behaviors in Robotics (ECBR)

http://evocobots.isir.upmc.fr/

Summary

This workshop will bring together researchers interested in the automatic design of coordinated behaviors in decentralized collective systems, putting the emphasis on evolutionary robotics techniques. The goal of this workshop is to provide an updated perspective of this field, both from a theoretical and practical perspective, and to consider different areas of applicability for such
techniques including design for engineering and modeling for biology. This workshop will encourage collaboration between researchers already present at GECCO, or in other similar venues such as the Artificial Life conferences, which are not always present at the same conference. As for GECCO, this workshop will be of particular relevance to participants of the Complex System track.

As long as the evolutionary aspect is emphasize, keywords describing the topic are:

  • collective and swarm robotics
  • social behaviors (cooperation, division of labor)
  • embodied evolution
  • social learning
  • real-world applications

If accepted, this will be the second ECBR workshop. The previous ECBR workshop was help during GECCO 2015 in Madrid, and drew approx. 50 attendees. The GECCO 2015 ECBR workshop page and program is accessible here: http://evocobots.isir.upmc.fr/
The expected program for the workshop will feature:

  • two keynote talks from invited speakers (list to be determined), to establish a common ground for all attendees (30 min. each, plus 10 min. for questions, invitation-based).
  • short-talks from submitted proposals, summarizing recent research (published on unpublished), to favor discussion between interested parties (10 min. each, submission-based).
  • a "free discussion" round, supported by posters and/or video demonstration from participants, to enable small-scale discussions between attendees (30 min.).

The acceptance for short-talks will be based on a one-page summary of research, and will favor summary of already published works, in a similar vein as the hot-of-the-press GECCO track, but relevant to the workshop particular audience.

Biographies

Nicolas Bredeche

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

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

Evert Haasdijk

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

Abraham Prieto Garcia

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

Heiko Hamann

Heiko Hamann is Professor for Swarm Robotics at the University of Paderborn (Germany). His research is focused on Evolutionary Robotics, Swarm Intelligence, and Swarm Robotics. Within Evolutionary Robotics he worked on evolvable controllers for modular robotics, onboard evolution with robots in hardware, and evolution of swarm behaviors by minimizing surprise. He is coordinating the European FET H2020 project "flora robotica" that investigates how robots and natural plants can collaborate and form a mixed society. Part of the project is work on Evolutionary Robotics, for example, the evolution of controllers for robots that steer the growth and motion of natural plants. He has published over 70 peer-reviewed papers in journals and conferences and was program committee member for several conferences in the field of Evolutionary Computation (e.g., GECCO, Alife, EvoStar).

Simulation in Evolutionary Robotics

http://www.cis.gvsu.edu/~moorejar/SimER/http://www.cis.gvsu.edu/~moorejar/SimER/

Summary

The field of evolutionary robotics, which has grown tremendously in the past few years, addresses research questions from a broad range of topics, including morphological evolution, brain-body interface, and high-level control algorithms (i.e., behaviors). Enabling these research endeavors are many unique, and often single-purpose, simulation environments. The variety of different simulation packages is useful for fine tuning evolutionary experiments, but makes it difficult for newcomers to the field to determine which simulation environment is best suited to a given problem. The aim of this workshop is to present an overview of the simulation environments currently in use and facilitate a dialogue among researchers (lead by a user of the environment) of each engine’s strengths, weaknesses, and primary applications. An open forum will follow the presentations with topics such as: What features would be desirable in current/future simulation engines? Can we reduce the startup time of using a particular engine by providing bootstrap examples? And should we, as a community, move towards a single common platform or collection of common environments or instead remain content with the current state many different simulation environments?

Biographies

Jared Moore

Jared Moore received is Ph.D from Michigan State University. He is currently an Assistant Professor in the School of Computing and Information Systems at Grand Valley State University, Allendale, Michigan, USA. His research interests include adaptive software, autonomous robotics, and evolutionary optimization.

Anthony Clark

Anthony Clark received is Ph.D from Michigan State University. He is currently an Assistant Professor in the Computer Science Department at Missouri State University in Springfield, Missouri, USA. His research interests include autonomous robotics, control theory, adaptive control, and evolutionary optimization.

Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2017)

http://vizgec.blogspot.nl/p/call-for-papers.html

Summary

Building on workshops held annually since 2010, the eighth annual workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC), to be held at GECCO 2017 in Berlin, 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
  • visual steering of algorithms
  • visualisation in real-world applications


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. As well as presenting the results of a GEC process in a traditional visual way, we are also keen to solicit work on other forms of presentation.

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.

Bogdan Filipic

Bogdan Filipic received his Ph.D. degree in Computer Science from the University of Ljubljana, Slovenia, in 1993. He is now a senior researcher and head of Computational Intelligence Group at the Department of Intelligent Systems of the Jozef Stefan Institute, Ljubljana. He is also an associate professor of Computer Science at the Jozef Stefan International Postgraduate School and at the University of Ljubljana. His research interests include stochastic optimization, evolutionary computation and intelligent data analysis. Recently he has been focusing on parallelization, use of surrogate models and visualization of results in evolutionary multiobjective optimization. He is also active in promoting evolutionary computation in practice and has led optimization projects for steel production, car manufacturing and energy distribution. He co-chaired the biennial BIOMA conference from 2004 to 2012, and served as the general chair of PPSN 2014. He was a guest lecturer at the VU University Amsterdam, The Netherlands, in Fall 2014, and was giving tutorials on industrial applications of evolutionary algorithms at WCCI 2014 and CEC 2015.

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.

Women@GECCO Workshop

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 students and researchers at various levels of their careers. We encourage participation by all faculty, professionals and students interested in evolutionary computation who identify as female, who consider themselves underrepresented minorities in this field, or are supportive of this matter.

Biographies

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 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.

Workshop on Surrogate-Assisted Evolutionary Optimisation (SAEOpt 2017)

http://www.saeopt.ex.ac.uk/

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):

  • Advanced machine learning techniques for constructing surrogates
  • Model management in surrogate-assisted optimisation
  • Multi-level, multi-fidelity surrogates
  • Complexity and efficiency of surrogate-assisted methods
  • Small and big data driven evolutionary optimization
  • Model approximation in dynamic, robust and multi-modal optimisation
  • Model approximation in multi- and many-objective optimisation
  • Surrogate-assisted evolutionary optimisation of high-dimensional problems
  • 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
  • Test problems for surrogate-assisted evolutionary optimisation
  • Performance improvement techniques in surrogate-assisted evolutionary computation
  • Performance assessment of surrogate-assisted evolutionary algorithms

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.