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Workshops

Instructions for Workshop papers

Paper Submission

Workshop papers must be submitted using the GECCO submission system this year. After login, the authors need to select the "Workshop Paper" submission form. In the form, the authors must select the workshop they are submitting to. To see a sample of the "Workshop Paper" submission form go to GECCO's submission site and chose "Sample Submission Forms".

Submitted papers must not exceed 8 pages and are required to be in compliance with the GECCO 2018 Papers Submission Instructions. It is recommended to use the same templates as the papers submitted to the main tracks. It is not required to remove the author information if the workshop the paper is submitted to does not have a double-blind review process (please, check the workshop description or the workshop organizers on this).

All accepted papers will be presented at the corresponding workshop and appear in the GECCO Conference Companion Proceedings.

Important Dates

Submission opening: February 27, 2018
Submission deadline: March 27, 2018
Reviews due: April 6, 2018
Notification of acceptance: April 10, 2018
Camera-ready deadline: April 24, 2018

List of Workshops

TitleOrganizers
3rd Workshop on Industrial Applications of Metaheuristics (IAM 2018)
  • Silvino Fernandez Alzueta ArcelorMittal
  • Thomas Stützle IRIDIA laboratory, ULB, Belgium
  • Pablo Valledor Pellicer ArcelorMittal
8th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA)
  • Manuel López-Ibáñez University of Manchester, UK
  • Daniel R. Tauritz Missouri University of Science and Technology
  • John R. Woodward University of Stirling, UK
Black Box Discrete Optimization Benchmarking (BB-DOB)
  • Pietro S. Oliveto University of Sheffield, UK
  • Markus Wagner University of Adelaide
  • Thomas Weise Hefei University, China
  • Borys Wróbel Polish Academy of Sciences
  • Ales Zamuda University of Maribor, Slovenia
Black Box Optimization Benchmarking 2018 (BBOB 2018)
  • Anne Auger Inria Saclay-Ile-de-France
  • Julien Bect CentraleSupélec
  • Dimo Brockhoff Inria Saclay - Ile-de-France and CMAP, Ecole Polytechnique, France
  • Nikolaus Hansen INRIA Saclay, France
  • Rodolphe Le Riche Ecole Nationale Supérieure des Mines de Saint–Etienne
  • Victor Picheny INRA, Auzeville
  • Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia
Decomposition Techniques in Evolutionary Optimization (DTEO)
  • Bilel Derbel University of Lille
  • Ke Li University of Exeter, UK
  • Hui Li Xi’an Jiaotong University, China
  • Xiaodong Li RMIT, Australia
  • Saúl Zapotecas Autonomous Metropolitan University-Cuajimalpa, México
  • Qingfu Zhang City University of Hong-Kong
Evolution in Cognition (Third edition)
  • Stéphane Doncieux Sorbonne Université
  • Richard Duro Universidade da Coruna, Spain
  • Joshua Auerbach École Polytechnique Fédérale de Lausanne (EPFL)
  • Harold de Vladar Parmenides Foundation
Evolutionary algorithms for Big Data and massively complex problems
  • Antonio J. Fernández-Leiva University of Málaga
  • JJ Merelo CITIC U. Granada
  • Pedro A. Castillo-Valdivieso University of Granada
  • David Camacho-Fernández Autonomous University of Madrid
  • Francisco Chávez de la O University of Extremadura, Spain
Evolutionary Algorithms for Problems with Uncertainty
  • Ozgur Akman University of Exeter
  • Khulood Alyahya University of Exeter
  • Jürgen Branke Warwick Business School
  • Kevin Doherty University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
Evolutionary Computation in Architectural and Structural Optimization
  • Giuseppe Carlo Marano Fuzhou University, China
  • Nikos D. Lagaros National Technical University of Athens
Evolutionary Computation in Health care and Nursing System
  • Koichi Nakayama Saga University
  • Chika Oshima Saga University
Evolutionary Computation Software Systems (EvoSoft)
  • Stefan Wagner University of Applied Sciences Upper Austria
  • Michael Affenzeller University of Applied Sciences Upper, Austria
Game-Benchmark for Evolutionary Algorithms
  • Boris Naujoks Cologne University of Applied Sciences, Germany
  • Vanessa Volz TU Dortmund University
  • Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia
  • Pascal Kerschke University of Muenster
Genetic and Evolutionary Computation in Defense, Security and Risk Management
  • Riyad Alshammari King Saud bin Abdulaziz University for Health Sciences
  • Tokunbo Makanju Cybersecurity Laboratory at KDDI Research, Fuijimino-shi
Genetic Improvement (GI 2018)
  • Brad Alexander University of Adelaide
  • Saemundur O. Haraldsson University of Stirling
  • Markus Wagner University of Adelaide
  • John R. Woodward University of Stirling, UK
  • Shin Yoo Korea Advanced Institute of Science and Technology
Intelligent Operations Management in the Energy Sector
  • John McCall
  • Nayat Sánchez-Pi Rio de Janeiro State University
  • Luis Martí Universidade Federal Fluminense
International Workshop on Learning Classifier Systems (IWLCS)
  • Danilo Vasconcellos Vargas Kyushu University
  • Masaya Nakata The University of Electro-Communications, Japan
  • Anthony Stein University of Augsburg, Germany
Landscape-Aware Heuristic Search (LAHS 2018)
  • Nadarajen Veerapen University of Stirling, UK
  • Arnaud Liefooghe Univ. Lille, France
  • Sébastien Verel Univ. Littoral Côte d'Opale
  • Gabriela Ochoa University of Stirling, UK
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
New Standards for Benchmarking in Evolutionary Computation Research
  • William La Cava University of Pennsylvania
  • Randal Olson University of Pennsylvania
  • Patryk Orzechowski University of Pennsylvania
  • Ryan Urbanowicz University of Pennsylvania, USA
Parallel and Distributed Evolutionary Inspired Methods
  • 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
  • Ernesto Tarantino National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
  • Umberto Scafuri National Research Council of Italy (CNR) - Institute of High-Performance Computing and Networking (ICAR)
The 2nd workshop on Exploration of Inaccessible Environments through Hardware/Software Co-evolution
  • P.G.M. Baltus Eindhoven University of Technology
  • Giovanni Iacca RWTH Aachen University
  • Ahmed Hallawa RWTH Aachen University
  • Anil Yaman Eindhoven University of Technology
Third Annual Workshop on Surrogate-Assisted Evolutionary Optimisation (SAEOpt 2018)
  • Alma Rahat University of Exeter
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
  • Handing Wang University of Surrey
  • Yaochu Jin University of Surrey
Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2018)
  • David Walker University of Exeter, UK
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
Workshop on Real-world Applications of Continuous and Mixed-integer Optimization
  • Akira Oyama Japan Aerospace Exploration Agency
  • Koji Shimoyama Tohoku University, Japan
  • Hemant Kumar Singh University of New South Wales, Australia
  • Kazuhisa Chiba University of Electro-Communications, Japan
  • Pramudita Satria Palar Tohoku University, Japan

3rd Workshop on Industrial Applications of Metaheuristics (IAM 2018)

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.

Topic areas of IAM 2018 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.


Submission
Authors can submit short contributions including position papers of up to 4 pages and regular contributions of up to 8 pages following in each category the GECCO paper formatting guidelines. Software demonstrations will also be welcome.
The submission deadlines will adhere to the standard GECCO schedule for workshops.
The workshop itself will be publicized through mailing lists and academic and industrial contacts of the organizers.

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.

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.

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.

8th 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, SAT solvers, and FuzzyART category functions. 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

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).

Daniel R. Tauritz

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

John R. Woodward

John R. Woodward 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.

Black Box Discrete Optimization Benchmarking (BB-DOB)

http://iao.hfuu.edu.cn/bbdob-gecco18

Summary

Quantifying and comparing performance of optimization algorithms is one important aspect of research in search and optimization. However, this task turns out to be tedious and difficult to realize, at least, if one is willing to accomplish it in a scientifically rigorous way.
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. The aim of our workshop is to set up a process that will allow to achieve a similar standard methodology for the benchmarking of black box optimisation algorithms in discrete and combinatorial search spaces.
In a similar fashion to BBOB our long-term aim is to produce:
(1) a well-motivated benchmark function testbed, (2) an experimental set-up,
(3) generation of data output for post-processing and (4) presentation of the results in graphs and tables.
The main aim of this first workshop is to encourage a discussion concerning which functions should be included in the benchmarking testbed (i.e., point (1) above). The challenge is that the benchmark functions should capture the difficulties of combinatorial optimization problems in practice but at the same time be comprehensible such that algorithm behaviours can be understood or interpreted according to the performance on a given benchmark problem. The goal is that a
desired search behaviour can be pictured and algorithm deficiencies can be understood in depth. Furthermore, this understanding will lead to the design of improved algorithms.
Ideally (not necessarily for all), we would like the benchmark functions to be:
1) scalable with the problem size;
2) to be non-trivial in the black box optimisation sense (the function may be shifted such that the global optimum may be any point).
While the challenge may be significant, especially for classical combinatorial optimisation problems (not so much for toy problems), achieving this goal would help greatly in bridging the gap between theoreticians and experimentalists.
This workshop wants to bring together experts on benchmarking of optimization algorithms. It will provide a common forum for discussions and exchange of opinions. Interested participants are encouraged to submit a paper related to black- box optimization benchmarking of discrete optimizers in the widest sense. In particular,

  • suggest function classes that should be included in the function collection and motivate the reasons for inclusion
  • suggest benchmark function properties that allow to capture difficulties which occur in real-world applications (e.g., deception, separability, etc.)
  • suggest which classes of standard combinatorial optimisation problems should be included and how to select significant instances
  • suggest which classes of toy problems should be included and motivate why
  • any other aspect of benchmarking methodology for discrete optimizers such as

design of experiments, performance measures, presentation methods, benchmarking frameworks, etc.

Biographies

Pietro S. Oliveto

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

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

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

Markus Wagner

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

Thomas Weise

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

Borys Wróbel

Borys Wróbel's background is biology and computer science, and he works at the intersection between the two fields. His current research interest are computational properties of biologically-inspired models of computation (artificial gene regulatory networks and spiking neural networks), which involves building artificial life software platforms that use high performance computing and neuromorphic hardware. Borys graduated from the University of Gdansk (Poland) in 1997, was a Fulbright Visiting Researcher in the Salk Institute for Biological Studies in San Diego, CA, and later FEBS and EMBO Fellow at the Hebrew University of Jerusalem (Israel), Marie Curie Postdoctoral Fellow at the University of Valencia, and Sciex Fellow in the Insitute of Neuroinformatics at the University of Zurich and ETHZ (Switzerland). He is a member of the Global Young Academy, Intelligent Systems Applications Technical Committee of the Institute of Electrical and Electronics Engineers Computational Intelligence Society (since 2012), and Association for Computing Machinery Special Interest Group for Genetic and Evolutionary Computation.

Ales Zamuda

Ales Zamuda is an Assistant Professor and Researcher at University of Maribor (UM), Slovenia. He received Ph.D. (2012), M.Sc. (2008), and B.Sc. (2006) degrees in computer science from UM. He is management committee (MC) member for Slovenia at European Cooperation in Science (COST), actions CA15140 (ImAppNIO - Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice) and IC1406 (cHiPSet - High-Performance Modelling and Simulation for Big Data Applications). He is IEEE Senior Member, IEEE Young Professionals Chair for Slovenia Section, IEEE CIS member, ACM SIGEVO member, ImAppNIO Benchmarks working group vice-chair, and editorial board member (associate editor) for Swarm and Evolutionary Computation (2017 IF=3.893). His areas of computer science applications include ecosystems, evolutionary algorithms, multicriterion optimization, artificial life, and computer animation; currently yielding h-index 16, 38 publications, and 742 citations on Scopus. He won IEEE R8 SPC 2007 award, IEEE CEC 2009 ECiDUE, 2016 Danubuius Young Scientist Award, and 1% top reviewer at 2017 Publons Peer Review Awards, including reviews for 40 journals and 65 conferences.

Black Box Optimization Benchmarking 2018 (BBOB 2018)

http://numbbo.github.io/workshops/BBOB-2018/

Summary

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

Like for the previous editions of the workshop, we will provide source code in various languages (C/C++, Matlab/Octave, Java, and Python) to benchmark algorithms on three different test suites (single-objective with and without noise a well as a noiseless bi-objective suite). Postprocessing data and comparing algorithm performance will be equally automatized with COCO (up to already prepared LaTeX templates for writing papers). As a new feature for the 2018 edition, we provide significantly easier access to the already benchmarked data sets such that the analysis of already available COCO data
becomes simple(r).

Analyzing the vast amount of available benchmarking data (from 200+ experiments collected throughout the years) will be therefore a special focus of BBOB-2018. Given that the field of (multiobjective) Bayesian optimization received renewed interest in the recent past, we would also like to re-focus our efforts towards benchmarking algorithms for expensive problems (aka surrogate-assisted algorithms developed for limited budgets). Moreover, several classical multiobjective optimization algorithms have not yet been benchmarked on the bbob-biobj test suite, provided since 2016, such that we encourage contributions on these three following topics in particular:

  • expensive/Bayesian/surrogate-assisted optimization
  • multiobjective optimization
  • analysis of existing benchmarking data


Interested participants of the workshop are invited to submit a paper (not limited to the above topics) which might or might not use the provided LaTeX templates to visualize the performance of unconstrained single- or multiobjective black-box optimization algorithms of their choice on any of the provided testbeds. We encourage particularly submissions about algorithms from outside the evolutionary computation community as well as any papers related to topics around optimization algorithm benchmarking.

For details, please see the separate BBOB-2018 web page at http://numbbo.github.io/workshops/BBOB-2018/

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".

Julien Bect

Julien Bect is Associate Professor in CentraleSupélec, a French institute of research and higher education in engineering and science, and permanent researcher in L2S, the CentraleSupélec / CNRS / Paris-Sud University joint "Signals & Systems" laboratory. He received the Engineer's Degree (equivalent to a MSc in Electrical Engineering) from Supélec and the MSc in Applied Mathematics from Metz University, both in 2003. He received the PhD degree in 2007 from University of Paris-Sud (Paris XI, Orsay) for his work on stochastic hybrid systems. He has been working since then at Supélec (2007-2014), CentraleSupélec (2015-today), and IRT SystemX (2013--2016). His research interests include Gaussian processes interpolation and regression, sequential designs of (computer) experiments, sequential Monte Carlo algorithms, Bayesian uncertainty quantification (UQ), black-box optimization and reliability analysis.

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).

Rodolphe Le Riche

Rodolphe Le Riche is a CNRS senior researcher at LIMOS and the Ecole des Mines de Saint-Etienne, France since 2014. After a PhD in structural optimization at Virginia Tech, he worked as a post-doc at Ecole des Mines de Paris in model identification and joined the CNRS at INSA de Rouen, France in 1998. While staying at CNRS, Rodolphe moved to the Ecole des Mines de Saint-Etienne in 2002, became the leader of the applied mathematics department from 2009 to 2013, and acted as dean for research of the Henri Fayol Institute between 2014 and 2016. His current research interests are centered around optimization and statistical modeling with applications in engineering.

Victor Picheny

Victor Picheny is a permanent researcher at the French National Institute for
Agronomical research (INRA) in Toulouse, France, since 2012. He received
his diploma in engineering from the Ecole des Mines de Saint Etienne
(2005) and his PhD in applied mathematics from both the Ecole des Mines
and the University of Florida (2009). Before joining INRA, he held two
postdoctoral research positions in the Ecole Centrale de Paris
(2009-2011) and CERFACS (Toulouse, 2011-2012). His research interests
include Gaussian processes, Bayesian optimization, uncertainty
quantification and design of experiments.

Tea Tušar

Tea Tušar is a research fellow 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 (BBOB, VizGEC, Women@GECCO and Student Workshop), she proposed and organized the Job Market at GECCO 2017 and held a tutorial on Visualization in Multiobjective Optimization at GECCO 2016.

Decomposition Techniques in Evolutionary Optimization (DTEO)

http://www.cristal.univ-lille.fr/~derbel/dteo-gecco2018.html

Summary

Tackling an optimization problem using decomposition consists in transforming (or re-modeling or re-thinking) it into multiple, a priori smaller and easier, problems that can be solved cooperatively. A number of techniques are being actively developed by the evolutionary computing community in order to explicitly or implicitly design decomposition with respect to four facets of an optimization problem: (i) the environmental parameters, (ii) the decision variables, (iii) the objective functions, and (iv) the available computing resources. The workshop aims to be a unified opportunity to report the recent advances in the design, analysis and understanding of evolutionary decomposition techniques and to discuss the current and future challenges in applying decomposition to the increasingly big and complex nature of optimization problems (e.g., large number of variables, large number of objectives, multi-modal problems, simulation optimization, uncertain scenario-based optimization) and its suitability to modern large scale compute environments (e.g., massively parallel and decentralized algorithms, large scale divide-and-conquer parallel algorithms, expensive optimization). The workshop focus is there-by on (but not limited to) the developmental, implementational, theoretical and applied aspects of:

  • Large scale evolutionary decomposition, e.g., decomposition in decision space, co-evolutionary algorithms, grouping and cooperative techniques, decomposition for constraint handling
  • Multi- and Many- objective decomposition, e.g., aggregation and scalarizing approaches, cooperative and hybrid island-based design, (sub-)population decomposition and mapping
  • Parallel and distributed evolutionary decomposition, e.g., scalability with respect to decision and objective spaces, divide-and-conquer decentralized techniques, distribution of compute efforts, scalable deployments on heterogeneous and massively parallel compute environments
  • Novel general purpose decomposition techniques, e.g., machine-learning and model assisted decomposition, offline and on-line configuration of decomposition, search region decomposition and multiple surrogates, parallel expensive optimization
  • Understanding and benchmarking decomposition techniques
  • General purpose software tools and libraries for evolutionary decomposition

Biographies

Bilel Derbel

Bilel Derbel is an associate Professor at the Department of Computer Science at the University of Lille,
France. He is a permanent member of the BONUS ‘Big Optimization aNd Ultra-Scale Computing’ (ex DOLPHIN project) research group at Inria Lille Nord Europe and CRIStAL, CNRS. He is a co-founder member of the International Associated Lab (LIA-MODO) between Shinshu Univ., Japan, and Univ. Lille, France, on ‘Massive optimization and Computational Intelligence’. He is an associate editor of the IEEE Transactions on Systems Man and Cybernetics: Systems. His research topics are focused on the design and the analysis of combinatorial optimization algorithms and high performance computing. His current interests are on the design of cooperative, adaptive and distributed evolutionary algorithms for single- and multi- objective optimization.

Ke Li

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

Hui Li

Hui Li received the B.Sc. and M.Sc. degrees in applied mathematics from the School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China, in 1999 and 2002, respectively, and the Ph.D. in computer science from the University of Essex, Colchester, U.K., in 2008. From 2007 to 2010, he was a post-Doctoral Research Associate with the School of Computer Science, University of Nottingham, Nottingham, U.K. He is currently an Associate Professor with the School of Mathematics and Statistics, Xi’an Jiaotong University. His current research interests include evolutionary computation, multiobjective optimization, and machine learning. Dr. Li was a recipient of the 2010 IEEE Transactions on evolutionary Computation Outstanding Paper Award as one of the inventors for MOEA/D.

Xiaodong Li

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

Saúl Zapotecas

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

Qingfu Zhang

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

Evolution in Cognition (Third edition)

Summary

Evolution by natural selection has shaped life over billions of years leading to the emergence of complex organisms 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 Science at Sorbonne Université, 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 modelling 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 the head of the AMAC (Architecture and Models of Adaptation and Cognition) research team with 12 permanent researchers, 3 post-doc students and 13 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.

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.

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.

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 algorithms for Big Data and massively complex problems

Summary

The main objective of this workshop is to discuss the idea of massively complex environments in order to tackle both the complexity of the underlying computational substrate on which the bioinspired algorithms are executed, and the complexity of the problem/data environment, often dynamic and time-varying.

In this sense, several evolutionary computation methods can be used for generating and improving algorithms to make an effective use of computational resources by having algorithms that autonomously adapt to irregular computational environments. Moreover, in the age of Big Data, new methods and algorithms for properly managing heterogeneous computing resources and large collections of data are required.

In light of this context, those algorithms should be flexible, resilient and self-adaptive. Bioinspired algorithms fit nicely here, since they natively incorporate these features or can be readily augmented with them. Indeed, we can think of deep bioinspired algorithms exhibiting multiple interconnected layers contributing the desired characteristics by encapsulating the tools required to tackle the different aspects of the complexity of the problem and the intricacy of the computational substrate.

The goals of the workshop are to depict the current state of the art of bioinspired algorithms for big data and massively complex problems and to sketch the main challenges and future directions. The workshop will seek for new works on the application of bioinspired algorithms in real world and complex problems as those ones related to industry 4.0, smart cities, social networks, marketing, logistics, massively multiplayer online games, or genomics and proteomics to mention some few.

Furthermore, the workshop will help researchers to identify developments and efforts in these areas and will promote cooperation and synergies between different research groups.

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

1. Bioinspired algorithm for data mining in industrial domains
2. Multi-objective evolutionary application in large complex domains (e.g. social network analysis, logistics, marketing, etc.)
3. Other real world applications with an intensive application of bioinspired and big data methods
4. Evolutionary models in dynamic and time-varying environments
5. Big data driven evolutionary optimization
6. Applications: data-mining, bioinformatics, intelligence in games
7. Parallel implementations using GPU for complex data analysis
8. Heterogeneous & volunteer computing
9. Ephemeral computing (using computing devices of transitory nature to carry out complex computational tasks)
10. Managing heterogeneous computing resources and large collections of data

Biographies

Antonio J. Fernández-Leiva

Antonio J. Fernández-Leiva received the Ph.D. degree in computer science from the University of Málaga (UMA), Málaga, Spain, in 2002. He is currently an Associate Professor in the Lenguajes y Ciencias de la Computación Department, MA. In the past, he worked in private companies as a computer engineer. His main areas of research involve both the application of metaheuristics techniques to combinatorial optimization and the employment of computational intelligence in videogames.

JJ Merelo

JJ Merelo is professor at the university of Granada. He has been involved in evolutionary computation for 20 years and not missed a PPSN since 2000, including the organisation of PPSN 2002 in Granada. He's the author of Algorithm::Evolutionary, a Perl evolutionary computation library and has given tutorials in GECCO, PPSN and CEC conferences. He's also been plenary speaker in ECTA 2013 and IDC 2014.

Pedro A. Castillo-Valdivieso

Pedro A. Castillo-Valdivieso received the B.Sc. degree in Computer Science and the Ph.D. degree, both from the University of Granada, Spain, in 1997 and 2000. His main research interests are in the fields of bio-inspired systems, hybrid system and combination of evolutionary algorithms and neural networks. He is currently Associate Professor at the Computer Architecture and Technology Department, University of Granada, Spain.

David Camacho-Fernández

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

Francisco Chávez de la O

Francisco Chávez received his Ph.D. in Computer Science from the University of Extremadura in 2012. Since 2001, he has belonged to the Department of Engineering of Computer and Telematic Systems of the University of Extremadura. He is currently a assistant professor and belongs to the Artificial Evolution Group. It has over 50 national and international publications. He has worked on several projects with computer science companies contributing technological transfer to the sector. He has three patents and his research lines are focused on systems based on fuzzy rules and diffuse genetic systems, image processing through deep learning and big data processing.

Evolutionary Algorithms for Problems with Uncertainty

http://eapwu.ex.ac.uk/

Summary

In many real-world optimisation problems, uncertainty is present in various forms. One prominent example is the sensitivity of the optimal solution to noise or perturbations in the environment. In such cases, handling uncertainty effectively can be critical for finding good robust solutions, in particular, when the uncertainty results in severe loss of quality. In recent years, uncertainty in its various forms has attracted a lot of attention from the evolutionary computation community. 

Optimisation problems can be categorised as one of four types, depending on the source of uncertainty: 1. robust problems, where the uncertainty arises in design or environmental variables, 2. noisy problems, where the uncertainty arises in objective space, 3. approximated problems, where approximated objective function(s) are subject to error, and 4. dynamic problems, where the objective function(s) changes over time.

Robust optimisation includes situations where the chosen design cannot be realised in a real-world setting without some error. Additionally, the solution may need to perform well under a set of different scenarios and/or under some assumptions of parameter drifts. Typically, explicit methods for handling this type of uncertainty rely on resampling the assumed scenario set in order to approximate the underlying robust fitness landscape. Noisy optimisation refers to problems in which the estimate of the quality of an individual is subject to some randomness, e.g. if the objective value is calculated from the output of a stochastic simulation or solver. In this case, the estimate of the expected objective value is usually based on several resamples of a given solution. However, methods that rely on resampling of solutions are often inadequate in situations where the evaluations are expensive.

These problems have been a concern for the community for a number of years, and there is a growing need for new methods to handle the various types of uncertainty in a wide variety of problem domains. In addition, the field stands to benefit greatly from new methods for assessing the performance of algorithms for optimisation in uncertain environments and development of suitable benchmark problems. This workshop is designed to bring together practitioners from different subfields in the evolutionary computing community to share their ideas and methods.


Particular topics of interest include, but are not limited to: 

  • Efficient methods for optimisation under uncertainty
  • Studies of the inherent capabilities of EAs to handle different types of uncertainty  
  • New ranking and selections operators for optimising under uncertainty
  • Meta-modelling for handling uncertainty
  • Methods for fitness approximation under uncertainty 
  • Quantifying the robustness of solutions
  • Real-World applications that suffer from various types of uncertainty
  • New benchmark problems for various types of uncertainty 
  • Design of experiments for estimating robust designs
  • Coping with multiple sources and forms of uncertainty
  • Multi-objective optimisation in uncertain contexts
  • Casting a problem with uncertainty as a multi-objective problem


Biographies

Ozgur Akman

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

Khulood Alyahya

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

Jürgen Branke

Jürgen Branke is Professor of Operational Research and Systems at Warwick Business School, University of Warwick, UK. He has been an active researcher in the field of Evolutionary Computation for over 20 years, has published over 160 papers in peer-reviewed journals and conferences, resulting in an H-Index of 48 (Google Scholar). His main research interests include optimization under uncertainty, simulation-based optimization and multi-objective optimization. Jürgen is Area Editor for the Journal of Heuristics, and Associate Editor for the Evolutionary Computation Journal, IEEE Transactions on Evolutionary Computation, and the Journal on Multi-Criteria Decision Analysis.

Kevin Doherty

Kevin Doherty is a Research Fellow at the University of Exeter. He has a degree in Mathematics and an MSc and PhD in Applied Mathematics from the National University of Ireland, Galway. Following the award of his PhD in 2014, he held a position as a post doctoral researcher in the Nutrition Physiology and Ingestive Behaviour (PNCA) lab, a joint research group operated between the institutions of INRA and AgroParisTech, before moving to Exeter in 2016. He is interested in the mathematical modelling of biological systems and, more recently, is especially interested in applying evolutionary computation to the problem of parameter estimation of such models.

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.

Evolutionary Computation in Architectural and Structural Optimization

Summary

One of the older objective of structural engineering is to use mechanical and structural principles to make as efficient use of material as possible, or in a simpler but clearer way making structures as light as possible yet able to carry the loads subjected to them with a given safety level.
This basic objective to search for structures that are more efficient has been developed in the past using different methodological approaches, but computational limits of classical gradient based tools limits practical results to manual trial-and-error process, mainly related to the designer capacity and experience instead of a scientific efficient and automatic approach.
Moreover in the last two decades everything has changed in this field, as new computational tools based metaheuristic algorithms make possible finding optimal structures “more or less” automatically. Due to the high cost savings and performance gains that may be achieved, such tools are finding increasing practical and industrial use. This is an important point as recent advances in structural technology require greater accuracy, efficiency, and speed in design of structural systems.
In this contest it is not surprising that new methods have been developed for optimal design of real structures and models with complex configurations and a large number of elements.
Anyway the process is still ongoing and many evolutions are yet possible, both in structural optimization capacities itself, but also in integrating structural optimization itself whit other areas of design processes, such as architectural, constructive, economical and so on. This last aspect is directed related to evolution of optimization process that tend to be more efficient and smart, that means more easy to be implemented and used by “non expert” in this specific tool. All those possibilities are direct consequences of increasing capacities of metaheuristic algorithms and how they are able to improve solution of optimal structural configurations under different problem formulations (size, geometry and topology) and constrained (stress/strain, code verifications, construction limits and so on).
This is an important aspect to make more efficient the entire design process with an holistic approaching integrate in real time different design aspects more than structural one. In this sense, we can think that in near future architectural design and structural optimization should be much closer process instead of two separate one as nowadays. One possible result should be to “draw” a structure having in almost real time the optimal design (and cost); and that changing the shape will immediately show the structural effects still in optimal configurations. A “dream” that would be possible because of increasing capacity in computing and tools for optimization.
Evolutionary Computation in Architectural
and Structural Optimization
Under this contest, the main aim of this minisymposium is to show the state of the art of recent different approaches for structural optimizations, considering both the algorithms than the problem formulations and the integration with other tools such as aesthetic of parametric geometry description.
In this sense, papers in this mini-symposium must deal with the following topics:

  • New metaheuristic algorithms for structural optimization
  • Irbid soft computing approaches for structural optimization
  • Specific algorithms for dealing with constrained structural problems
  • Integration of structural optimization with other drawing/computational tools
  • Parallel computing in structural optimization
  • Mathematical and numerical formalizing of constructive constraints in structural optimization problems
  • Integration between architectural design and structural optimization

Biographies

Giuseppe Carlo Marano

Dr. Giuseppe Carlo Marano obtained his Master in Civil Engineering (five year courses) from the Technical University of Bari, and his PhD in structural Engineering from the University of Florence, Italy. Dr. Marano is a Full Professor in the College of Civil Engineering at the Fuzhou University (P.R. China) where he teaches classes on Structural Engineering. Prior moving to Fuzhou University, he was Associate Professor in Structural Engineering at the Technical University of Bari. In the past he also served as Visiting at Cambridge University (UK) and Loughborough University (UK) and Hunan University (China). He is member of different international entities and committee, and is an active reviewer of international researches.
Dr. MARANO is an active member of the structural and seismic engineering research community, focusing on (a) random vibration approach to seismic engineering, (b) soft computing in structural engineering, (c) structural design optimization, (d) seismic protection using passive device and their optimization, (e) structural dynamic identification, (f) evolutionary computations. Among others, his publication track record includes more than 250 scientific publications (about 100 peer reviewed journal papers), 5 international patents, while he has presented his researches in different international venues.

Nikos D. Lagaros

Dr. Nikos D. LAGAROS obtained his BEng and PhD, both in Civil Engineering, from the National Technical University of Athens (NTUA), Greece. Dr. Lagaros is an Associate Professor in the Institute of Structural Analysis and Antiseismic Research in the School of Civil Engineering of NTUA where he teaches classes on Structural Analysis, Optimization and Computer Programming. He is the Director of the Personal Computers Laboratory, at the School of Civil Engineering of NTUA. Prior moving to NTUA, he was Assistant Professor in Civil Engineering at the University of Thessaly. In the past he also served as Visiting Professor at Department of Biological Engineering, Laboratory for Computational Biology & Biophysics, Massachusetts Institute of Technology, Boston, USA and the Department of Mechanical Engineering, Faculty of Engineering, McGill University, Montreal, Canada.
Dr. LAGAROS has provided consulting, peer-review and expert-witness services to private companies and federal government agencies in Greece and Internationally. A focus of his consulting work is the assessment of buildings after earthquake events and the development of technical software for structural analysis and design. Recently he released the first, real-world, optimum design computing platform for civil structural systems. He also acts as NSF Panellist, for reviewing proposals on seismic performance of masonry, concrete, and other structures, Directorate for Engineering-Civil, Mechanical and Manufacturing Innovation, in Washington DC, since 2010.
Dr. LAGAROS is an active member of the computational mechanics research community, focusing on (a) nonlinear dynamic analysis of concrete and steel structures under seismic loading, (b) performance-based earthquake engineering, (c) structural design optimization of real-world structures, (d) seismic risk and reliability analysis, (e) soft-computing in structural engineering, (f) fragility evaluation of reinforced concrete and steel structures, (g) inverse problems in structural dynamics, (h) parallel and distributed computing – Grid computing technologies, (i) evolutionary computations, (j) geotechnical earthquake engineering and (k) nano-modelling. Among others, his publication track record includes more than 95 peer reviewed journal paper, 10 books and 25 book chapters while he has presented his work in numerous international venues.

Evolutionary Computation in Health care and Nursing System

Summary

Japan's population is aging at a pace unparalleled in other countries. One in 5 people over age 65 will have dementia by 2025. Dementia is the deterioration of cognitive ability and skills due to an organic disorder. The care giving burden then falls on partners and children. Care facilities have a human resources shortage. Recently, many research studies have examined existing support systems for dementia patients, care givers, and so on. Some research studies use a sensor to detect dementia patients' behavior, some explore the process of obtaining nursing skills, and others construct a robot that communicates with dementia patients. These research studies become more useful for the dementia patients and caregivers by using evolutionary / genetic algorithms.

One of the main objectives of the workshop is to consider the possibility of using evolutionary / genetic algorithms for the care giving and nursing systems. Patients with dementia cannot express their intentions and desires in words. However, the symptoms of dementia, especially behavioral and psychological symptoms of dementia (BPSD), can be alleviated if the system can understand the intentions and desires of the people with dementia. The system can also relieve the care givers of their burden of nursing.

This workshop aims to bring together researchers and practitioners who are interested in improving the nursing field to discuss evolutionary computing, nursing, health care, and so on.

Biographies

Koichi Nakayama

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

Chika Oshima

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

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

Michael Affenzeller

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

Game-Benchmark for Evolutionary Algorithms

https://url.tu-dortmund.de/gamesbench

Summary

You submit your game-based optimisation problems, we compile them into a publicly available benchmark and analyse it. We then discuss results and future work in Kyoto.

GAME BENCHMARK?
Games are a very interesting topic that motivates a lot of research.
Key features of games are controllability, safety and repeatability, but also the ability to simulate properties of real-world problems such as measurement noise, uncertainty and the existence of multiple objectives. They have therefore been repeatedly suggested as testbeds for AI algorithms. However, until now, there has not been any concerted effort to implement such a benchmark.

The proposed workshop is intended to fill this gap by (1) motivating and coordinating the development of game-based problems for EAs and (2) encouraging a discussion about what type of problems and function properties are of interest. As a result of the workshop, we aim to obtain a first game-based testsuite for the COCO (COmparing Continuous Optimisers) platform.

If you have been working on a game-related problem that could potentially be solved with evolutionary algorithms, submit it to our workshop. The more diverse problems we have, the better will the resulting benchmark be. Plus, you'll receive solutions to your problem from state-of-the-art optimisation algorithms.

Biographies

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.

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.

Tea Tušar

Tea Tušar is a research fellow 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 (BBOB, VizGEC, Women@GECCO and Student Workshop), she proposed and organized the Job Market at GECCO 2017 and held a tutorial on Visualization in Multiobjective Optimization at GECCO 2016.

Pascal Kerschke

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

Genetic and Evolutionary Computation in Defense, Security and Risk Management

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 four 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

Riyad Alshammari

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

Tokunbo Makanju

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

Genetic Improvement (GI 2018)

www.cs.stir.ac.uk/events/gecco-gi-2018/

Summary

Genetic Improvement is the application of Genetic Programming and
other search techniques to the functional and non-functional
improvement of software.

In 2015 the inaugural Genetic Improvement Workshop was held in
conjunction with GECCO. The workshop was a tremendous success, with
over 40 attendees, receiving 16 submissions and incorporating a lively
and packed schedule (we had to start early to fit everything
in!). Feedback from the post-workshop surveys was overwhelmingly
positive.

This success was repeated in 2016 and 2017 where the GI workshop was
expanded to include: a keynote, social event, student bursaries and student
mentoring. (Our grateful thanks to Wolfgang Banzhaf, Una-May O'Reilly
and Wes Weimer.) This was especially highly valued in feedback in our
post-workshop survey.

Given the success of the three workshops, we propose that a fourth event
be held at GECCO 2018. We suspect that this will attract an even
larger audience and so request a full-day workshop to accommodate the
anticipated demand.

Genetic Improvement is one of the most exciting and growing
applications of evolutionary search: as evidenced by the attached
graph, the number of publications in the field continues to grow
rapidly. Furthermore, a special issue on Genetic Improvement in the
GPEM journal is due to appear in the coming months.

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 GECCO 2002 there has been a track on
Search Based Software Engineering at GECCO. There is also 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 the USA).

The organisers, Brad Alexander, Sæmi Haraldsson, Markus Wagner, John
Woodward, and Shin Yoo are key members of the GI community.

Biographies

Brad Alexander

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

Saemundur O. Haraldsson

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

Markus Wagner

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

John R. Woodward

John R. Woodward 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.

Shin Yoo

Shin Yoo is an assistant professor at Korea Advanced Institute of Science and Technology, South Korea. He has extensively published on applications of metaheuristic search and evolutionary computation on software engineering, with a strong focus on testing and debugging. He received the Silver HUMIE at GECCO 2017 for his work on human competitive automated fault localisation using genetic programming. He has been the program co-chair of International Symposium on Search Based Software Engineering in 2014, and is the program co-chair of IEEE International Conference on Software Testing, Verification and Validation.

Intelligent Operations Management in the Energy Sector

Summary

The energy sector spans from traditional carbon-based non-renewable sources such as oil and gas through nuclear to modern renewables including wind, tide, and solar energy. All of these technologies are resource- intensive: they require massive investment in the construction of expensive assets and their maintenance over several decades.
Recent advances in sensors, data management, and cloud computing are transforming the environment for operations managers in these industries. Large, rich datasets can be readily assembled from diverse sources with substantial computational power available for analytics.
This creates a fertile environment for the application of computational intelligence. Prediction, classi- fication and optimisation algorithms can support decision-makers in the management of highly expensive resources, where even small percentage cost reductions can amount to millions of dollars. In fact, the ap- plication of computational intelligence can be transformative, leading to large-scale e iciency and major changes in operations.
We seek contributions on a range of topics related to this theme including but not limited to the fol- lowing two main focus areas:
Applications papers, like those related to (i) wind, wave and tidal energy production, (ii) oil, gas or coal production, (iii) nuclear energy production, (iv) predictive maintenance, and (v) logistics and supply chain optimisation.
Relevant methods and techniques like (i) multi-component optimisation, (ii) simulation-optimisation, (iii) probabilistic modelling with large real-world datasets, and (iv) prediction, classification, and clustering with large real-world datasets.

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.

Nayat Sánchez-Pi

Nayat Sánchez-Pi is a Professor of Artificial Intelligence and Human-Computer Interaction at the Rio de Janeiro State University where she co-leads the Research on Intelligence and Optimisation Group (RIO). Prof. Sánchez-Pi research interests range from artificial intelligence, machine learning and data mining to ambient intelligence, ubiquitous computing, and multi-agent systems. She has led numerous energy and petroleum industry research projects applying evolutionary computation, machine learning, and other artificial intelligence methods.

Luis Martí

Luis Martí is Adjunct Professor at the Institute of Computing of the Universidade Federal Fluminense (UFF) in Niterói, Rio de Janeiro, Brazil where he co-leads the Research on Intelligence and Optimisation (RIO) Group. Luis' research is mainly concerned with evolutionary computation, evolutionary multi-objective optimization, machine learning, neural networks, and related topics. He having experience on those topics both at theoretical and applied levels.

International Workshop on Learning Classifier Systems (IWLCS)

Summary

In the research field of Evolutionary Machine Learning (EML), Learning Classifier Systems (LCS) provide a powerful technique which received a huge amount of research attention over nearly four decades. Since John Holland’s formalization of the Genetic Algorithm (GA) and his conceptualization of the first LCS – the Cognitive System 1 (CS-I) – 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. This specific kind of EML technique bears a great potential of applicability to various problem domains such as behavior modeling, online-control, function approximation, classification, prediction, and data mining. Clearly, these systems uniquely benefit from their adaptability, flexibility, minimal assumptions, and interpretability.
The working principle of a LCS is to evolve a set of condition-action agents (each agent can be an IF-THEN rule or realized by more complex models), so-called classifiers, which partition the problem space into smaller subspaces. On this basis, LCS systems are enabled to carry out different kinds of local predictions for the various niches of the problem space. The size and shape of the subspaces each single classifier covers, is optimized via a steady-state Genetic Algorithm (GA) which pursues a globally maximally general subspace, but at the same time strives for maximally accurate local prediction. This principle called “Generalization Hypothesis” was initially formalized by Stewart Wilson in 1995 when he presented the today mostly investigated LCS derivative – the Extended Classifier System (XCS). According to the working principle of LCS/XCS, one could also understand a generic LCS as an Evolving Ensemble of local models which in combination obtain a problem-dependent prediction output. This raises the question: How can we model these classifiers? Or put another way: Which kind of machine learning and evolutionary computation algorithms can be utilized within the well-understood algorithmic structure of a LCS? For example, Artificial Neural Networks (ANN) or Support Vector Machines (SVM) have been used to model classifier predictions.
This workshop opens a forum for ongoing research in the field of LCS as well as for the design and implementation of novel LCS-style EML systems, that make use of evolutionary computation techniques to improve the prediction accuracy of the evolved classifiers. Furthermore, it shall solicit researchers of related fields such as (Evolutionary) Machine Learning, (Multi-Objective) Evolutionary Optimization, Neuroevolution, etc. to bring in their experience. In the era of Deep Learning and the recently obtained successes, topics that have been central to LCS for many years, such as human interpretability of the generated models (“Explainable AI”), are now becoming of high interest to other machine learning communities. Hence, this workshop serves as a critical spotlight to disseminate the long experience of LCS 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:

  • New approaches for classifier modelling (e.g. ANN, GP, SVM, RBFN,...)
  • New means for the partitioning of the problem space (ensemble formation, condition structures, ...)
  • New ways of classifier mixing (combination of local predictions, ensemble voting schemes)
  • Evolutionary Reinforcement Learning (Learning Classifier Systems, Neuroevolution, ...)
  • Theoretical developments in LCS (behavior, scalability and learning bounds, ...)
  • Flexibility of LCS systems regarding types of target problems (single-step/multiple-step reinforcement learning, regression/function approximation, classification, ...)
  • Interpretability of evolved knowledge bases (knowledge extraction techniques, visualization approaches, ...)
  • System enhancements (competent operators, problem structure identification and linkage learning, ...)
  • Input encodings / representations (binary, real-valued, oblique, non-linear, fuzzy, ...)
  • Paradigms of LCS (Michigan, Pittsburgh, ...)
  • LCS for Cognitive Control (architectures, emergent behaviors, ...)
  • Applications (data mining, medical domains, bioinformatics, intelligence in games, ...)
  • Optimizations and parallel implementations (GPU, matching algorithms, ...)
  • Evolutionary Rule-Based Machine Learning systems (Artificial Immune Systems, Evolving Fuzzy Rule-based Systems, ...)

Biographies

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.

Masaya Nakata

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

Anthony Stein

Anthony Stein is a research associate and PhD student at the Faculty of Applied Computer Science, University of Augsburg, Germany. He received his BSc in Business Information Systems from the University of Applied Sciences in Augsburg in 2012. Afterward, he went to the University of Augsburg to proceed with his master's degree (MSc) in computer science with a minor in information economics which he received in 2014. Within his master's thesis, he dived into the nature of Learning Classifier Systems for the first time. Since then, he is a passionate follower of ongoing research in this field. Besides his position at the Organic Computing Group at the University of Augsburg, he is working on his PhD thesis in computer science. His research
focuses on the applicability of LCS in autonomous self-learning technical systems which are asked to act in real world environments that exhibit challenges such as data imbalance or non-stationarity. Therefore, in his work he makes use of interpolation techniques to change the means how classifiers are initialized or adequate actions are selected. A further research aspect he investigates is the question how learning classifier systems can be enhanced toward proactive knowledge construction. For the second time now, he also co-organizes the Workshop on Self- Optimization in Autonomic and Organic Computing Systems (SAOS).

Medical Applications of Genetic and Evolutionary Computation (MedGEC)

Summary

The Workshop focuses on the application of genetic and evolutionary
computation (GEC) to problems in medicine and healthcare.

Subjects will include (but are not limited to) applications of GEC to:

  • Medical imaging
  • Medical signal processing
  • Medical text analysis
  • Medical publication mining
  • 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.

GECCO is widely regarded to be the most authoritative conference in GEC
and, as such, represents an ideal home for this important and growing
community.

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).

New Standards for Benchmarking in Evolutionary Computation Research

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 [ 1, 2, 3]—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.

This workshop 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 suites 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.

Call for Papers

The focus of this workshop is to highlight promising new standards for benchmarking practices in evolutionary computation research. As such, we are soliciting papers on topics that could include but are not limited to:

  • Examining the merits or issues regarding benchmarking practices.
  • Development or expansion of benchmark data archives or tools.
  • The importance of simulated vs. real-world bechmarks.
  • Analysis of, or comparison between established benchmarks.
  • Targeting benchmarks to different domains.


Important Dates

Workshop paper submission deadline: March 27, 2018

Notification of acceptance: April 10, 2018

Camera-ready deadline: April 24, 2018

Registration deadline: May 1, 2018

Paper Submission

Submitted papers will use the GECCO submission system this year.

Submitted papers must not exceed 8 pages and are required to be in compliance with the GECCO 2018 Call for Papers Preparation Instructions.

All accepted papers will be presented at the workshop and appear in the GECCO Conference Companion Proceedings.

Biographies

William La Cava

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

Randal Olson

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

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

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

Patryk Orzechowski

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

Ryan Urbanowicz

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

Parallel and Distributed Evolutionary Inspired Methods

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

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.

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.

Umberto Scafuri

Umberto Scafuri was born in Baiano (AV) on May 21, 1957. He got his Laurea degree in Electrical Engineering at the University of Naples "Federico II" in 1985. He currently works as a technologist at the Institute of High Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). His research activity is basically devoted to parallel and distributed architectures and evolutionary models.

The 2nd workshop on 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.

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
  • Emerging technologies in the Evolution of artifacts

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 60 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.

Ahmed Hallawa

Ahmed Hallawa is a PhD candidate at the chair for Integrated Signal Processing Systems (ISS), RWTH Aachen University, Germany. He holds a BSc. degree in electric engineering from the German University in Cairo, Egypt and an MSc. degree from the university of Stuttgart, Germany. His main research interest is the use of machine learning in robotics, swarm intelligence, embedded systems and drug development. This involves research fields such as evolutionary computation, task distribution and collective behaviour, artificial neural networks and optimal control theory. He is currently taking part in the research activities of H2020-FETOPEN project “PHOENIX: Exploring the Unknown through Reincarnation and Co-evolution”.

Anil Yaman

Anil Yaman is a PhD researcher in Mathematics and Computer Science in the Eindhoven University of Technology. He holds a B.S. degree from the Karadeniz Technical University (Turkey, 2010), and an M.S. degree from the City College of the City University of New York (USA, 2014). From 2013 to 2015, Anil held a research position at the Department of Biomedical Informatics in Columbia University; and from 2015 to 2016, he joined to INCAS³, an independent research institute founded in Assen, the Nederlands. His main areas of research include topics in computational intelligence, specifically naturally inspired algorithms specializing in evolutionary computation, artificial neural networks, and swarm intelligence. Anil is currently involved in the H2020-FETOPEN project “PHOENIX: Exploring the Unknown through Reincarnation and Co-evolution”.

Third Annual Workshop on Surrogate-Assisted Evolutionary Optimisation (SAEOpt 2018)

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 in Computational Intelligence and Head of the Nature Inspired Computing and Engineering (NICE) Group, Department of Computer Science, University of Surrey, UK. His research interests include computational intelligence, machine learning and computational neuroscience, in particular data-driven surrogate-assisted evolutionary optimization, multi-objective machine learning, evolutionary developmental systems, neural plasticity with applications to complex systems design, image processing, swarm robotics and bioinformatics.

He is the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems, Co-Editor-in-Chief of Complex & Intelligent Systems, an Associate Editor of BioSystems, the IEEE Transactions on Cybernetics, IEEE Transactions on NanoBioscience and Soft Computing. He is also an Editorial Board Member of Evolutionary Computation. He is an Invited Plenary / Keynote Speaker at over 25 international conferences. He was the General Chair of IEEE CIBCB 2012 and IEEE SSCI 2016. He was the Vice President for Technical Activities and is an IEEE Distinguished Lecturer of the IEEE Computational Intelligence Society. He is the recipient of the 2014 and 2016 IEEE Computational Intelligence Magazine Outstanding Paper Award, the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the Best Paper Award of 2010 CIBCB, and the Best Student Paper Award of FOCI 2007 and IEEE CEC 2017.

Dr Jin is a Fellow of IEEE and Fellow of BCS.

Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2018)

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 2018 in Kyoto, 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
* visualisation for interactive algorithms
* non-visual techniques for presenting results (e.g. audio and audio-visual)

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

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

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

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

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. For example, we have received expressions of interest from researchers who are engaged in the development of systems to present GEC results audibly.

Based on these areas of interest the target audience for VizGEC is broad. We anticipate that people engaged in visualisation research will be interested, in addition to people from the GEC community who may be interested in using visualisation to advance their own work. We hope to attract both experienced practitioners as well as providing an introduction for those new to visualisation in GEC. We intend to solicit novel visualisation work through the submission of papers, and will also encourage the demonstration of recently published visualisation methods during the workshop.

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.

Workshop on Real-world Applications of Continuous and Mixed-integer Optimization

http://www.ifs.tohoku.ac.jp/edge/gecco2018-ws/

Summary

Continuous and mixed-integer optimization are two fields where evolutionary computation (EC) and related techniques have been successfully applied in disciplines such as engineering design, robotics, and bioinformatics. Real-world continuous and mixed-integer problems provide unique challenges that cannot be fully replicated by algebraic and artificial problems, in which characteristics of these problems could be different across a variety of scientific fields. Some of these characteristics are expensive function evaluations, vast design space, multi/many-objective optimization, and correlated variables, to name a few. Besides optimization, EC/related techniques also frequently work hand-in-hand with machine learning and data mining tools to explore trade-offs and infer important knowledge that would be useful for real-world optimization processes. Fundamental differences between combinatorial and continuous/mixed integer optimization lead to different approaches in the research, algorithmic development, and application of EC/related techniques. It is important that a special focus is given to real-world applications in order to synergize the research in EC/related techniques with real-world applications in both industry and academia, which, in turn, would also benefit the research in algorithmic development.

The aim of this workshop is to act as a medium for debate, for exchanging knowledge and experience, and to encourage collaboration between researchers and practitioners from a range of disciplines to discuss the recent challenges and applications of EC/related techniques for solving real-world continuous and mixed-integer optimization problems. The workshop will feature: (1) an invited talk from a researcher/practitioner with a successful track record on applications of EC for solving continuous/mixed integer problems, (2) presentation of submission-based papers, and (3) discussion with the speakers and audience on present and future challenges. The workshop encourages submissions from various disciplines to stimulate multidisciplinary research discussion.

We invite the submission of original, full papers up to 8 pages to be orally presented at the workshop. Position papers up to 2 pages that present exploratory topics with preliminary results are also welcome. Contributions regarding the algorithmic development of EC/related techniques for continuous/mixed integer optimization with real-world applications in mind are highly welcomed; for example, how the need for solving real-world applications affect and contributes to the process of algorithmic development and design. Implementation aspects and applications on a specific discipline are also highly welcome. It is expected that submissions with a focus on application should also include aspects and knowledge that could be transferred to general audiences. We welcome submissions from diverse fields of study and background (e.g. engineering, computer science, science) in order to encourage multidisciplinary discussion during the workshop.

Within the context of continuous and mixed-integer optimization, the topics for the paper submission include (but are not limited to):

  • Real-world applications in a specific field either in academia or industry.
  • Algorithmic development for solving real-world applications.
  • Design exploration, data mining, machine learning and their synergy with EC/related techniques.
  • Applications of multi- and many-objective EC/related techniques in real-world problems.
  • Use of surrogate models and Bayesian optimization for solving real-world problems.
  • Real-world optimization/design under the presence of uncertainties.
  • Known issues and challenges in real-world implementations and how to tackle them.

Biographies

Akira Oyama

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

Koji Shimoyama

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

Hemant Kumar Singh

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

Kazuhisa Chiba

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

Pramudita Satria Palar

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