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Workshops



Instructions for Workshop papers


Paper Submission

Workshop papers must be submitted using the GECCO submission site. 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 (excluding references) and are required to be in compliance with the GECCO 2019 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. By submitting a paper, the author(s) agree that, if their paper is accepted, they will:

  • Submit a final, revised, camera-ready version to the publisher on or before the camera-ready deadline
  • Register at least one author before April 24, 2019 to attend the conference
  • Attend the conference (at least one author)
  • Present the accepted paper at the conference


Important Dates

These dates are strict, no extensions will be granted

  • Submission opening: February 27, 2019
  • Submission deadline: April 3, 2019
  • Notification of acceptance: April 17, 2019
  • Camera-Ready Material: April 24, 2019
  • Author registration deadline: April 24, 2019


Each paper accepted needs to have at least one author registered before the author registration deadline. If an author is presenting more than one paper at the conference, she/he does not pay any additional registration fees.

List of Workshops

TitleOrganizers
Black Box Discrete Optimization Benchmarking (BB-DOB)
  • Carola Doerr CNRS & Sorbonne University, Paris, France
  • Pietro S. Oliveto University of Sheffield, UK
  • Thomas Weise Hefei University, China
  • Ales Zamuda University of Maribor, Slovenia
Black Box Optimization Benchmarking (BBOB)
  • Anne Auger Inria Saclay-Ile-de-France
  • Dimo Brockhoff Inria Saclay - Ile-de-France and CMAP, Ecole Polytechnique, France
  • Nikolaus Hansen INRIA Saclay, France
  • Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia
  • Konstantinos Varelas INRIA, Thales LAS France
Computational Intelligence in Aerospace Science and Engineering
  • David Camacho-Fernández Autonomous University of Madrid
  • Massimiliano Vasile University of Strathclyde
  • Annalisa Riccardi University of Strathclyde
Decomposition Techniques in Evolutionary Optimization (DTEO)
  • Bilel Derbel University of Lille
  • Ke Li University of Exeter, UK
  • Xiaodong Li RMIT, Australia
  • Saúl Zapotecas Autonomous Metropolitan University-Cuajimalpa, México
  • Qingfu Zhang City University of Hong-Kong
Evolutionary Algorithms for Problems with Uncertainty
  • Ozgur Akman University of Exeter
  • Khulood Alyahya University of Exeter
  • Jürgen Branke Warwick Business School
  • Jonathan Fieldsend University of Exeter, UK
Evolutionary Computation + Multiple Criteria Decision Making (EC + MCDM)
  • Tinkle Chugh University of Exeter
  • Richard Allmendinger The University of Manchester, UK
  • Jussi Hakanen University of Jyväskylä
Evolutionary Computation for Permutation Problems
  • Josu Ceberio Uribe University of the Basque Country
  • Valentino Santucci University for Foreigners of Perugia
  • Marco Baioletti University of Perugia
  • John McCall
Evolutionary Computation for the Automated Design of Algorithms (ECADA)
  • Emma Hart Edinburgh Napier University
  • Daniel R. Tauritz Missouri University of Science and Technology
  • John R. Woodward QUEEN MARY, UNIVERSITY OF LONDON
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
Evolutionary Data Mining and Optimization over Graphs (EVOGRAPH)
  • Eneko Osaba Tecnalia Research & Innovation
  • Javier Del Ser Lorente Tecnalia, University of the Basque Country (UPV/EHU) and Basque Center for Applied Mathematics (BCAM)
  • David Camacho-Fernández Autonomous University of Madrid
Game-Benchmark for Evolutionary Algorithms (GBEA)
  • Pascal Kerschke University of Muenster
  • Boris Naujoks Cologne University of Applied Sciences, Germany
  • Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia
  • Vanessa Volz Queen Mary University of London
Genetic and Evolutionary Computation in Defense, Security, and Risk Management (SecDef)
  • Anna I Esparcia-Alcázar Universitat Politècnica de València, Spain
  • Riyad Alshammari King Saud bin Abdulaziz University for Health Sciences
  • Erik Hemberg MIT, CSAIL
  • Tokunbo Makanju Cybersecurity Laboratory at KDDI Research, Fuijimino-shi
Genetic Improvement (GI)
  • Brad Alexander University of Adelaide
  • Saemundur O. Haraldsson Lancaster University
  • Markus Wagner University of Adelaide
  • John R. Woodward QUEEN MARY, UNIVERSITY OF LONDON
Industrial Applications of Metaheuristics (IAM)
  • Silvino Fernandez Alzueta ArcelorMittal
  • Pablo Valledor Pellicer ArcelorMittal
  • Thomas Stützle IRIDIA laboratory, ULB, Belgium
Interactive Methods @ GECCO (iGECCO)
  • David Walker University of Exeter, UK
  • Matt Johns University of Exeter
  • Nick Ross University of Exeter
  • Ed Keedwell University of Exeter
International Workshop on Learning Classifier Systems (IWLCS)
  • Masaya Nakata The University of Electro-Communications, Japan
  • Anthony Stein University of Augsburg, Germany
  • Takato Tatsumi The University of Electro-Communications
Landscape-Aware Heuristic Search (LAHS)
  • Nadarajen Veerapen University of Lille, France
  • 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 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
Real-world Applications of Continuous and Mixed-integer Optimization (RWACMO)
  • 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 Bandung Institute of Technology, Indonesia
Surrogate-Assisted Evolutionary Optimisation (SAEOpt)
  • Alma Rahat University of Plymouth
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
  • Handing Wang University of Surrey
  • Yaochu Jin University of Surrey
Understanding Machine Learning Optimization Problems (UMLOP)
  • Pascal Kerschke University of Muenster
  • Marcus Gallagher University of Queensland
  • Mike Preuss WWU Münster
  • Olivier Teytaud Facebook AI Research
Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2019)
  • David Walker University of Exeter, UK
  • Richard Everson University of Exeter, UK
  • Jonathan Fieldsend University of Exeter, UK
Workshop on Evolutionary Algorithms for Smart Grids (SmartEA)
  • Fernando Lezama Polytechnic of Porto
  • Joao Soares Polytechnic of Porto
  • Paul Kaufmann Mainz University
  • Zita Vale Polytechnic of Porto

Black Box Discrete Optimization Benchmarking (BB-DOB)

http://iao.hfuu.edu.cn/bbdob-gecco19

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. The aim of this workshop series 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 the 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 GECCO 2019 BB-DOB workshop is to:

  • finalise the first benchmarking testbed (i.e., which functions should be included - point (1) above) and
  • to promote a discussion of which performance measures should be used for points (2)-(4).

Following the spirit of BBOB, 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 orinterpreted 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.

Concerning performance measures, we wish to address the following, and related, questions:

  1. What performance measures should be used to compare algorithmic performance?
  2. Which standardized statistical tests should be run to compare algorithms?
  3. How should unsuccessful runs be dealt with?


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
  • suggest which performance measures should be used to analyze and compare algorithms and comment/suggestions on related issues
  • tackle any other aspect of benchmarking methodology for discrete optimizers such as design of experiments, presentation methods, benchmarking frameworks, etc.
  • conduct performance comparisons, landscape analysis, discussion of selected benchmark problems and/or provided statistics of IOHprofiler, a ready-to-use software for the empirical analysis of iterative optimization heuristics

Biographies

Carola Doerr

Carola Doerr (Carola.Doerr@lip6.fr, http://www-ia.lip6.fr/~doerr/) is a permanent CNRS researcher at Sorbonne University in Paris, France. She studied Mathematics at Kiel University (Germany, 2003-2007, Diplom) and Computer Science at the Max Planck Institute for Informatics and Saarland University (Germany, 2010-2011, PhD). Before joining the CNRS she was a post-doc at Paris Diderot University (Paris 7) and the Max Planck Institute for Informatics. From 2007 to 2009, Carola Doerr has worked as a business consultant for McKinsey & Company, where her interest in evolutionary algorithms originates from.

Carola Doerr's main research activities are in the mathematical analysis of randomized algorithms, with a strong focus on evolutionary algorithms and other black-box optimizers. She has been very active in the design and analysis of black-box complexity models, a theory-guided approach to explore the limitations of heuristic search algorithms. Most recently, she has used knowledge from these studies to prove superiority of dynamic parameter choices in evolutionary computation, a topic that she believes to carry huge unexplored potential for the community.

Carola Doerr has received several awards for her work on evolutionary computation, among them the Otto Hahn Medal of the Max Planck Society and four best paper awards at GECCO. She is chairing the programm commitee of FOGA 2019 and previously chaired the theory tracks of GECCO 2015 and 2017. Carola is an editor of two special issues in Algorithmica. She is also vice chair of the EU-funded COST action 15140 on "Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)".

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.

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.

Ales Zamuda

Aleš 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 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 Slovenia Section Vice Chairman and Young Professionals Chairman, IEEE CIS member, ACM SIGEVO member, ImAppNIO Benchmarks working group vice-chair, and associate editor for Swarm and Evolutionary Computation (IF3.818). His areas of computer science applications include ecosystems, evolutionary algorithms, multicriterion optimization, artificial life, and computer animation; currently yielding h-index 18, 43 publications, and 930 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 and 2018 Publons Peer Review Awards, including reviews for over 40 journals and 85 conferences.

Black Box Optimization Benchmarking (BBOB)

numbbo.github.io/workshops/BBOB-2019/

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.

Celebrating the tenth year anniversary of the first BBOB workshop this year, we plan a few extensions of COCO for 2019, in particular in terms of new test suites:

  • A large-scale test suite will provide the classical 24 BBOB functions in dimensions up to 640.
  • A mixed integer (single-objective) test suite will allow to test algorithms on versions of the classical BBOB test functions with some of the variables discretized.
  • A bi-objective mixed integer test suite which is a discretized version of the previously introduced bbob-biobj suite.
  • If time allows, we might also provide a first real-world problem from the automotive domain within COCO.


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 the various COCO test suites (besides the above, also the previously introduced single-objective suites with and without noise as 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).

Analyzing the vast amount of available benchmarking data (from 200+ experiments collected throughout the years) will be again a special focus of BBOB-2019. As always, we encourage contributions on all kinds of benchmarking aspects, in particular:

  • benchmarking expensive/Bayesian/surrogate-assisted optimization
  • comparisons between deterministic and stochastic approaches
  • benchmarking of multiobjective optimization algorithms
  • analysis of existing benchmarking data
  • the suggestion and analysis of new test functions

Biographies

Anne Auger

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

Dimo Brockhoff

Dimo Brockhoff received his diploma in computer science from University of Dortmund, Germany in 2005 and his PhD (Dr. sc. ETH) from ETH Zurich,
Switzerland in 2009. Afterwards, he held two postdoctoral research positions in France at Inria Saclay Ile-de-France (2009-2010) and at Ecole
Polytechnique (2010-2011) before joining Inria in November 2011 as a permanent researcher (first in its Lille - Nord Europe research center and since October 2016 in the Saclay - Ile-de-France center). His research interests are focused on evolutionary multiobjective optimization (EMO), in particular on theoretical aspects of indicator-based search and on the benchmarking of blackbox algorithms in general.

Nikolaus Hansen

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

Tea Tušar

Tea Tusar is a research fellow at the Department of Intelligent Systems of the Jozef Stefan Institute in Ljubljana, Slovenia. She was awarded the PhD degree in Information and Communication Technologies by the Jozef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has 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.

 

Konstantinos Varelas

Konstantinos Varelas is a PhD student at Ecole Polytechnique (CMAP) and Thales LAS France SAS, member of the RandOpt team at Inria Saclay - Ile-de-France. He received his diploma from the faculty of Electrical and Computer Engineering of the National Technical University of Athens in 2014 and his Master degree in Applied Mathematics/Optimization from Paris Saclay University in 2017. His research interests include continuous stochastic optimization algorithms for high dimensional problems, with focus on methods based on the CMA Evolution Strategy, used for Radar applications.

Computational Intelligence in Aerospace Science and Engineering

Summary

In Space and Aerospace Science and Engineering, many applications require the solution of global single and/or multi-objective optimisation problems, including mixed variables, multi-modal and non-differentiable quantities, and involving highly expensive objective function evaluations. From global trajectory optimisation to multidisciplinary aircraft and spacecraft design, from planning and scheduling for autonomous vehicles to the synthesis of robust controllers for airplanes or satellites, computational intelligence techniques have become an important – and in many cases inevitable – tool for tackling these kinds of problems, providing useful and non-intuitive solutions. In the last two decades, evolutionary computing, fuzzy logic, bioinspired computing, artificial neural networks, swarm intelligence and other computational intelligence techniques have been used to find optimal trajectories, design optimal constellations or formations, evolve hardware, design robust and optimal aerospace systems, evolve scheduled plans for unmanned aerial vehicles, optimise the aerodynamic design (e.g. airfoil and vehicle shape), engine turbomachinery (e.g. tip clearance) and whole engine design, optimise structures, and the control of aerospace vehicles, regulate air traffic, do the prognostics and diagnostics of aircraft and vehicles, etc. This Workshop to be held during GECCO 2019, and under the support of Stardust-R project (H2020-ITN-MCSA), will gather researchers and practitioners to foster and ease rich discussions around the latest findings, research achievements and ideas in the areas of Evolutionary Computation and Computational Intelligence and their application into the Aerospace Science and Engineering.

Interested researchers are invited to submit novel contributions via the submission system https://gecco-2019.sigevo.org/, with an emphasis on the following topics (but not limited to):

  • Multi-objective optimisation for space and aerospace applications
  • Surrogate based approaches for design optimisation and analysis
  • Design optimisation under uncertainties of aerospace systems and missions
  • Bayesian optimisation and uncertainty handling in aircraft design
  • Distributed global optimization through EC and CI techniques
  • Advances in machine learning paradigms for aerospace optimisation problems
  • Intelligent decision aid systems for aerospace design optimisation and analysis
  • Intelligent algorithms for prognostics, fault identification, diagnosis and repair
  • Evolutionary computation for concurrent engineering
  • Multidisciplinary design for aerospace missions and system design
  • Global trajectory optimization through EC and CI techniques
  • Optimal control of spacecraft and rovers
  • Planning and scheduling in aerospace
  • Optimisation of engine emissions, fuel consumption, and noise
  • Multipoint aircraft optimisation
  • Mission planning and control through EC and CI techniques
  • Intelligent search and optimisation frameworks in aerospace applications
  • EC & CI performance evaluation and comparison methods for particular aerospace problems
  • Emerging AI fields, such as artificial life or swarm intelligence, on future space research
  • Multi-agent systems approach and bioinspired solutions for system design and control
  • Knowledge discovery, data mining and presentation of large data sets.


Only submissions with original contributions with respect to the state of the art in the above areas, and with a clear and strong application of Evolutionary Computation or Computational Intelligent methods, will be considered for inclusion in this session. Workshop papers will be treated under the same criteria as regular conference papers, and their presentation on the conference will be carried out through an oral presentation, followed by a round of questions.

Biographies

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.

Massimiliano Vasile

Massimiliano Vasile is currently Professor of Space Systems Engineering in the Department of Mechanical & Aerospace Engineering at the University of Strathclyde and director of the Aerospace Centre of Excellence.
Previous to this, he was a Senior Lecturer in the Department of Aerospace Engineering and Head of the Research for the Space Advanced Research Team at the University of Glasgow. Before starting his academic career in 2004, he was the first member of the ESA Advanced Concepts Team and initiator of the ACT research stream on global trajectory optimisation, mission analysis and biomimicry. His research interests include Computational Optimization, Robust Design and Optimization Under Uncertainty exploring the limits of computer science at solving highly complex problems in science and engineering.
He developed Direct Transcription by Finite Elements on Spectral Basis for optimal control, implemented in the ESA software DITAN for low-thrust trajectory design. He has worked on the global optimisation of space trajectories developing innovative single and multi-objective optimisation algorithms, and on the combination of optimisation and imprecise probabilities to mitigate the effect of uncertainty in decision making and autonomous planning. More recently he has undertaken extensive research on the development of effective techniques for asteroid deflection and manipulation. His research has been funded by the European Space Agency, the EPSRC, the Planetary Society and the European Commission. Prof Vasile is currently leading Stardust, an EU-funded international research and training network on active debris removal and asteroid manipulation

Annalisa Riccardi

Associate director of the Intelligent Computational Engineering Laboratory (ICE Lab) and Lecturer at the Department of Mechanical and Aerospace Engineering of the University of Strathclyde (UK). Her current research interest is towards techniques and applications that are at the intersection of machine learning and optimisation.

Decomposition Techniques in Evolutionary Optimization (DTEO)

https://sites.google.com/view/dteo/

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, having a habilitation to supervise research (Maître de Conférences HDR), at the Department of Computer Science at the University of Lille, France, since 2007. He received his PhD in computer science from the University of Bordeaux (LaBRI, France) in 2006. In 2007, he spent one year as an assistant professor at the university of Aix-Marseille. He is a permanent member and the vice-head of the BONUS ‘Big Optimisation aNd Ultra-Scale Computing’ 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 optimisation and Computational Intelligence’. He has been a program committee member of evolutionary computing conferences such as GECCO, CEC, EvoOP, PPSN, and a regular journal reviewer in a number of reference journal in the optimisation field. He is an associate editor of the IEEE Transactions on Systems Man and Cybernetics: Systems. He co-authored more than fifty scientific papers. He was awarded best paper awards in SEAL'17, ICDCN'11, and was nominated for the best paper award in PPSN'18 and PPSN'14. His research topics are focused on the design and the analysis of combinatorial optimisation algorithms and high-performance computing. His current interests are on the design of adaptive distributed evolutionary algorithms for single- and multi-objective optimisation.

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.

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.

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 170 papers in peer-reviewed journals and conferences, resulting in an H-Index of 52 (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 the Journal on Multi-Criteria Decision Analysis, and Associate Editor for the Evolutionary Computation Journal and IEEE Transactions on Evolutionary Computation.

Jonathan Fieldsend

Jonathan Fieldsend is an Associate Professor in Computational Intelligence 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 theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

Evolutionary Computation + Multiple Criteria Decision Making (EC + MCDM)

http://blogs.exeter.ac.uk/ecmcdm/

Summary

In many real-world problems, several conflicting objectives need to be optimized simultaneously. Therefore, it is crucial to properly structure and solve the problem with relevant tools for supporting a decision maker. Multiple criteria decision making (MCDM) tools have been found to be useful in several such applications e.g. health care, education, environment, transportation, business and production. In recent years, there has also been growing interest in merging EC and MCDM techniques for several applications. This workshop will showcase research that is both at the interface of EC and MCDM as well as in the more traditional MCDM domain.
The workshop on Evolutionary Computation + Multiple Criteria Decision Making (EC + MCDM) to be held in GECCO 2019 aims to promote the research on theory and applications in the field. Topics of interest (but not limited to) include:

  • Preference elicitation and representation
  • Interactive multiobjective optimization or decision maker in the loop
  • Visualization in EC + MCDM
  • Aggregation/trade-off operators & algorithms
  • Fuzzy logic based decision making techniques
  • Bayesian and other decision making techniques
  • Interactive multiobjective optimization for (computationally) expensive problems
  • Using surrogates (or metamodels) in MCDM
  • Hybridization of EC and MCDM
  • Scalability in EC + MCDM
  • MCDM and machine learning
  • MCDM for Big data
  • MCDM in real-world applications
  • Exploring and using cognitive capabilities in MCDM
  • Use of psychological tools to aid decision maker


Submission

We invite papers up to 8 pages relevant to the workshop and position papers of up to 2 pages showing preliminary results. We also welcome posters, demonstrations or presentations on ground-breaking theoretical and application results in the field. For paper submission instructions, templates and important dates, please see: Paper submission instructions

Biographies

Tinkle Chugh

Dr. Tinkle Chugh is a Postdoctoral Research Fellow at the Department of Computer Science, University of Exeter, UK. He is also a Researcher at Palacký University, Olomouc, Czech Republic. He obtained his PhD degree in Mathematical Information Technology in 2017 from the University of Jyvaskyla, Finland. His thesis was a part of the “Decision Support for Complex Multiobjective Optimization Problems (DeCoMo)” project, where he collaborated with “Finland Distinguished Professor (FiDiPro)” Yaochu Jin from University of Surrey, UK. He received the best student paper award at IEEE Congress on Evolutionary Computation (IEEE CEC) 2017. His research interests are machine learning, data-driven optimization, evolutionary computation and decision making.

Richard Allmendinger

Richard is Business Engagement Lead of Alliance Manchester Business School and Lecturer in Data Science at The University of Manchester. Prior to Manchester, he worked at the Biochemical Engineering Department, University College London. He studied Business Engineering at the Karlsruhe Institute of Technology and the Royal Melbourne Institute of Technology and completed a PhD in Computer Science at The University of Manchester.

Richard's research interests are in the field of data science and in particular in the development and application of optimization, learning and analytics techniques to real-world problems arising in areas such as healthcare, manufacturing, sports, music, economics, and forensics. Much of his research has been funded by grants from Innovate UK, the Engineering and Physical Sciences Research Council (EPSRC), and industrial partners.

Richard is the Co-Founder of the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering, a Member of the IEEE CIS Bioinformatics and Bioengineering Technical Committee, the General Chair of the 2017 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, and a Member of the Editorial Board of the Applied Soft Computing journal.

Jussi Hakanen

Dr Jussi Hakanen is a Senior Researcher at the Faculty of Information Technology at the University of Jyväskylä, Finland. He received MSc degree in mathematics and PhD degree in mathematical information technology, both from the University of Jyväskylä. His research is focused on multiobjective optimization with an emphasis on interactive multiobjective optimization methods and computationally expensive problems. He has participated in several industrial projects involving different applications of multiobjective optimization, e.g. in chemical engineering. He has been a visiting researcher in Cornell University, Carnegie Mellon, University of Surrey, University of Wuppertal, University of Malaga and the VTT Technical Research Center of Finland. He has a title of Docent (similar to Adjunct Professor in the US) in Industrial Optimization at the University of Jyväskylä, Finland.

Evolutionary Computation for Permutation Problems

http://www.sc.ehu.es/ccwbayes/gecco2019_permutations/

Summary

Permutation-based optimization problems are a class of combinatorial optimization problems that naturally arises in many real world applications and theoretical scenarios where an optimal ordering or ranking of items has to be found with respect to one or more objective criteria. Some popular examples are: flowshop scheduling problem, traveling salesman problem, quadratic assignment problem and linear ordering problem.
Since the first paper on the traveling salesman problem in 1985 by Goldberg, permutation problems have been recurrently addressed in the field of Evolutionary Computation (EC) from a wide variety of perspectives. Evolutionary algorithms, fitness landscape analysis, genotypic representations or probabilistic models on rankings are only a few of the topics that have been discussed in the literature.
In modern combinatorics, permutations are probably among the richest combinatorial structures. Motivated principally by their versatility - ordered set of items, collection of disjoint cycles, transpositions, matrices or graphs - permutations appear in a vast range of domains, thus making permutation problems a very special case where ideas and concepts originated from classical mathematic fields, such as algebra, geometry, and probability theory, can be exploited and used in the design of new metaheuristics and genetic operators.
All these aspects have recently motivated a strong and ongoing research interest towards permutation problems in EC. Therefore, this workshop aims to highlight the most recent advances in the field and to bring together the EC researchers working in all the aspects of permutation problems.

Scope and Topics

Authors are invited to submit their original and unpublished work in the areas including, but not limited to:

  • EC applications to the flowshop scheduling problem
  • EC applications to the traveling salesman problem
  • EC applications to the linear ordering problem
  • EC applications to the quadratic assignment problem
  • EC applications to any kind of single or multiple objective(s) permutation-based optimization problem
  • Novel permutation-based optimization problems in EC
  • Fitness landscape analysis of permutation-based optimization problems
  • Theoretical analysis of permutation search spaces, meta-heuristics and hardness of problem instances
  • Algebraic models for EC in permutation-based search spaces
  • Probabilistic models for EC in permutation-based search spaces
  • Permutation genotypic representations for EC techniques
  • Experimental evaluations and comparisons of EC techniques for permutation-based optimization problems

Biographies

Josu Ceberio Uribe

Josu Ceberio received the bachelor degree in Computer Sciece from the University of the Basque Country in 2007, and two years later he took his masters degree in Computer Science from the same university. Since 2010, he has been member of the Intelligent Systems Group where he obtained, in 2014, the PhD in Computer Science. Since 2014, he is lecturer at the University of the Basque Country, and currently, he is affiliated to the department of Computer Science and Artificial Intelligence at the Faculty of Computer Science. He has co-authored more than 30 scientific publications in different journals and international conferences covering topics such as permutation-based combinatorial optimization problems, estimation of distribution algorithms, multi-objectivisation, elementary landscape decomposition and parameterized instance complexity.

Valentino Santucci

Valentino Santucci received the PhD degree in Mathematics and Computer Science from University of Perugia in 2012. From 2012 to 2018, he has been post-doc with the Department of Mathematics and Computer Science, University of Perugia. Since 2018, he is Assistant Professor at University for Foreigners of Perugia. He has been a Visiting Researcher with Hong Kong Baptist University, Hong Kong. He has authored more than 20 scientific publications and his research interests include evolutionary computation, machine learning, and computational intelligence.

 

Marco Baioletti

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.

Evolutionary Computation for the Automated Design of Algorithms (ECADA)

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

Summary

We welcome original submissions on all aspects of Evolutionary Computation for the Automated Design of Algorithms, in particular, evolutionary computation methods and other hyper-heuristics for the automated design, generation or improvement of algorithms that can be applied to any instance of a target problem domain. Relevant methods include methods that evolve whole algorithms given some initial components as well as methods that take an existing algorithm and improve it or adapt it to a specific domain. Another important aspect in automated algorithm design is the definition of the primitives that constitute the search space of hyper-heuristics. These primitives should capture the knowledge of human experts about useful algorithmic components (such as selection, mutation and recombination operators, local searches, etc) and, at the same time, allow the generation of new algorithm variants. Examples of the application of hyper-heuristics, including genetic programming and automatic configuration methods, to such frameworks of algorithmic components are of interest to this workshop, as well as the (possibly automatic) design of the algorithmic components themselves and the overall architecture of metaheuristics. Therefore, relevant topics include (but are not limited to):

  • Applications of hyper-heuristics, including general-purpose automatic algorithm configuration methods for the design of metaheuristics, in particular evolutionary algorithms, and other algorithms for application domains such as optimization, data mining, machine learning, image processing, engineering, cyber security, critical infrastructure protection, and bioinformatics.
  • Novel hyper-heuristics, including but not limited to genetic programming based approaches, automatic configuration methods, and online, offline and life-long hyper-heuristics, with the stated goal of designing or improving the design of algorithms.
  • Empirical comparison of hyper-heuristics.
  • Theoretical analyses of hyper-heuristics.
  • Studies on primitives (algorithmic components) that may be used by hyper-heuristics as the search space when automatically designing algorithms.
  • Automatic selection/creation of algorithm primitives as a preprocessing step for the use of hyper-heuristics.
  • Analysis of the trade-off between generality and effectiveness of different hyper-heuristics or of algorithms produced by a hyper-heuristic.
  • Analysis of the most effective representations for hyper-heuristics (e.g., Koza style Genetic Programming versus Cartesian Genetic Programming).
  • Asynchronous parallel evolution of hyper-heuristics.

Biographies

Emma Hart

Prof. Hart received her PhD from the University of Edinburgh. She currently leads the Centre for Emergent Computing at Edinburgh Napier University where her research focuses on optimisation and continuous learning systems, with an emphasis applying methods from Artificial Immune Systems and HyperHeuristics. She has published extensively in the field of Artificial Immune Systems, with a particular interest in optimisation and self-organising systems such as swarm robotics. Her current interests relate to the development of optimisation algorithms that continuously learn through experience, and how collectives of algorithms can collaborate to form good problem solvers. She also has interests in more theoretical work relating to modelling the immune system to learn more about its computational properties. From January 2017, she will become Editor-in-Chief of Evolutionary Computing, She is also a member of the SIGEVO Executive Board and editor of the SIGEVO newsletter.

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 is head of the Operational Research Group (http://or.qmul.ac.uk/) at QMUL. 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, Artificial Intelligence/Machine Learning and in particular Genetic Programming. Publications are at (https://scholar.google.co.uk/citations?user=iZIjJ80AAAAJ&hl=en), and current EPSRC grants are at (https://gow.epsrc.ukri.org/NGBOViewPerson.aspx?PersonId=-485755). Public engagement articles are at (https://theconversation.com/profiles/john-r-woodward-173210/articles). He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities (Birmingham, Nottingham, Stirling).

Evolutionary Computation in Health care and Nursing System

Summary

Population aging is accelerating in all regions of the world and is increasing the care burden of younger generations with elderly patients; indeed, the number of caregivers in care facilities is already insufficient. Some research has studied the use of sensors to monitor the behavior of patients with dementia and other work has focused on processes for obtaining nursing skills, while still other studies have investigated the construction of robots that can communicate with people with dementia.
Aging people must maintain their physical and mental health as much as possible; indeed, extending “healthy life expectancy” has become a major national concern. Some studies have investigated sport training sessions for detecting a person’s mental health and have recommended physical activities based on biomedical instrumentation with computational intelligence.
The frequent natural disasters in recent years also indicate a need for strategies that will allow rapid and reliable evacuation of people requiring assistance to safer areas during disasters. Some investigations have focused on safe refuge courses, prediction of river flooding, and prioritizing evacuation tasks.
One of the main objectives of this workshop is to consider the use of evolutionary/genetic algorithms in the health care and nursing systems. We are interested in technologies that motivate people to act, especially elderly persons, caregivers, and people affected by disasters, and technologies for the prevention of disasters and diseases. We also intend to hold an interactive demo session about health care and nursing systems or in connection with disasters.

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).

Evolutionary Data Mining and Optimization over Graphs (EVOGRAPH)

http://evograph.jrlab.science/

Summary

Nowadays complex paradigms arising from diverse domains can be modelled as a graph. Application scenarios of this modelling paradigm abound in practice in transportation, mobility, logistics, social networks, chemistry, bioinformatics and Internet of Things, among others. In such graphs nodes and links represent variables defined for the application scenario at hand, over which different problems can be formulated depending on the information to be inferred from the network, ranging from graph clustering to graph classification, motif discovery and frequent subgraph mining, among many others.
While existing techniques for each of such tasks are manifold, the community has lately shifted its focus on the use of Evolutionary Computation (EC) and Swarm Intelligence (SI) as efficient algorithmic means to undertake new formulations of the aforementioned tasks and/or to deal with graph instances of unprecedented complexity. Community detection (clustering) over graphs is arguably one of the problems best exemplifying the upsurge of EC and SI to cope with their increasingly complex nature.
The EVOGRAPH workshop, to be held during GECCO 2019, aims at fostering and exchanging rich discussions around the latest findings, research achievements and novel ideas in the areas of data mining and optimization over graphs tackled under the EC/SI umbrella. Interested colleagues are invited to submit contributions via the submission system (https://gecco-2019.sigevo.org/) , with an emphasis on (but not limited to) the following topics:

  • Community partition (graph clustering) problems
  • Graph coloring problems
  • Graph packing problems
  • Vertex cover problems
  • Tree/Subgraph induction problems
  • Graph classification problems
  • Graph construction problems
  • Spread epidemics over graphs
  • Routing over graphs
  • New approaches for graph embedding/representation
  • Applications of Social Networks and graphs (in Social Networks, Transport, Logistics, Cyber bullying,
  • Terrorism detection, Bioinformatics, Energy, etc).

Only submissions with original contributions with respect to the state of the art related to the above areas will be considered for its inclusion in this session, i.e. workshop papers will be treated under the same criteria as regular conference papers. Oral presentation at the conference will be mandatory for accepted papers to be included in the proceedings.

Biographies

Eneko Osaba

Eneko Osaba works at TECNALIA as researcher in the ICT/OPTIMA area. He obtained his Ph.D. degree on Artificial Intelligence (Cum Laude) in 2015 in the University of Deusto. He has participated in the proposal, development and justification of more than 15 research projects.

He has contributed in the development of more than 80 papers, including more than 15 Q1. He has performed several stays in universities of United Kingdom, Italy and Malta. He served as a member of the program and/or organizing committee in more than 20 international conferences. At HAIS 2015 and IDC 2018, Eneko was also member of the organizing committee, and he organized several special sessions in conferences such CEC 2017, IDC 2018, IDEAL 2018 and PAAMS 2018. Besides this, he is member of the editorial board of International Journal of Artificial Intelligence and Journal of Advanced Transportation. Furthermore, he has acted as guess editor in journals such as Journal of Computational Science, Neurocomputing, Logic Journal of IGPL and IEEE ITS Magazine.

Javier Del Ser Lorente

Javier (Javi) Del Ser joined the Faculty of Engineering of the University of the Basque Country (Spain) to study Electrical Engineering, obtaining his combined B.S. and M.S. degree in May 2003. After finishing this degree, he became a recipient of the Fundacion de Centros Tecnologicos Inaki Goenaga doctoral grant. He received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude, Extraordinary Prize) from the University of Alcala, Spain, in 2013. Currently he is a Research Professor in Data Science and Optimization at TECNALIA (Spain), a visiting fellow at the Basque Centre for Applied Mathematics (BCAM) and an adjunct lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 260 scientific articles, co-supervised 8 Ph.D. theses (+ 7 ongoing), edited 6 books, co-authored 7 patents and participated/led more than 45 research projects. He has also been involved in the organization of various national and international conferences. He is a Senior Member of the IEEE, and a recipient of the Bizkaia Talent prize for his research career.

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.

Game-Benchmark for Evolutionary Algorithms (GBEA)

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

Summary

Games are a very interesting topic that motivates a lot of research. For instance, in several keynotes (e.g. IJCCI’15, EMO’17), blogs and papers, games have been suggested repeatedly as testbeds for artificial intelligence (AI) research. The most commonly cited reasons for these suggestions ​ are based on key features of games, such as controllability, safety and repeatability. Also, their ability to simulate properties of real-world problems, such as noisy measurements, uncertainty and the existence of multiple objectives, are frequently mentioned reasons. A key advantage of games (when compared to other simulations) is that the application itself can be used as a test framework, which in turn neglects the need for modelling the problem.

The same arguments for researching AI algorithms on games can be applied to the idea of games as benchmarks for genetic and evolutionary algorithms (EAs). However, despite the existence of the DETA track at GECCO conferences, along with its many computational intelligence in games (CIG) researchers, there has been no concerted effort of these two communities 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.

With the second iteration of the GBEA workshop, we plan to extend the existing benchmark by adding new problems and computing new results (e.g. using surrogate-assisted evolutionary algorithms which should be well-suited to the problems). Furthermore, we want to advance our understanding of the problems and of the behaviour of the algorithms. Lastly, we hope to garner more widespread interest in GBEA, as well as related benchmarks, and encourage more discussions on how to improve them.

Biographies

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".

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.

Tea Tušar

Tea Tusar is a research fellow at the Department of Intelligent Systems of the Jozef Stefan Institute in Ljubljana, Slovenia. She was awarded the PhD degree in Information and Communication Technologies by the Jozef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has 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.

Vanessa Volz

Vanessa Volz is a post-doctoral research associate at Queen Mary University London, UK, with focus in computational intelligence in games. She received her PhD in 2019 from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. 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. 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.

Genetic and Evolutionary Computation in Defense, Security, and Risk Management (SecDef)

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 five 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

Anna I Esparcia-Alcázar

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

 

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).

Erik Hemberg

Erik Hemberg is a Research Scientist in the AnyScale Learning
For All(ALFA) group at Massachusetts Institute of Technology Computer Science and Artificial Intelligence Lab, USA. He has a PhD in Computer Science from University College Dublin, Ireland and a MSc in Industrial Engineering and Applied Mathematics from Chalmers University of Technology, Sweden. He has 10 years of experience in EC focusing on the use of programs with grammatical representations, estimation of distribution and coevolution. His work has been applied to networks, tax avoidance, and Cyber Security.

 

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)

www.geneticimprovementofsoftware.com

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 big success, with over 40 attendees, receiving 16 submissions and incorporating a lively and packed schedule. Feedback from the post-workshop surveys was overwhelmingly positive.

This event was repeated in 2016, 2017 with strong participation from the community and a full schedule in each. In 2018 two GI workshops were held (the 4th and 5th). The 4th GI workshop was held as part of the International Conference on Software Engineering, whilst the 5th continued to promote GI to the evolutionary computing at GECCO 2018 in Japan. It is hoped to continue both lines and that the 6th international GI workshop will be held at ICSE 2019 in May and we hope to hold the 7th at GECCO in July. The GI@GECCO workshop continues to fulfill a key role in promoting new work, building community for new researchers, and setting new directions in Genetic Improvement. Feedback from all of these events has been strongly positive.

To build on the success of these past events we propose a seventh event to be held at GECCO 2019. The focus of this event will be on the presentation of new work of high quality work and the development of joint proposals for directions of research with high potential. We expect this workshop to attract participants from the key groups of the field. Our intention is to focus on quality so we propose an intensive single session workshop to showcase the four best new works in this field.

A particular focus of the workshop will be on research that helps to eliminate barriers to deployment of GI in real-world and industrial settings. There has been a surge of publications in the past two years focused, not only on scaling GI to applications of industrial scale but also in successfully addressing some of the challenges of integrating the products of GI into workflows in software engineering environments. The workshop aims to provide a venue for the most recent work in this area as well as providing a venue for the community to identify new challenges and directions for future work.

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 senior research associate at Lancaster University. 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 is head of the Operational Research Group (http://or.qmul.ac.uk/) at QMUL. 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, Artificial Intelligence/Machine Learning and in particular Genetic Programming. Publications are at (https://scholar.google.co.uk/citations?user=iZIjJ80AAAAJ&hl=en), and current EPSRC grants are at (https://gow.epsrc.ukri.org/NGBOViewPerson.aspx?PersonId=-485755). Public engagement articles are at (https://theconversation.com/profiles/john-r-woodward-173210/articles). He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities (Birmingham, Nottingham, Stirling).

Industrial Applications of Metaheuristics (IAM)

Summary

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

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

Topic areas of IAM 2019 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.

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 Department. 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 analytics, metaheuristics and swarm intelligence, and he has broad experience using these kind of techniques in industrial environment to optimize production processes. His paper ‘Scheduling a Galvanizing Line by Ant Colony Optimization‘ obtained the best paper award in the ANTS conference in 2014.

Pablo Valledor Pellicer

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

Thomas Stützle

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

Interactive Methods @ GECCO (iGECCO)

Summary

As nature-inspired methods have evolved, it has become clear that optimising towards a quantified fitness function is not always feasible, particularly where part or all of the evaluation of a candidate solution is inherently subjective. This is particularly the case when applying search algorithms to problems such as the generation of art and music. In other cases, optimising to a fitness function might result in a highly optimal solution that is not well suited to implementation in the real world. Incorporating a human into the optimisation process can yield useful results in both examples, and as such the work on interactive evolutionary algorithms (IEAs) has matured in recent years. This proposed workshop will provide an outlet for this research for the GECCO audience. Particular topics of interest are:

* Interactive generation of solutions.
* Interactive evaluation of solutions.
* Psychological aspects of IEAs.
* Multi- and many-objective optimisation with IEAs.
* Machine learning approaches within IEAs.
* Novel applications of IEAs.

Most IEAs focus on either asking the user to generate solutions to a problem with which they are interacting, or asking them to evaluate solutions that have been generated by an evolutionary process. To enable users to generate solutions it is necessary to develop mechanisms by which they can interact with a given solution representation. Solution evaluation requires the display of the solution (e.g., with a visualisation of the chromosome) so that the user can choose between two or more solutions having identified characteristics that best suit them.

As well as the basic interaction and solution evaluation, IEAs bring with them additional considerations through the inclusion of the user. A prime example of such a consideration is "user fatigue". The many iterations required by most nature-inspired methods can equate to a very large number of interactions between the user and system. Over many repeated interactions the user can become fatigued, so methods aimed at addressing this (and other similar effects) are of great importance to the future development of IEAs.

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.

 

Matt Johns

Dr Matt Johns is a Research Fellow in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter. He obtained a PhD in Computer Science from the University of Exeter developing methods for incorporating domain expertise into evolutionary algorithms. Following the submission of his PhD thesis, he worked as an Associate Research Fellow in the Centre for Water Systems developing decision support tools to aid in the optimal design of waste water treatment systems. He then went on to work on the Human-Computer Optimisation for Water Systems Planning and Management project, developing new approaches to the design and management of water systems by incorporating visual analytics, heuristic optimisation and machine learning. His research interests include evolutionary optimisation, water systems optimisation, human-computer interaction and interactive visualisation.

 

Nick Ross

Nick Ross is a Computer Science PhD student in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter. His interests lie in nature-inspired computation, gamification, and artificial intelligence.

Ed Keedwell

Prof. Keedwell is an Associate Professor and Director of Research for Computer Science. He has personal research interests in nature-inspired computing techniques (e.g. genetic algorithms, neural networks, cellular automata) and hyper-heuristics, exploring their application to a variety of difficult problems in bioinformatics and engineering. He leads the Nature Inspired computing research group focusing on applied optimisation and has been involved with successful funding applications totalling over £2 million from the EPSRC, Innovate UK, EU and industry.

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 IF(condition)-THEN(action) rules, so-called classifiers, which partition the problem space into smaller subspaces. Thereby, each of these elements encoding the system’s knowledge can either be represented by rather straight-forward schemes such as IF-THEN rules, or be realized by more complex models such as Artificial Neural Networks. Accordingly, LCSs are also 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 tension was formalized as the “Generalization Hypothesis” by Stewart Wilson in 1995 when he presented todays 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 an LCS? For example, Radial Basis Function Interpolation and Approximation Networks, Multi-Layer Perceptrons (MLP), as well as 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, are now becoming of high interest to other machine learning communities (“Explainable AI”). 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 modeling (e.g. ANN, GP, SVM, RBFs,…)
  • New means for the partitioning of the problem space (condition structures, ensemble formation, …)
  • New ways of classifier mixing (combination of local predictions, ensemble voting schemes,…)
  • Evolutionary Reinforcement Learning (Multi-step LCS, Neuroevolution, …)
  • Theoretical developments in LCS (provably optimal parametrization, 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 such as Attribute Tracking or Feature Dependency Trees …)
  • System enhancements (competent operators, problem structure identification and linkage learning, ...)
  • Input encoding / 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, bio-informatics, intelligence in games, ...)
  • Optimizations and parallel implementations (GPU acceleration, matching algorithms, …)
  • Similar Evolutionary Rule-Based ML systems (Artificial Immune Systems, Evolving FRI Systems, …)

Biographies

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 Ph.D. candidate at the Department of Computer Science of the University of Augsburg, Germany. He received his B.Sc. in Business Information Systems from the University of Applied Sciences in Augsburg in 2012. He then switched to the University of Augsburg to proceed with his master's degree (M.Sc.) 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 and contributor of ongoing research in this field. Besides his position in the Organic Computing Group at the University of Augsburg, he currently finishes his Ph.D. in computer science. His research focuses on the applicability of EML techniques in self-learning adaptive systems which are asked to act in real world environments that exhibit challenges such as data imbalance and non-stationarity. Therefore, in his work he investigates the utilization of interpolation and active learning methods to change the means of how classifiers are initialized, insufficiently covered problem space niches are filled, or adequate actions are selected. A further aspect he investigates is the question how Learning Classifier Systems can be enhanced toward a behavior of proactive knowledge construction.
Since 2017, he is an organizing committee member of the International Workshop on Learning Classifier Systems (IWLCS). For the third year now, he also co-organizes the Workshop Series on Autonomously Learning and Optimizing Systems (SAOS). Among others, he serves as reviewer for GECCO’s EML track, ACM's Transactions on Autonomous and Adapative Systems (TAAS) Journal, and several workshops.

Takato Tatsumi

Takato Tatsumi is a Ph.D. student in the University of Electro-Communications, Japan and the research fellow of Japan Society for the promotion of Science, Japan. He received B.Sc. and M.Sc. degrees in informatics from the University of Electro-Communications, Japan, in 2015 and 2017, respectively. His research interests are in data mining, evolutionary computation, reinforcement learning, more specifically, in learning classifier systems. He has authored more than 7 peer-reviewed LCS research papers, some of them in journals and conferences such as GECCO, CEC, and IWLCS. He received GECCO 2016 Student Travel Grant and Excellent Paper Award from the Japanese Symposium of Informatics (SSI) in 2017.

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.

Biographies

Stephen Smith

Stephen Smith is a full professor in the Department of Electronic Engineering at the University of York, UK. He received a BSc in Computer Science, an MSc in Electronics and a PhD in Electronic Engineering, all from the University of Kent, UK. Stephen's research uses genetic programming (a representation of Cartesian Genetic Programming) in the diagnosis and monitoring of Parkinson's disease, Alzheimer's disease and other neurodegenerative conditions. He has applied this work to clinical studies in the UK, USA, UAE, Australia, China and Singapore. The resulting technology is protected under 12 patent applications, of which 7 have been granted. A spinout company, ClearSky Medical Diagnostics (www.clearskymd.com), is marketing medical devices based on the technology and a recent co-authored publication detailing the clinical efficacy of this work won the Gold Humies award at GECCO 2018.

Stephen is co-founder and organizer of the MedGEC Workshop, which is now in its fourteenth year. He is also an associate editor for the journal Genetic Programming (Springer) and co-editor of a book on Medical Applications of Genetic and Evolutionary Computation (John Wiley, November 2010). Stephen has some 100 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.

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.

Real-world Applications of Continuous and Mixed-integer Optimization (RWACMO)

http://www.ifs.tohoku.ac.jp/edge/gecco2019-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.

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.
  • Real-world optimization/design under the presence of uncertainties.
  • Known issues and challenges in real-world implementations and how to tackle them.
  • Review paper on the applications of EC/related techniques in a specific discipline.
  • Competitiveness and disadvantages of EC/related techniques compared to other techniques such as gradient-based methods.
  • Comparison and performance assessment of various EC/related techniques for solving real-world problems.

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 lecturer at Faculty of Mechanical and Aerospace Engineering, Bandung Institute of Technology, Indonesia. Previously, he was a research fellow at Tohoku University, Japan, from 2016-2018. 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 include aerodynamic design optimization, surrogate-assisted optimization, and uncertainty quantification. He has published several journal and conference papers on the development and application of evolutionary computations and surrogate models in the field of aerospace and biomedical engineering

Surrogate-Assisted Evolutionary Optimisation (SAEOpt)

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 Lecturer in Computer Science at the University of Plymouth. He has a BEng in Electronic Engineering from the University of Southampton, and a PhD in Computer Science from the University of Exeter. He has been a product development engineer before starting his PhD, and has held post-doctoral research positions at the University of Exeter before starting his role in Plymouth. 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 computationally expensive 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 an Associate Professor in Computational Intelligence 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 theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). 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 PhD 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. He was a Finland Distinguished Professor and Changjiang Distinguished Professor. His research interests include data-driven evolutionary optimization, Bayesian optimization, secure and interpretable machine learning, evolutionary multi-objective learning, evolutionary developmental systems, and neural plasticity.

He is the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems and the Co-Editor-in-Chief of Complex & Intelligent Systems. He is also an Associate Editor of the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, and IEEE Transactions on NanoBioscience. He is an Editorial Board Member of Evolutionary Computation. He is an Invited Plenary / Keynote Speaker at over 30 international conferences. He is the recipient of several awards including the 2014 and 2016 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an IEEE Distinguished Lecturer.

Dr Jin is a Fellow of IEEE.

Understanding Machine Learning Optimization Problems (UMLOP)

http://erc.is/go/gecco2019

Summary

Over the years, researchers have developed a plethora of optimization algorithms and investigative approaches for dealing with continuous optimization problems, but usually tested them on purely artificial problems such as the well-known BBOB or CEC test suites.
Yet, the machine learning (ML) community actually possesses many challenging practical optimization problems. Unfortunately, there exists little knowledge exchange between the two communities (EC and ML). Recent work shows how ML methods can actually benefit from incorporating EC strategies - see for instance https://arxiv.org/abs/1802.08842, https://arxiv.org/abs/1806.05695 or https://blog.openai.com/evolution-strategies/ for some examples.
The aim of this workshop is to gain further insights into common ML problems, with the overall goal of improving the understanding of their specific structure. What do their landscapes look like? Which ML problems are of benign/malign nature for EC approaches (i.e., when should one consider incorporating EC into ML methods)? How similar are such problems to instances from popular EC benchmark suites? In order to facilitate first analyses, we provide an initial set of ML problems (related to real-world applications) and invite participants to contribute their insights into these problems. Possible approaches could consist of - but are definitely not limited to - applying exploratory landscape analysis, analyzing algorithm performances (with a focus on "why"), or investigating already existing problem solutions w.r.t. quality and/or diversity aspects.

Biographies

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".

Marcus Gallagher

Dr Marcus Gallagher is an Associate Professor in the School of Information Technology and Electrical Engineering at the University of Queensland, Brisbane Australia. He was awarded his PhD from the University of Queensland in 2000. His main research interests are in the areas of Optimization, Metaheuristics, Evolutionary Computation and Machine Learning. More specifically, he is interested in the intersections between these areas, including the use of probabilistic models in black-box optimization algorithms (e.g. Estimation of Distribution Algorithms) and using machine learning and data-driven techniques to better understand the nature of optimization problems and algorithms.

Mike Preuss

Mike Preuss is Research Associate at ERCIS, University of Muenster, Germany. Previously, he was with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization. He is also active in computational intelligence methods for computer games, especially in procedural content generation (PGC) and realtime strategy games (RTS).

Olivier Teytaud

Olivier Teytaud (https://www.lri.fr/~teytaud/) is research scientist at Facebook. He has been working in numerical optimization in many real-world contexts - scheduling in power systems, in water management, hyperparameter optimization for computer vision and natural language processing, parameter optimization in reinforcement learning. He is currently maintainer of the open source derivative free optimization platform of Facebook AI Research (https://github.com/facebookresearch/nevergrad), containing various flavors of evolution strategies, Bayesian optimization, sequential quadratic programming, Cobyla, Nelder-Mead, differential evolution, particle swarm optimization, and a platform of testbeds including games, reinforcement learning, hyperparameter tuning and real-world engineering problems.

Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2019)

Summary

Building on workshops held annually since 2010, the tenth annual workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC), to be held at GECCO 2019 in Prague, 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, permitting observation and interception of undesirable traits such as premature convergence and population stagnation. In addition, 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 an Associate Professor in Computational Intelligence 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 theoretical work includes algorithm development and analysis, along with data structures required for efficient multi-objective optimisation and Pareto set maintenance. His applied work includes costly and uncertain industrial design problems, air traffic control safety systems, automating biological experiments and robust multi-objective routing.

He has previous been a workshop organiser at GECCO for VizGEC (Visualisation Methods in Genetic and Evolutionary Computation), SAEOpt (Surrogate-Assisted Evolutionary Optimisation) and EAPU (Evolutionary Algorithms for Problems with Uncertainty). He has been active within the evolutionary computation community as a reviewer and program committee member since 2003.

Workshop on Evolutionary Algorithms for Smart Grids (SmartEA)

Summary

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

Scope and Topics

Following the success of the previous edition at GECCO2017 “Workshop on Evolutionary Algorithms for Smart Grids (SmartEA)” (http://ci4energy.uni-paderborn.de/smartEA), the main goal of this workshop is to promote the research on evolutionary algorithms in smart grids. We are seeking

  • Energy generation and load forecasting
  • Demand side and smart home energy management
  • Network Restoration
  • Real-time control and optimization
  • Smart micro-grids

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

Biographies

Fernando Lezama

Fernando Lezama received an M.Sc. degree (with Honors) in Electronic Engineering (2011), and a Ph.D. in ITCs (2014) both from the Monterrey Institute of Technology and Higher Education (ITESM), Mexico. Currently, he is a researcher at GECAD, Polytechnic of Porto, Portugal, where he works in the development of intelligent systems for optimization in smart grids. His research interests include computational intelligence, evolutionary computation, and optimization of smart grids and optical networks.

Joao Soares

João Soares has a BSc in computer science and a master in Electrical Engineering in Portugal, namely Polytechnic of Porto. He attained his PhD degree in Electrical and Computer Engineering at UTAD university. He his a researcher at GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development. His research interests include optimization in power and energy systems, including heuristic, hybrid and classical optimization.

Paul Kaufmann

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

Zita Vale

Zita Vale is full professor at the Polytechnic Institute of Porto and the director of the Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD). She received her diploma in Electrical Engineering in 1986 and her PhD in 1993, both from University of Porto. Zita Vale works in the area of Power and Energy Systems, with special interest in the application of Artificial Intelligence techniques. She has been involved in more than 50 funded projects related to the development and use of Knowledge-Based systems, Multi-Agent systems, Genetic Algorithms, Neural networks, Particle Swarm Intelligence, Constraint Logic Programming and Data Mining.
She published over 800 works, including more than 100 papers in international scientific journals, and more than 500 papers in international scientific conferences.