List of Workshops
| Title | Organizers |
|---|---|
| AABOH — Analysing Algorithmic Behaviour of Optimisation Heuristics |
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| BBOB — Black Box Optimization Benchmarking |
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| BENCHMARKING — Benchmarking and Reproducibility/Replicability |
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| DTEO — 4th GECCO Workshop on Decomposition Techniques in Evolutionary Optimization |
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| EAHPC — Evolutionary Algorithms and HPC |
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| EAPWU — Evolutionary Algorithms for Problems with Uncertainty |
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| EC+DM — Evolutionary Computation and Decision Making |
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| ECADA — 11th Workshop on Evolutionary Computation for the Automated Design of Algorithms |
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| ECPERM — Evolutionary Computation for Permutation Problems |
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| EVOGRAPH — 2nd Workshop on Evolutionary Data Mining and Optimization over Graphs |
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| EVORL — Evolutionary Reinforcement Learning Workshop |
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| EVOSOFT — Evolutionary Computation Software Systems |
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| IAM — 6th Workshop on Industrial Applications of Metaheuristics |
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| IWLCS — 24th International Workshop on Learning Classifier Systems |
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| LAHS — Landscape-Aware Heuristic Search |
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| NEWK — Neuroevolution at Work |
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| PDEIM — Parallel and Distributed Evolutionary Inspired Methods |
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| RWACMO — Real-World Applications of Continuous and Mixed-integer Optimization |
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| SAEOPT — Surrogate-Assisted Evolutionary Optimisation |
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| SECDEF — Genetic and Evolutionary Computation in Defense, Security, and Risk Management |
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| SWINGA — Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges |
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| VIZGEC — Visualisation Methods in Genetic and Evolutionary Computation |
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AABOH — Analysing Algorithmic Behaviour of Optimisation Heuristics
http://iao.hfuu.edu.cn/aaboh21
Summary
Optimisation and Machine Learning tools are among the most used tools in the modern world with its omnipresent computing devices. Yet, the dynamics of these particular tools has not been analysed in detail. This is largely attributed to the complexity of the underlying processes that cannot be subject to complete theoretical analysis. More and more, carefully designed experiments or heavy data-based approaches come to help in analysing the dynamics and quantifying the primary algorithmic behaviour such as:
–global search vs. local search,
–exploration vs. exploitation,
–time and space complexity,
–premature convergence,
–stagnation,
–structural bias,
–solution diversity (genotypic or phenotypic),
–robustness of the solution produced by an algorithm
–anytime performance.
Typically, researchers and practitioners look only at the final result produced by these methods. Meanwhile, the vast amount of information collected over the run(s) is wasted. Yet, it is becoming more evident that such information can be useful if some design principles are defined that allow online or offline analysis of the processes taking place in the population and their dynamics. Meanwhile, the importance of looking at anytime performance lies in the empirical analysis of optimization algorithms, where often the performance profiles of twoalgorithms cross each other and hence we should be able to identify the best working scenario for each algorithm using anytime performance analysis. Thus, both theoretical and empirical contributions in the following directions are invited to this workshop:
–identify the desired features of optimisation and machine learning algorithms and suggest novel methods to establish such features;
–explain why a particular feature is important and suggest methods for quantifying it;
–investigate whether transitions in behaviour can be detected to establish whether any benefits are practically possible for any-time performance estimation based on features?
Organizers
Anna V Kononova
Anna V. Kononova is currently an Assistant Professor at the Leiden Institute of Advanced Computer Science. She received her MSc degree in Applied Mathematics from Yaroslavl State University (Russia) in 2004 and her PhD degree in Computer Science from University of Leeds in 2010. After 5 years of postdoctoral experiences at Technical University Eindhoven, the Netherlands and Heriot-Watt University, UK, Anna has spent a number of years working as a mathematician in industry. Her current research interests include analysis of optimisation algorithms.
Hao Wang
Hao Wang obtained his PhD (cum laude, promotor: Prof. Thomas Bäck) from Leiden University in 2018. He is currently employed as an assistant professor of computer science at Leiden University. Previously, he has a research stay at Sorbonne University, France (supervised by Carola Doerr). He received the Best Paper Award at the PPSN2016conference and was a best paper award finalist at the IEEE SMC2017 conference. His research interests are in the analysis and improvement of efficient global optimization for mixed-continuous search spaces, Evolution strategies, Bayesian optimization, and benchmarking.
Thomas Weise
Thomas Weise obtained the MSc in Computer Science in2005from the Chemnitz University of Technology and his PhD from the University of Kassel in2009. 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 IntelligentComputation 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 ComputerScience and Technology. Prof. Weise has more than seven years of experience as a fulltime 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 than730times. He has acted as reviewer, editor, or programme committee member at 70 different venues.
Carola Doerr
Carola Doerr, formerly Winzen, is a permanent CNRS researcher at Sorbonne University in Paris, France. Carola's main research activities are in the mathematical analysis of randomized algorithms, with a strong focus on evolutionary algorithms and other sampling-based optimization heuristics. Carola 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 associate editor of ACM Transactions on Evolutionary Learning and Optimization, editorial board member of the Evolutionary Computation journal, advisory board member of the Springer Natural Computing Book Series. She was program chair of PPSN 2020, FOGA 2019, and the theory tracks of GECCO 2015 and 2017. Carola was guest 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)''. She is a founding and coordinating member of the \href{https://sites.google.com/view/benchmarking-network/}{Benchmarking Network}, an initiative created to consolidate and to stimulate activities on benchmarking sampling-based optimization heuristics, she has organized several workshops on benchmarking, and was co-organizer of the \href{https://facebookresearch.github.io/nevergrad/opencompetition2020.html}{Open Optimization Competition 2020}.
Thomas Bäck
Thomas Bäck is professor of Computer Science at the Leiden Institute of Advanced Computer Science, Leiden University, Netherlands, since 2002. He received his PhD in Computer Science from Dortmund University, Germany, in 1994, and was leader of the Center for Applied Systems Analysis at the Informatik Centrum Dortmund until 2000. Until 2009, Thomas was also CTO of NuTech Solutions, Inc.~(Charlotte, NC), where he gained ample experience in solving real-world problems in optimization and data analytics, by working with global enterprises in the automotive and other industry sectors. Thomas received the IEEE Computational Intelligence Society (CIS) Evolutionary Computation Pioneer Award for his contributions in synthesizing evolutionary computation (2015), was elected as a fellow of the International Society of Genetic and Evolutionary Computation (ISGEC) for fundamental contributions to the field (2003), and received the best dissertation award from the ""Gesellschaft für Informatik"" in 1995. Thomas has more than 300 publications, as well as two books on evolutionary algorithms: Evolutionary Algorithms in Theory and Practice (1996), Contemporary Evolution Strategies (2013). He is co-editor of the Handbook of Evolutionary Computation and the Handbook of Natural Computing.
Fabio Caraffini
Fabio Caraffini is currently an Associate Professor in Computer Science and Mathematics at De Montfort University (UK) and a Fellow of the Higher Education Academy (UK). He received the BSc in ""Electronics Engineering and the MSc in ""Telecommunications Engineering degrees from the University of Perugia (Italy) in 2008 and 2011 respectively. Fabio holds a PhD in Computer Science (De Montfort University, UK, 2014) and a PhD in Computational Mathematics (University of Jyväskylä, Finland, 2016). His research interests include theoretical and applied computational intelligence with a strong emphasis on metaheuristics for optimisation.
Johann Dreo
Johann Dreo works at the Pasteur Institute and CNRS, Computational Biology Departement, USR 3756, System Biology Group. His scientific interests are in optimization, search heuristics, artificial intelligence, machine learning, algorithm design and engineering, automated planning, and differential geometry. He has more than 13 years of expertise of applying randomized optimization heuristics in practice.
BBOB — Black Box Optimization Benchmarking
http://numbbo.github.io/workshops/
Summary
Benchmarking optimization algorithms is a crucial part in the design and application of them in practice. The Comparing Continuous Optimizers platform (COCO, https://github.com/numbbo/coco) has been developed in the past decade to support algorithm developers and practitioners alike by automating benchmarking experiments for blackbox optimization algorithms in single- and bi-objective, unconstrained continuous problems in exact and noisy, as well as expensive and non-expensive scenarios.
For the year 2021 and the 10th edition of the Blackbox Optimization Benchmarking workshop at GECCO (1 workshop was held at CEC), we plan to widen our focus towards mixed-integer benchmark problems. Concretely, we highly encourage submissions describing the benchmarking results from blackbox optimization algorithms on the single-objective bbob-mixint and the bi-objective bbob-biobj-mixint suites previously released at GECCO-2019.
Any other submission discussing other aspects of (blackbox) benchmarking, especially on the other available bbob, bbob-noisy, bbob-biobj, and bbob-largescale test suites are welcome as well. We encourage particularly submissions about algorithms from outside the evolutionary computation community and papers analyzing the large amount of already publicly available algorithm data of COCO (see https://coco.gforge.inria.fr/doku.php?id=algorithms).
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 test suites mentioned. Postprocessing data and comparing algorithm performance will be equally automatized with COCO (up to already prepared ACM-compliant LaTeX templates for writing papers).
For details, please see the separate BBOB-2021 web page at http://numbbo.github.io/workshops/BBOB-2021/.
Organizers
Anne Auger
Anne Auger is a research director at Inria heading the RandOpt team. She received her diploma (2001) and PhD (2004) in mathematics from the Paris VI University.
Before joining Inria, she worked for two years (2004-2006) at ETH in Zurich.
Her main research interest is stochastic continuous optimization including
theoretical aspects, algorithm designs and benchmarking. She is a member of ACM-SIGECO executive committee and of the editorial board of Evolutionary Computation. She has been General chair of GECCO in 2019. She is co-organizing the forthcoming Dagstuhl seminar on benchmarking and one of the core members behind the COCO/BBOB benchmarking platform.
Peter A. N. Bosman
Peter A. N. Bosman is a senior researcher in the Life Sciences research group at the Centrum Wiskunde & Informatica (CWI) (Centre for Mathematics and Computer Science) located in Amsterdam, the Netherlands. Peter was formerly affiliated with the Department of Information and Computing Sciences at Utrecht University, where also he obtained both his MSc and PhD degrees in Computer Science, more specifically on the design and application of estimation-of-distribution algorithms (EDAs). He has (co-)authored over 125 refereed publications on both algorithmic design aspects and real-world applications of evolutionary algorithms. At the GECCO conference, Peter has previously been track (co-)chair (EDA track, 2006, 2009), late-breaking-papers chair (2007), (co-)workshop organizer (OBUPM workshop, 2006; EvoDOP workshop, 2007; GreenGEC workshop, 2012-2014), (co-)local chair (2013) and general chair (2017).
Tobias Glasmachers
Tobias Glasmachers is a professor at the institute for neural computation at the Ruhr-university of Bochum, Germany. He received his PhD from the faculty of Mathematics of the same university in 2008. Afterwards he joined the Swiss AI lab IDSIA for two years, where he was involved in developing natural evolution strategies. In 2012 he returned to Bochum as a junior professor, and he was appointed full professor in 2018. His research interests are optimization and machine learning. In the context of evolutionary algorithms he is interested in algorithm design and analysis of evolution strategies for single- and multi-objective optimization.
Nikolaus Hansen
Nikolaus Hansen is a research director 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 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).
Petr Pošík
Petr Pošík received his Diploma degree in Technical Cybernetics in 2001 and Ph.D. in Artificial Intelligence and Biocybernetics in 2007, both from the Czech Technical University in Prague, Czech Republic. From 2001 to 2004 he also worked as statistician, analyst and lecturer for StatSoft, Czech Republic. Since 2005 he has worked as a researcher and lecturer at the Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University. Being on the boundary of optimization, statistics and machine learning, his research interests are aimed at improving the characteristics of evolutionary algorithms with techniques of statistical machine learning. He also serves as a reviewer for several journals and conferences in the evolutionary computation field.
Tea Tušar
Tea Tušar is a research associate at the Department of Intelligent Systems of the Jožef Stefan Institute, and an assistant professor at the Jožef Stefan International Postgraduate School, both in Ljubljana, Slovenia. After receiving the PhD degree in Information and Communication Technologies from the Jožef 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.
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. After two postdocs at Inria Saclay Ile-de-France (2009-2010) and at Ecole Polytechnique (2010-2011), he joined 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 one). 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.
BENCHMARKING — Benchmarking and Reproducibility/Replicability
https://sites.google.com/view/benchmarking-network/home/activities/gecco-2021-workshop
Summary
The core theme of this workshop is on benchmarking evolutionary computation methods and related sampling-based optimization heuristics. For 2021, we will have two sessions:
- A session on general aspects of benchmarking (as in previous editions, we will select 2-3 invited speakers and will leave ample time for actual discussion)
- A session on reproducibility (Call for papers: http://lopez-ibanez.eu/reproducibility-gecco/)
Session 1: General Aspects of Benchmarking Evolutionary Computation Methods.
Benchmarking plays a vital role for understanding performance and search behavior of sampling-based optimization techniques such as evolutionary algorithms. Even though benchmarking is a highly-researched topic within the evolutionary computation community, there are still a number of open questions and challenges that should be explored:
- most commonly-used benchmarks are too small and cover only a part of the problem space,
- benchmarks lack the complexity of real-world problems, making it difficult to transfer the learned knowledge to work in practice,
- we need to develop proper statistical analysis techniques that can be applied depending on the nature of the data,
- we need to develop user-friendly, openly accessible benchmarking software. This enables a culture of sharing resources to ensure reproducibility, and which helps to avoid common pitfalls in benchmarking optimization techniques. 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.
Scope of Session 2: Understanding reproducibility in Evolutionary Computation.
Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies has increased in recent years, following similar discussions in other scientific fields. In this workshop, we want to raise awareness of the reproducibility issue, shed light on the obstacles when trying to reproduce results, and discuss best practices in making results reproducible as well as reporting reproducibility results.
We invite submissions of papers repeating an empirical study published in a journal or conference proceedings, either by re-using, updating or reimplementing the necessary codes and datasets, irrespectively of whether this code was published in some form at the time or not.
Please find the complete call for papers here: http://lopez-ibanez.eu/reproducibility-gecco/
Organizers
Carola Doerr
Carola Doerr, formerly Winzen, is a permanent CNRS researcher at Sorbonne University in Paris, France. Carola'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 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/was program chair of PPSN 2020, FOGA 2019 and 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)''.
Jürgen Branke
Juergen Branke is Professor of Operational Research and Systems at Warwick Business School. He has been active in the area of evolutionary algorithms applied to problems involving uncertainty for over 20 years, including problems that are dynamically changing over time, problems where the evaluation is uncertain (as in simulation-based optimisation), the search for robust solutions or uncertainty about user preferences. Juergen has published over 180 peer-reviewed papers in international peer-reviewed journals and conferences.
He is Editor-in-Chief of ACM Transactions on Evolutionary Learning an Optimization, Area Editor of the Journal of Heuristics and the Journal on Multi-Criteria Decision Analysis, as well as Associate Editor of IEEE Transactions on Evolutionary Computation and the Evolutionary Computation Journal. He is also Vice-Chair of the ACM Special Interest Group on Evolutionary Computation (SIGEvo).
Tome Eftimov
Tome Eftimov is a researcher at the Computer Systems Department at the Jožef Stefan Institute, Ljubljana, Slovenia. He is a visiting assistant professor at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje. He was a postdoctoral research fellow at the Stanford University, USA, where he investigated biomedical relations outcomes by using AI methods. In addition, he was a research associate at the University of California, San Francisco, investigating AI methods for rheumatology concepts extraction from electronic health records. He obtained his PhD in Information and Communication Technologies (2018). His research interests include statistical data analysis, metaheuristics, natural language processing, representation learning, and machine learning. He has been involved in courses on probability and statistics, and statistical data analysis. The work related to Deep Statistical Comparison was presented as tutorial (i.e. IJCCI 2018, IEEE SSCI 2019, GECCO 2020, and PPSN 2020) or as invited lecture to several international conferences and universities. He is an organizer of several workshops related to AI at high-ranked international conferences. He is a coordinator of a national project “Mr-BEC: Modern approaches for benchmarking in evolutionary computation” and actively participates in European projects.
Pascal Kerschke
Pascal Kerschke is professor of Big Data Analytics in Transportation at TU Dresden, Germany. Until his appointment in 2021, he was a postdoctoral researcher at the University of Münster, Germany. Prior to that, he obtained academic degrees in Data Analysis and Management (B.Sc.) and Data Science (M.Sc.) from the TU Dortmund University, Germany, as well as in Information Systems (Ph.D.) from the University of Münster, Germany.
His research interests cover a wide range of topics in the context of benchmarking, data science, machine learning, and optimization. In particular, his research focuses on Automated Algorithm Selection, Exploratory Landscape Analysis, and continuous single- and multi-objective optimization. Moreover, he is the main developer of the related R-package flacco (https://flacco.shinyapps.io/flacco/), co-authored further R-packages such as smoof and moPLOT, co-organized numerous tutorials and workshops in the context of Exploratory Landscape Analysis and/or benchmarking, and is an active member of the Benchmarking Network (https://sites.google.com/view/benchmarking-network/) and the COSEAL group (http://coseal.net).
Manuel López-Ibáñez
Dr. López-Ibáñez is Senior Distinguished Researcher at the University of Málaga (Spain) and a Senior Lecturer (Associate Professor) in the Decision and Cognitive Sciences Research Centre at the Alliance Manchester Business School, University of Manchester, UK. He received the M.S. degree in computer science from the University of Granada, Granada, Spain, in 2004, and the Ph.D. degree from Edinburgh Napier University, U.K., in 2009. He has published 27 journal papers, 9 book chapters and 48 papers in peer-reviewed proceedings of international conferences on diverse areas such as evolutionary algorithms, ant colony optimization, multi-objective optimization, pump scheduling and various combinatorial optimization problems. His current research interests are experimental analysis and automatic design of stochastic optimization algorithms, for single and multi-objective optimization. He is the lead developer and current maintainer of the irace software package (http://iridia.ulb.ac.be/irace).
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.
DTEO — 4th GECCO Workshop on Decomposition Techniques in Evolutionary Optimization
https://sites.google.com/view/dteo/home?authuser=1
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 optimization and 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 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).
Organizers
Bilel Derbel
Bilel Derbel is an associate Professor (2007), having a research habilitation (2017), at the Department of Computer Science at the University of Lille, France,. He has a PhD in Computer Science (2006) from the University of Bordeaux (LaBRI, France). He was an assistant professor (2006) at the University of Aix-Marseille, France. He is deputy team leader of the BONUS ‘Big Optimisation aNd Ultra-Scale Computing’ research group at Inria Lille — Nord Europe, and CRIStAL, CNRS UMR 9189. He is a co-founder member of the LIA-MODO international laboratory between Shinshu Univ., Japan, and Univ. Lille, France. He has been a program committee member of evolutionary computing conferences such as GECCO, CEC, EvoOP, PPSN, etc. He is an associate editor of the IEEE Transactions on Systems Man and Cybernetics: Systems. He co-authored more than seventy scientific papers. He was awarded best paper awards in EMO'19, SEAL'17, ICDCN'11, and was nominated for the best paper award in EvoCOP'20, PPSN'18 and PPSN'14. His research topics are focused on the synergies between evolutionary algorithms, fitness landscape analysis and high-performance computing. His current interests are on the design and analysis of autonomous and distributed evolutionary algorithms for single- and multi-objective optimisation.
Ke Li
Ke Li is a Senior Lecturer (Associate Professor) in Computer Science 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. He is the founding chair of IEEE CIS Task Force on Decomposition-based Techniques in Evolutionary Computation. He currently serves as an associate editor of IEEE Transactions on Evolutionary Computation, International Journal of Machine Learning and Cybernetics and Complex \& Intelligent Systems. He served as a guest editor in Neurocomputing Journal and Multimedia Tools and Applications Journal. His current research interests include the evolutionary multi-objective optimization, automatic problem solving, machine learning and applications in water engineering and software engineering. Recently, he has been awarded a prestigious UKRI Future Leaders Fellowship.
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. 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, machine learning, complex systems, multiobjective optimization, multimodal optimization (niching), 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, a Program Co-Chair for IEEE CEC’2012, a General Chair for ACALCI’2017 and AI’17. 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.
EAHPC — Evolutionary Algorithms and HPC
https://piprrr.github.io/gecco_eahpc_workshop_site/2021/
Summary
Evolutionary algorithms (EAs) are well-suited for High Performance Computing (HPC) because fitness evaluations can be readily done in parallel. Consequently, EAs have gathered considerable attention for their ability to accelerate finding solutions for a variety of computationally expensive problem domains, including reinforcement learning, neural architecture search, and model calibration for complex simulations. However, use of HPC resources adds an implicit secondary objective of ensuring those resources are efficiently utilized. This means that practitioners have to make decisions regarding evolutionary algorithms tailored for maximum HPC resource use, as well as associated software and hardware support. New EA-oriented HPC benchmarks might also be needed to guide practitioners in making those decisions.
We are looking for papers on the following sub-topics to facilitate discussion:
∙ algorithmic — what novel EA variants best exploit HPC resources?
∙ benchmarks — are there HPC specific measures for EA performance?
∙ hardware — can we improve use of HPC hardware, such as GPUs?
∙ software — what EA software, or software development practices, best leverage HPC capabilities?
An example of an algorithm-oriented concept would be using an asynchronous steady-state evolutionary algorithm (ASEA) to efficiently use HPC resources by minimizing idle times between evaluations. That is, an ASEA maintains a pool of already evaluated individuals and concurrently updates that pool with newly evaluated individuals, and immediately assigns offspring to newly idle computational resources for evaluation. This concept of using an ASEA is generalizable in that it scales from modestly sized clusters to supercomputers. However, it is an open issue whether this approach can be refined to further improve HPC resource utilization.
Organizers
Mark Coletti
Mark Coletti is a research scientist with the Oak Ridge National Laboratory (ORNL), and he received his Ph.D. in Computer Science from George Mason University in 2014. His main research focus is improving understanding of evolutionary algorithms within HPC contexts, particularly in petascale and exascale environments. His technical background includes evolutionary computation, machine learning, agent-based modeling, software engineering, image processing, and geoinformatics.
Robert Patton
Robert M. Patton is a Distinguished Research Staff in the Computer Science and Mathematics Division at ORNL and the Team Lead for the Nature Inspired Machine Learning Team consisting of 12 research staff members within the Computational Data Analytics Group. He has over 15 years of professional experience in government research and development as well as being the principle investigator (PI) of more than $2 million in research funds. His research focuses on artificial intelligence, evolutionary algorithms, and machine learning as they apply to data analysis, information processing, and prediction. His work has achieved more than 90 publications, 4 patents, 3 software copyrights, 3 R&D 100 Awards, and 3 nominations for the Association of Computing Machinery Gordon Bell Award.
Catherine (Katie) Schuman
Catherine (Katie) Schuman is a research scientist at Oak Ridge National Laboratory (ORNL). She received her Ph.D. in Computer Science from the University of Tennessee in 2015, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. She is continuing her study of models and algorithms for neuromorphic computing at ORNL. Katie has an adjunct faculty appointment with the Department of Electrical Engineering and Computer Science at the University of Tennessee, where she co-leads the TENNLab neuromorphic computing research group. Katie has over 50 publications as well as six patents in the field of neuromorphic computing. Katie received the U.S. Department of Energy Early Career Award in 2019.
Eric “Siggy” Scott
Eric Scott is a Senior Artificial Intelligence Engineer at MITRE Corporation in Northern Virginia and a PhD candidate at George Mason University. His research focuses on heuristic optimization algorithms, transfer learning, and their applications to modeling problems in a variety of fields. He holds a double B.Sc. in Computer Science and Mathematics from Andrews University in Berrien Springs, Michigan, and a M.Sc. in Computer Science from George Mason University.
Kenneth De Jong
Kenneth A. De Jong received his Ph.D. in computer science from the University of Michigan in 1975. He is currently a Professor Emeritus of Computer Science at George Mason University and head of the Evolutionary Computation Laboratory. His research interests include genetic algorithms, evolutionary computation, machine learning, and complex adaptive systems. He is currently involved in research projects involving the development of new evolutionary algorithm (EA) theory, the use of EAs as high-performance optimization techniques, and the application of EAs to the problem of learning task programs in domains such as robot navigation. He is an active member of the Evolutionary Computation research community as the author of a wide variety of publications including a book on evolutionary computation, and has been involved in organizing many of the workshops and conferences in this area. He is the founding editor-in-chief of the journal Evolutionary Computation (MIT Press), and a member of the board of ACM SIGEVO. He is the recipient of an IEEE Pioneer award in the field of Evolutionary Computation and a lifetime achievement award from the Evolutionary Programming Society.
EAPWU — Evolutionary Algorithms for Problems with Uncertainty
http://eapu.ex.ac.uk/index.html
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 that 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
Organizers
Khulood Alyahya
Khulood Alyahya is a lecturer in the Computer Science department at the University of Exeter. Prior to that, she was a research fellow at the same department working on a multidisciplinary project addressing key challenges in optimisation problems in the field of Computational Systems Biology. She has a PhD degree in Computer Science and an MSc degree in Intelligent Systems Engineering from the University of Birmingham. Her main research interests include complex networks analysis, landscape analysis and optimisation under multiple sources of uncertainty with applications to medical and biological problems.
Tinkle Chugh
Dr Tinkle Chugh is a Lecturer in Computer Science at the University of Exeter. Between Feb 2018 and June 2020, he worked as a Postdoctoral Research Fellow in the BIG data methods for improving windstorm FOOTprint prediction (BigFoot) project funded by Natural Environment Research Council (NERC) UK. He is also a member of the expert network in the Constructing a Digital Environment programme funded by NERC UK. He obtained his PhD degree in Mathematical Information Technology in 2017 from the University of Jyväskylä, 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 the University of Surrey, UK. His research interests are machine learning, data-driven optimization, evolutionary computation and decision making.
Jürgen Branke
Juergen Branke is Professor of Operational Research and Systems at Warwick Business School. He has been active in the area of evolutionary algorithms applied to problems involving uncertainty for over 20 years, including problems that are dynamically changing over time, problems where the evaluation is uncertain (as in simulation-based optimisation), the search for robust solutions or uncertainty about user preferences. Juergen has published over 180 peer-reviewed papers in international peer-reviewed journals and conferences.
He is Editor-in-Chief of ACM Transactions on Evolutionary Learning an Optimization, Area Editor of the Journal of Heuristics and the Journal on Multi-Criteria Decision Analysis, as well as Associate Editor of IEEE Transactions on Evolutionary Computation and the Evolutionary Computation Journal. He is also Vice-Chair of the ACM Special Interest Group on Evolutionary Computation (SIGEvo).
Jonathan Fieldsend
Jonathan Fieldsend is Professor of 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 over 100 peer-reviewed publications in the evolutionary computation and machine learning domains, and has been working on uncertain problems in evolutionary computation since 2005. This strand of work has mainly been in multi-objective data-driven problems, although more recently has undertaken work on expensive uni-objective robust problems. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Force on Data-Driven Evolutionary Optimisation of Expensive Problems, and sits on the IEEE CIS Task Force on Multi-modal Optimisation and the IEEE CIS Task Force on Evolutionary Many-Objective Optimisation. Alongside EAPwU, he has been a co-organiser of the VizGEC and SAEOpt workshops at GECCO for a number of years.
EC+DM — Evolutionary Computation and Decision Making
https://blogs.exeter.ac.uk/ecmcdm/
Summary
Solving real-world optimisation problems typically involve an expert or decision-maker. Decision making 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 Evolutionary Computation (EC) and decision-making techniques for several applications. This workshop will showcase research that is both at the interface of EC and decision making.
The workshop on Evolutionary Computation and Decision Making to be held in GECCO 2021 aims to promote the research on theory and applications in the field. Topics of interest (but not limited to) include:
1. Interactive multiobjective optimization or decision-maker in the loop
2. Visualization
3. Aggregation/trade-off operators & algorithms
4. Fuzzy logic-based decision-making techniques
5. Bayesian and other decision-making techniques
6. Interactive multiobjective optimization for (computationally) expensive problems
7. Using surrogates (or metamodels) in decision making
8. Hybridization of EC and decision making
9. Scalability in EC and decision making
10. Decision making and machine learning
11. Decision making in Big data
12. Decision making in real-world applications
13. Use of psychological tools to aid the decision maker
Organizers
Tinkle Chugh
Dr Tinkle Chugh is a Lecturer in Computer Science at the University of Exeter. Between Feb 2018 and June 2020, he worked as a Postdoctoral Research Fellow in the BIG data methods for improving windstorm FOOTprint prediction (BigFoot) project funded by Natural Environment Research Council (NERC) UK. He is also a member of the expert network in the Constructing a Digital Environment programme funded by NERC UK. He obtained his PhD degree in Mathematical Information Technology in 2017 from the University of Jyväskylä, 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 the University of Surrey, UK. His research interests are machine learning, data-driven optimization, evolutionary computation and decision making.
Richard Allmendinger
Richard is Senior Lecturer in Data Science at The University of Manchester, and the Business Engagement Lead of Alliance Manchester Business School. Richard has a background in Business Engineering (Diplom, Karlsruhe Institute of Technology, Germany + Royal Melbourne Institute of Technology, Australia), Computer Science (PhD, The University of Manchester, UK), and Biochemical Engineering (Research Associate, University College London, UK).
Richard's research interests are in the field of data and decision science and in particular in the development and application of optimization, learning and analytics techniques to real-world problems arising in areas such as management, engineering, healthcare, sports, music, and forensics. Richard is known for his work on non-standard expensive optimization problems comprising, for example, heterogeneous objectives, ephemeral resource constraints, changing variables, and lethal optimization environments. Much of his research has been funded by grants from various UK funding bodies (e.g. Innovate UK, EPSRC, ESRC, ISCF) and industrial partners.
Richard is a 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, regularly contributing to conference/workshop organization and editorial duties of well-known journals in the field of Optimization and Machine Learning.
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 and decision making with an emphasis on interactive multiobjective optimization methods, data-driven decision making, computationally expensive problems, explainable/interpretable machine learning, and visualization aspects related to many-objective 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.
ECADA — 11th Workshop on Evolutionary Computation for the Automated Design of Algorithms
https://bonsai.auburn.edu/ecada/
Summary
Scope
The main objective of this workshop is to discuss hyper-heuristics and algorithm configuration methods for the automated generation and improvement of algorithms, with the goal of producing solutions (algorithms) that are applicable to multiple instances of a problem domain. The areas of application of these methods include optimization, data mining and machine learning.
Automatically generating and improving algorithms by means of other algorithms has been the goal of several research fields, including artificial intelligence in the early 1950s, genetic programming since the early 1990s, and more recently automated algorithm configuration and hyper-heuristics. The term hyper-heuristics generally describes meta-heuristics applied to a space of algorithms. While genetic programming has most famously been used to this end, other evolutionary algorithms and meta-heuristics have successfully been used to automatically design novel (components of) algorithms. Automated algorithm configuration grew from the necessity of tuning the parameter settings of meta-heuristics and it has produced several powerful (hyper-heuristic) methods capable of designing new algorithms by either selecting components from a flexible algorithmic framework or recombining them following a grammar description.
Although most evolutionary algorithms are designed to generate specific solutions to a given instance of a problem, one of the defining goals of hyper-heuristics is to produce solutions that solve more generic problems. For instance, while there are many examples of evolutionary algorithms for evolving classification models in data mining and machine learning, a genetic programming hyper-heuristic has been employed to create a generic classification algorithm which in turn generates a specific classification model for any given classification dataset, in any given application domain. In other words, the hyper-heuristic is operating at a higher level of abstraction compared to how most search methodologies are currently employed; i.e., it is searching the space of algorithms as opposed to directly searching in the problem solution space, raising the level of generality of the solutions produced by the hyper-heuristic evolutionary algorithm. In contrast to standard genetic programming, which attempts to build programs from scratch from a typically small set of atomic functions, hyper-heuristic methods specify an appropriate set of primitives (e.g., algorithmic components) and allow evolution to combine them in novel ways as appropriate for the targeted problem class. While this allows searches in constrained search spaces based on problem knowledge, it does not in any way limit the generality of this approach as the primitive set can be selected to be Turing-complete. Typically, however, the initial algorithmic primitive set is composed of primitive components of existing high-performing algorithms for the problems being targeted; this more targeted approach very significantly reduces the initial search space, resulting in a practical approach rather than a mere theoretical curiosity. Iterative refining of the primitives allows for gradual and directed enlarging of the search space until convergence.
As meta-heuristics are themselves a type of algorithm, they too can be automatically designed employing hyper-heuristics. For instance, in 2007, genetic programming was used to evolve mate selection in evolutionary algorithms; in 2011, linear genetic programming was used to evolve crossover operators; more recently, genetic programming was used to evolve complete black-box search algorithms, SAT solvers, and FuzzyART category functions. Moreover, hyper-heuristics may be applied before deploying an algorithm (offline) or while problems are being solved (online), or even continuously learn by solving new problems (life-long). Offline and life-long hyper-heuristics are particularly useful for real-world problem solving where one can afford a large amount of a priori computational time to subsequently solve many problem instances drawn from a specified problem domain, thus amortizing the a priori computational time over repeated problem solving. Recently, the design of multi-objective evolutionary algorithm components was automated.
Very little is known yet about the foundations of hyper-heuristics, such as the impact of the meta-heuristic exploring algorithm space on the performance of the thus automatically designed algorithm. An initial study compared the performance of algorithms generated by hyper-heuristics powered by five major types of genetic programming. Another avenue for research is investigating the potential performance improvements obtained through the use of asynchronous parallel evolution to exploit the typical large variation in fitness evaluation times when executing hyper-heuristics.
Content
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.
Organizers
Daniel Tauritz
Daniel Tauritz is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University (AU), Interim Director and Chief Cyber AI Strategist of the Auburn Cyber Research Center, the founding Head of AU’s Biomimetic Artificial Intelligence Research Group (BioAI Group), a cyber consultant for Sandia National Laboratories, a Guest Scientist at Los Alamos National Laboratory (LANL), and founding academic director of the LANL/AU Cyber Security Sciences Institute (CSSI). He received his Ph.D. in 2002 from Leiden University. His research interests include the design of generative 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 Woodward
John R. Woodward is a lecturer at the Queen Mary University of London. Formerly he was a lecturer at the University of Stirling, within the CHORDS group (http://chords.cs.stir.ac.uk/) and was employed on the DAASE project (http://daase.cs.ucl.ac.uk/). Before that he was a lecturer for four years at the University of Nottingham. He holds a BSc in Theoretical Physics, an MSc in Cognitive Science and a PhD in Computer Science, all from the University of Birmingham. His research interests include Automated Software Engineering, particularly Search Based Software Engineering, Artificial Intelligence/Machine Learning and in particular Genetic Programming. He has over 50 publications in Computer Science, Operations Research and Engineering which include both theoretical and empirical contributions, and given over 50 talks at International Conferences and as an invited speaker at Universities. He has worked in industrial, military, educational and academic settings, and been employed by EDS, CERN and RAF and three UK Universities.
Manuel López-Ibáñez
Dr. López-Ibáñez is Senior Distinguished Researcher at the University of Málaga (Spain) and a Senior Lecturer (Associate Professor) in the Decision and Cognitive Sciences Research Centre at the Alliance Manchester Business School, University of Manchester, UK. He received the M.S. degree in computer science from the University of Granada, Granada, Spain, in 2004, and the Ph.D. degree from Edinburgh Napier University, U.K., in 2009. He has published 27 journal papers, 9 book chapters and 48 papers in peer-reviewed proceedings of international conferences on diverse areas such as evolutionary algorithms, ant colony optimization, multi-objective optimization, pump scheduling and various combinatorial optimization problems. His current research interests are experimental analysis and automatic design of stochastic optimization algorithms, for single and multi-objective optimization. He is the lead developer and current maintainer of the irace software package (http://iridia.ulb.ac.be/irace).
ECPERM — Evolutionary Computation for Permutation Problems
http://www.sc.ehu.es/ccwbayes/gecco2021_ecperm/
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.
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
Organizers
Valentino Santucci
Valentino Santucci (Ph.D.) is Assistant Professor in Computer Science and Engineering at the University for Foreigners of Perugia. In 2012, he received his Ph.D. in Computer Science and Mathematics from the University of Perugia. His main research interests involve the broad areas of Artificial Intelligence and Computational Intelligence. In particular, in the field of Evolutionary Computation, his research focuses on algebraic frameworks for studying combinatorial search spaces and the dynamics of evolutionary algorithms. Other areas of interests include Natural Language Processing and Machine Learning applications to both e-learning and sustainability-related problems. He authored over forty scientific publications, organized special sessions and workshops in international conferences, and served as guest editor for special issues in top journals.
Marco Baioletti
Marco Baioletti received a PhD in Statistics and Mathematics for the Economical and Social Research from the University of Perugia in 1993. He is currently Assistant Professor at Department of Mathematics and Computer Science, University of Perugia. His research interests include evolutionary computation, automated planning, scheduling, probabilistic logic, possibility theory, neuroevolution. He is the author of more than 50 scientific publications on international journals and conferences.
Josu Ceberio
Josu Ceberio received the bachelor degree in Computer Science 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 40 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 reinforcement learning.
John McCall
John McCall is a Professor of Computing Science at Robert Gordon University. He works in the Computational Intelligence research group, which he founded in 2003. He has over twenty-five years research experience in naturally-inspired computing. His research focuses on the study and analysis of a range of naturally-inspired optimization algorithms (genetic algorithms, particle swarm optimisation, ant colony optimisation, estimation of distribution algorithms etc.) and their application to difficult learning and optimisation problems, particularly real-world problems arising in complex engineering and medical / biological systems. Application areas of this research include medical decision support, data modeling of drilling operations, analysis of biological sequences, staff rostering and scheduling, industrial process optimization and bio-control. He has over 150 publications in books, journals and conferences. He has successfully supervised 20 PhD students and has examined over 30 PhD theses.
Alfredo Milani
Alfredo Milani received the Doctor degree in computer science from University of Pisa, Pisa, Italy, in 1987. He is an Associate Professor with the Department of Computer Science, University of Perugia, Perugia, Italy, where he is the Head of the Knowledge and IT Laboratory. He has been a Visiting Associate Professor with Hong Kong Baptist University, Hong Kong, and a Visiting Scientist with Jet Propulsion Laboratory, Pasadena, CA, USA. His current research interests include artificial intelligence (AI), planning and agent systems, evolutionary meta-heuristics, data mining, and semantic proximity measures. Prof. Milani was the Chair or the Co-Chair of several international conferences in the AI area.
EVOGRAPH — 2nd Workshop on Evolutionary Data Mining and Optimization over Graphs
(This URL will be updated once the workshop is accepted) http://jrlab.science/1st-workshop-on-evolutionary-data-mining-and-optimization-over-graphs-evograph
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 2021, 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-2021.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, fake news and misinformation, 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.
Organizers
Eneko Osaba
Eneko Osaba is a researcher at TECNALIA Research & Innovation. He holds a BSc in Computer Science since 2010, and he received in 2011 the title of MSc in Development and Integration of Software Solutions. He defended his thesis, which is focused on artificial intelligence, at November 2015, obtaining the maximum qualification Cum-Laude and the International Mention. Eneko also obtained the "PhD obtaining award" granted by the Basque Government. Throughout his career, he has participated in the proposal, development and justification of 25 local and european research projects. Additionally, Eneko has also participated in the publication of 110 scientific papers (including more than 25Q1).
It should be highlighted that, in 2016, Eneko has obtained three national accreditations from the National Agency for Quality Assessment and Accreditation of Spain, ANECA: PhD lecturer, PhD lecturer of private university and PhD assistant lecturer. It is also noteworthy that during 2014-2015 Eneko began his career as a lecturer, teaching statistics at the University of Deusto to 2nd year engineering students. Additionally, in 2016-2017, he has participated as coordinator and teacher of the Research Seminar on Academic Writing in Engineering, and as teacher in the Research Seminar on the Research Career in Engineering, both of them part of the PhD Program in Engineering for the Information Society and Sustainable Development of the University of Deusto.
Eneko completed in June 2015 a 3-months-stay at Middlesex University (London) under the supervision of Dr. Xin-She Yang thanks to a grant awarded by the Basque Government, after a competitive process. Additionally, he completed two different STSM, one in the Department of Spatial Planning and Infrastructure of the University of Malta during April 2016, and the other one in the Dipartimento di Ingegneria Civile, Edile e Architettura. Universitá Politecnica delle Merche, during March 2017. These short stays wererelated with COST Action TU1306, Cyberparks project. Finally, it is interesting to mention that he has been part of the program committee in more than 20 different conferences. At HAIS 2015 and IDC 2018 Eneko was also member of the organizing committee. Additionally, Eneko was the publications chair in IDC 2018, and he organized several special sessions in conferences such as CEC 2017, IDC 2018, IDEAL 2018, DCAI 2018, PAAMS 2019, GECCO 2019, IEEE ITSC 2020, IDEAL 2020 and CEC 2020. Besides this, he is member of the editorial board of International Journal of Artificial Intelligence, Journal of Advanced Transportation and Data in Brief journal. Furthermore, he has acted as guess editor in journals such as Journal of Computational Science, Neurocomputing, Logic Journal of IGPL, Advances in Mechanical Engineering journal, Swarm & Evolutionary Computation, Processes, Algorithms and IEEE ITS Magazine. In his research profile it can be found a 18 H-index with 1300 cites in Google Scholar. Additionally, Eneko was an Individual Ambassador for ORCID in 2017 and 2018. Finally, he has eight intellectual property registers, granted by the Basque Government, as well as two european patents.
Javier Del Ser
Prof. Dr. Javier Del Ser 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) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimization at TECNALIA (Spain), a visiting fellow at the Basque Centre for Applied Mathematics (BCAM) and a part-time 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 190 articles, co-supervised 6 Ph.D. theses, edited 4 books, co-authored 6 patents and participated/led more than 35 research projects. He is a senior member of the IEEE.
David Camacho
Dr. David Camacho David Camacho is currently working as Full Professor in the Computer Systems Engineering Department at Technical University of Madrid (Universidad Politécnica de Madrid), Spain. He is the head of the Applied Intelligence & Data Analysis Group (http://aida.etsisi.upm.es). He received a Ph.D. with honors in Computer Science (2001) from Universidad Carlos III de Madrid, and a B.S. in Physics (1994) from Universidad Complutense de Madrid. He has published more than 300 journals, books, and conference papers. His research interests include Data Mining (Clustering), Computational Intelligence (mainly in Evolutionary Computation: GA & GP, Swarm Intelligence: ACO algorithms), Social Network Analysis and Graph Computing, and Multi- Agent Systems & DSS (Unmanned Air Vehicles).
EVORL — Evolutionary Reinforcement Learning Workshop
https://sites.google.com/view/evorl
Summary
In recent years reinforcement learning (RL) has received a lot of attention thanks to its performance and ability to address complex tasks. At the same time, multiple recent papers, notably work from OpenAI, have shown that evolution strategies (ES) can be competitive with standard RL algorithms on some problems while being simpler and more scalable. Similar results were obtained by researchers from Uber, this time using a gradient-free genetic algorithm (GA) to train deep neural networks on complex control tasks. Moreover, recent research in the field of evolutionary algorithms (EA) has led to the development of algorithms like Novelty Search and Quality Diversity, capable of efficiently addressing complex exploration problems and finding a wealth of different policies while improving the external reward (QD) or without relying on any reward at all (NS). All these results and developments have sparked a strong renewed interest in such population-based computational approaches.
Nevertheless, even if EAs can perform well on hard exploration problems they still suffer from low sample efficiency. This limitation is less present in RL methods, notably because of sample reuse, while on the contrary they struggle with hard exploration settings. The complementary characteristics of RL algorithms and EAs have pushed researchers to explore new approaches merging the two in order to harness their respective strengths while avoiding their shortcomings.
Some recent papers already demonstrate that the interaction between these two fields can lead to very promising results. We believe that this is a nascent field where new methods can be developed to address problems like sparse and deceptive rewards, open-ended learning, and sample efficiency, while expanding the range of applicability of such approaches.
In this workshop, we want to highlight this new field currently developing while proposing an outlet at GECCO for the two communities (RL and EA) to meet and interact, in order to encourage collaboration between researchers to discuss past and new challenges and develop new applications.
The workshop will focus on the following topics:
* Evolutionary reinforcement learning
* Population-based methods for policy search
* Evolution strategies
* Neuroevolution
* Hard exploration and sparse reward problems
* Deceptive rewards
* Novelty and diversity search methods
* Divergent search
* Sample-efficient direct policy search
* Intrinsic motivation, curiosity
* Building or designing behaviour characterisations
* Meta-learning, hierarchical learning
* Evolutionary AutoML
* Open-ended learning
Organizers
Giuseppe Paolo
Giuseppe Paolo is a PhD student at ISIR in Sorbonne University, under the supervision of Stéphane Doncieux, and at AI-Lab in Softbank Robotics Europe, under the supervision of Alban Laflaquière. His research focuses on the intersection between evolutionary algorithms and reinforcement learning algorithm to tackle sparse rewards problems. Giuseppe Paolo received his M.Sc. in Robotics, Systems and Control at ETH Zurich in 2018 and the engineering degree from the Politecnico di Torino in 2015. He also did two research internships at RAM-Lab at the Hong Kong University of Science and Technology and at IBM Research Zurich.
Alex Coninx
Alex Coninx is an associate professor at ISIR, Sorbonne Université, with a main research focus on how artificial agents can autonomously build relevant representations of their environment and use them to explore and achieve some goals. This question is investigated using methods from developmental robotics, evolutionary and population-based approaches, state representation learning and bayesian optimization. Alex Coninx received an engineering degree from École Centrale Paris in 2006 and a Ph.D. in computer science from Université de Grenoble in 2012. Before joinining Sorbonne Université as faculty in 2016, Alex also worked at LPPA Collège de France, Imperial College London and EDF Labs, in the context of several European projects (ICEA, ALIZ-E, BAMBI, DREAM) focusing on neuroinspired models, autonomous robotics, cognitive architectures, developmental approaches and bayesian reasoning.
Antoine Cully
Antoine Cully is Lecturer (Assistant Professor) at Imperial College London (United Kingdom). His research is at the intersection between artificial intelligence and robotics. He applies machine learning approaches, like evolutionary algorithms, on robots to increase their versatility and their adaptation capabilities. In particular, he has recently developed Quality-Diversity optimization algorithms to enable robots to autonomously learn large behavioural repertoires. For instance, this approach enabled legged robots to autonomously learn how to walk in every direction or to adapt to damage situations.
Antoine Cully received the M.Sc. and the Ph.D. degrees in robotics and artificial intelligence from the Pierre et Marie Curie University of Paris, France, in 2012 and 2015, respectively, and the engineer degree from the School of Engineering Polytech’Sorbonne, in 2012. His Ph.D. dissertation has received three Best-Thesis awards. He has published several journal papers in prestigious journals including Nature, IEEE Transaction in Evolutionary Computation, and the International Journal of Robotics Research. His work with Jean-Baptiste Mouret was recently featured on the cover of Nature (Cully et al., 2015) and received the "Outstanding Paper of 2015" award from the Society for Artificial Life (2016), and the French "La Recherche" award (2016).
Adam Gaier
Adam Gaier is a research scientist at the Autodesk AI Lab where he pursues basic research in evolutionary and machine learning and the application of these techniques to problems in design and robotics. He received masters degrees in Evolutionary and Adaptive Systems form the University of Sussex and Autonomous Systems at the Bonn-Rhein-Sieg University of Applied Sciences, and a PhD from Inria and the University of Lorraine — where his dissertation focused on tackling expensive design problems through the fusion of machine learning, quality diversity, and neuroevolution approaches. His PhD work received recognition at top venure across these fields: including a spotlight talk at NeurIPS (machine learning), multiple best paper awards at GECCO (evolutionary computation), and a best student paper at AIAA (aerodynamics design optimization).
EVOSOFT — Evolutionary Computation Software Systems
https://evosoft.heuristiclab.com
Summary
Evolutionary computation (EC) methods are applied in many different domains. Therefore, soundly engineered, reusable, flexible, user-friendly, and interoperable software systems are more than ever required to bridge the gap between theoretical research and practical application. However, due to the heterogeneity of 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
Organizers
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. From 2011 to 2018 he was also CEO of the FH OÖ IT GmbH, which is the IT service provider 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, head of the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL), head of the Master degree program Software Engineering, vice-dean for research and development, and scientific director of the Softwarepark Hagenberg.
IAM — 6th Workshop on Industrial Applications of Metaheuristics
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 2021 include (but are not restricted to):
• Success stories for industrial applications of metaheuristics
• Pitfalls of industrial applications of metaheuristics.
• Metaheuristics to optimize dynamic industrial problems.
• Multi-objective optimization in real-world industrial problems.
• Meta-heuristics in very constraint industrial optimization problems: assuring feasibility, constraint-handling techniques.
• Reduction of computing times through parameter tuning and surrogate modelling.
• Parallelism and/or distributed design to accelerate computations.
• Algorithm selection and configuration for complex problem solving.
• Advantages and disadvantages of metaheuristics when compared to other techniques such as integer programming or constraint programming.
• New research topics for academic research inspired by real (algorithmic) needs in industrial applications.
Submission
Authors can submit short contributions including position papers of up to 4 pages and regular contributions of up to 8 pages following in each category the GECCO paper formatting guidelines. Software demonstrations will also be welcome.
The submission deadlines will adhere to the standard GECCO schedule for workshops.
The workshop itself will be announced through mailing lists and academic and industrial contacts of the organizers.
Organizers
Silvino Fernández Alzueta
Silvino Fernandez Alzueta is an R&D Engineer at the Global R&D Division of ArcelorMittal for more than 15 years. He develops his activity in the ArcelorMittal R&D Centre of Asturias (Spain), in the framework of the Business and TechnoEconomic Department. His has a Master Science degree in Computer Science and a Ph.D. in Engineering Project Management, both obtained at University of Oviedo in Spain. His main research interests are in analytics, metaheuristics and swarm intelligence, and he has broad experience in using these 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 Pellicer is a research engineer of the Global R&D Asturias Centre at ArcelorMittal (world's leading integrated steel and mining company), working at the Business & Technoeconomic department. 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 research director of the Belgian F.R.S.-FNRS working at the IRIDIA laboratory of Université libre de Bruxelles (ULB), Belgium. He received 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 22 edited proceedings or books, 11 journal special issues, and more than 250 journal, conference articles and book chapters, many of which are highly cited. He is associate editor of Computational Intelligence, Evolutionary Computation and Applied Mathematics and Computation and on the editorial board of seven other journals. 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.
IWLCS — 24th International Workshop on Learning Classifier Systems
https://iwlcs.organic-computing.de
Summary
Learning Classifier Systems (LCSs) are a class of powerful Evolutionary Machine Learning (EML) algorithms that combine the global search of evolutionary algorithms with the local optimization of reinforcement or supervised learning. They form predictions by combining an evolving set of localized models each of which is responsible for a part of the problem space. While the localized models themselves are trained using machine learning techniques ranging from simple adaptive filters to more complex ones such as artificial neural networks, their responsibilities are optimized by powerful heuristic such as GAs.
Over the last four decades, LCSs have shown great potential in various problem domains such as behaviour modelling, online control, function approximation, classification, prediction and data mining. Their unique strengths lie in their adaptability and flexibility, them making only a minimal set of assumptions and, most importantly, their transparency. Topics that have been central to LCS for many years are more and more becoming a matter of high interest for other machine learning communities as well these days; the prime example is an increase in human interpretability of generated models which especially the booming Deep Learning community is keen on obtaining (Explainable AI). This workshop serves as a critical spotlight to disseminate the long experience of LCS research in these areas, to present new and ongoing research in the field, to attract new interest and to expose the machine learning community to an alternative, often advantageous, modelling paradigm.
Particular topics of interest are (not exclusively):
• advances in LCS methods (local models, problem space partitioning, classifier mixing, …)
• evolutionary reinforcement learning (multi-step LCS, neuroevolution, …)
• state of the art analysis (quantitative/qualitative surveys, carefully crafted comparative experimental benchmarks, …)
• formal developments in LCSs (provably optimal parametrization, time bounds, generalization, …)
• interpretability of evolved knowledge bases (knowledge extraction, visualization, …)
• advances in LCS paradigms (Michigan/Pittsburgh style, hybrids, iterative rule learning, …)
• hyperparameter optimization (hyperparameter selection, online self-adaptation, …)
• applications (medical domains, bio-informatics, intelligence in games, cyber-physical systems, …)
• optimizations and parallel implementations (GPU acceleration, matching algorithms, …)
• other evolutionary rule-based ML systems (artificial immune/evolving fuzzy rule-based systems, …)
Organizers
David Pätzel
David Pätzel is a PhD student at the Department of Computer Science at the University of Augsburg, Germany. He received his B.Sc. in Computer Science from the University of Augsburg in 2015 and his M.Sc. in the same field in 2017. His main research is directed towards Learning Classifier Systems with a focus on developing a more formal, probabilistic understanding of LCSs such as XCS(F) that can, for example, be used to improve existing algorithms. Besides that, his research interests include reinforcement learning, evolutionary machine learning algorithms and pure functional programming. He is an elected organizing committee member of the International Workshop on Learning Classifier Systems since 2020.
Alexander Wagner
Alexander Wagner is a PhD student at the Department of Artificial Intelligence in Agricultural Engineering at the University of Hohenheim, Germany. He received his B.Sc. and M.Sc. degrees in computer science from the University of Augsburg in 2018 and 2020, respectively. His bachelor’s thesis already dealt with the field of Learning Classifier Systems. This sparked his interest and he continued working on Learning Classifier Systems, especially XCS, during his master studies. Consequently, he also dedicated his master’s thesis to this topic in greater depth. His current research focuses on the application of Learning Classifier Systems, in particular XCS and its derivatives, to self-learning adaptive systems designed to operate in real-world environments, especially in agricultural domains. In this context, the emphasis of his research is to increase reliability of XCS or LCS in general. His research interests also include reinforcement learning, evolutionary machine learning algorithms, neural networks and neuro evolution.
Michael Heider
Michael Heider is a PhD student at the Department of Computer Science at the University of Augsburg, Germany. He received his B.Sc. in Computer Science from the University of Augsburg in 2016 and his M.Sc. in Computer Science and Information-oriented Business Management in 2018. His main research is directed towards Learning Classifier Systems, especially following the Pittsburgh style, with a focus on regression tasks encountered in industrial settings. Those have a high regard for both accurate as well as comprehensive solutions. To achieve comprehensibility he focuses on compact and simple rule sets. Besides that, his research interest include optimization techniques and unsupervised learning (e.g. for data augmentation or feature extraction).
LAHS — Landscape-Aware Heuristic Search
https://sites.google.com/view/lahs-workshop/
Summary
Fitness landscape analysis and visualisation can provide significant insights into problem instances and algorithm behaviour. The aim of the workshop is to encourage and promote the use of landscape analysis to improve the understanding, the design and, eventually, the performance of search algorithms. Examples include landscape analysis as a tool to inform the design of algorithms, landscape metrics for online adaptation of search strategies, mining landscape information to predict instance hardness and algorithm runtime. The workshop will focus on, but not be limited to, topics such as:
- Evolvability and searchability characterisation
- Exploiting problem structure
- Fitness landscape analysis
- Fitness landscape visualisation
- Fitness landscape theory
- Grey-box optimisation
- Informed search strategies
- Local optima networks
- Multi-objective fitness landscapes
- Performance and failure prediction
We will invite submissions of three types of articles:
- research papers (up to 8 pages)
- software libraries/packages (up to 4 pages)
- position papers (up to 2 pages)
Organizers
Nadarajen Veerapen
Nadarajen Veerapen is an Associate Professor (maître de conférences) at the University of Lille, France. Previously he was a research fellow at the University of Stirling in Scotland. He holds a PhD in Computing Science from the University of Angers, France, where he worked on adaptive operator selection. His research interests include local search, hybrid methods, search-based software engineering and visualisation. He is the Electronic Media Chair for GECCO 2021 and has served as Electronic Media Chair for GECCO 2020, Publicity Chair for GECCO 2019 and as Student Affairs Chair for GECCO 2017 and 2018. He has previously co-organised the workshop on Landscape-Aware Heuristic Search at PPSN 2016, GECCO 2017-2019.
Katherine Malan
Katherine Malan is an associate professor in the Department of Decision Sciences at the University of South Africa. She received her PhD in computer science from the University of Pretoria in 2014 and her MSc & BSc degrees from the University of Cape Town. She has over 20 years' lecturing experience, mostly in Computer Science, at three different South African universities. Her research interests include automated algorithm selection in optimisation and learning, fitness landscape analysis and the application of computational intelligence techniques to real-world problems.
Arnaud Liefooghe
Arnaud Liefooghe has been an Associate Professor with the University of Lille, France, since 2010. He is a member of the CRIStAL Research Center, CNRS, and of the Inria Lille-Nord Europe Research Center. He is also the Co-Director of the MODŌ international lab between Shinshu University, Japan, and the University of Lille. He received a PhD degree from the University of Lille in 2009. In 2010, he was a Postdoctoral Researcher with the University of Coimbra, Portugal. In 2020, he was on CNRS sabbatical at JFLI, and an Invited Professor at the University of Tokyo, Japan. His research activities deal with the foundations, the design and the analysis of stochastic local search heuristic algorithms, with a particular interest in multi-objective optimization and landscape analysis. He has co-authored over eighty scientific papers in international journals and conferences. He was a recipient of the best paper award at EvoCOP 2011 and at GECCO 2015. He has recently served as the co-Program Chair for EvoCOP 2018 and 2019, as the Proceedings Chair for GECCO 2018, and as the co-EMO Track Chair for GECCO 2019.
Sébastien Verel
Sébastien Verel is a professor in Computer Science at the Université du Littoral Côte d'Opale, Calais, France, and previously at the University of Nice Sophia-Antipolis, France, from 2006 to 2013. He received a PhD in computer science from the University of Nice Sophia-Antipolis, France, in 2005. His PhD work was related to fitness landscape analysis in combinatorial optimization. He was an invited researcher in DOLPHIN Team at INRIA Lille Nord Europe, France from 2009 to 2011. His research interests are in the theory of evolutionary computation, multiobjective optimization, adaptive search, and complex systems. A large part of his research is related to fitness landscape analysis. He co-authored of a number of scientific papers in international journals, book chapters, book on complex systems, and international conferences. He is also involved in the co-organization EC summer schools, conference tracks, workshops, a special issue on EMO at EJOR, as well as special sessions in indifferent international conferences.
Gabriela Ochoa
Gabriela Ochoa is a Professor in Computing Science at the University of Stirling, Scotland, where she leads the Data Science and Intelligent Systems (DAIS) research group. She received BSc and MSc degrees in Computer Science from University Simon Bolivar, Venezuela and a PhD from University of Sussex, UK. She worked in industry for 5 years before joining academia, and has held faculty and research positions at the University Simon Bolivar, Venezuela and the University of Nottingham, UK. Her research interests lie in the foundations and application of evolutionary algorithms and optimisation methods, with emphasis on autonomous search, hyper-heuristics, fitness landscape analysis, visualisation and applications to logistics, transportation, healthcare, and software engineering. She has published over 110 scholarly papers (H-index 31) and serves various program committees. She was associate editor of the IEEE Transactions on Evolutionary Computation, is currently for the Evolutionary Computation Journal, and is a member of the editorial board for Genetic Programming and Evolvable Machines. She has served as organiser for various Evolutionary Computation events and served as the Editor-in-Chief for the Genetic and Evolutionary Computation Conference (GECCO) 2017. She is a member of the executive boards of the ACM interest group on Evolutionary Computation (SIGEVO), and the leading European Event on bio-inspired computing (EvoSTAR).
NEWK — Neuroevolution at Work
https://www.newk2021.icar.cnr.it/
Summary
In the last years, inspired by the fact that natural brains themselves are the products of an evolutionary process, the quest for evolving and optimizing artificial neural networks through evolutionary computation has enabled researchers to successfully apply neuroevolution to many domains such as strategy games, robotics, big data, and so on. The reason behind this success lies in important capabilities that are typically unavailable to traditional approaches, including evolving neural network building blocks, hyperparameters, architectures and even the algorithms for learning themselves (meta-learning).
Although promising, the use of neuroevolution poses important problems and challenges for its future developments.
Firstly, many of its paradigms suffer from lack of parameter-space diversity, meaning with this a failure in providing diversity in the behaviors generated by the different networks.
Moreover, the harnessing of neuroevolution to optimize deep neural networks requires noticeable computational power and, consequently, the investigation of new trends in enhancing the computational performance.
This workshop aims:
- to bring together researchers working in the fields of deep learning, evolutionary computation and optimization to exchange new ideas about potential directions for future research;
- to create a forum of excellence on neuroevolution that will help interested researchers from a variety of different areas, ranging from computer scientists and engineers on the one hand to application-devoted researchers on the other hand, to gain a high-level view about the current state of the art.
Since an increasing trend to neuroevolution in the next years seems likely to be observed, not only will a workshop on this topic be of immediate relevance to get in insight in future trends, it will also provide a common ground to encourage novel paradigms and applications. Therefore, researchers putting emphasis on neuroevolution issues in their work are encouraged to submit their work. This event is also the ideal place for informal contacts, exchanges of ideas and discussions with fellow researchers.
The scope of the workshop is to receive high-quality contributions on topics related to neuroevolution, ranging from theoretical works to innovative applications in the context of (but not limited to):
- theoretical and experimental studies involving neuroevolution on machine learning in general, and on deep and reinforcement learning in particular
- development of innovative neuroevolution paradigms
- parallel and distributed neuroevolution methods
- new search operators for neuroevolution
- hybrid methods for neuroevolution
- surrogate models for fitness estimation in neuroevolution
- applications of neuroevolution to Artificial Intelligence agents and to real-world problems.
Organizers
Ernesto Tarantino
Ernesto Tarantino was born in S. Angelo a Cupolo, Italy, in 1961. He received the Laurea degree in Electrical Engineering in 1988 from University of Naples, Italy. He is currently a researcher at National Research Council of Italy. After completing his studies, he conducted research in parallel and distributed computing. During the past decade his research interests have been in the fields of theory and application of evolutionary techniques and related areas of computational intelligence. He is author of more than 100 scientific papers in international journal, book and conferences. He has served as referee and organizer for several international conferences in the area of evolutionary computation.
De Falco Ivanoe
Ivanoe De Falco received his degree in Electrical Engineering “cum laude” from the University of Naples “Federico II”, Naples, Italy, in 1987. He is currently a senior researcher at the Institute for High-Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR), where he is the Responsible of the Innovative Models for Machine Learning (IMML) research group. His main fields of interest include Computational Intelligence, with particular attention to Evolutionary Computation, Swarm Intelligence and Neural Networks, Machine Learning, Parallel Computing, and their application to real-world problems, especially in the medical domain. He is a member of the World Federation on Soft Computing (WFSC), the IEEE SMC Technical Committee on Soft Computing, the IEEE ComSoc Special Interest Research Group on Big Data for e-Health, the IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing, and is an Associate Editor of Applied Soft Computing Journal (Elsevier). He is the author of more than 120 papers in international journals and in the proceedings of international conferences.
Della Cioppa Antonio
Antonio Della Cioppa received the Laurea degree in Physics and the Ph.D. degree in Computer Science, both from University of Naples “Federico II”, Naples, Italy, in 1993 and 1999, respectively. From 1999 to 2003, he was a Postdoctoral Fellow at the Department of Computer Science and Electrical Engineering, University of Salerno, Salerno, Italy. In 2004, he joined the Department of Information Engineering, Electrical Engineering and Mathematical Applications, University of Salerno, where he is currently Associate Professor of Computer Science and Artificial Intelligence. His main fields of interest are in the Computational Intelligence area, with particular attention to Evolutionary Computation, Swarm Intelligence and Neural Networks, Machine Learning, Parallel Computing, and their application to real-world problems. Prof. Della Cioppa is a member of the Association for Computing Machinery (ACM), the ACM Special Interest Group on Genetic and Evolutionary Computation, the IEEE Computational Intelligence Society and the IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing. He serves as Associate Editor for the Applied Soft Computing journal (Elsevier), Evolutionary Intelligence (Elsevier), Algorithms (MDPI). He has been part of the Organizing or Scientific Committees for tens of international conferences or workshops, and has authored or co-authored about 100 papers in international journals, books, and conference proceedings.
Scafuri Umberto
Umberto Scafuri was born in Baiano (AV) on May 21, 1957. He got his Laurea degree in Electrical Engineering at the University of Naples ""Federico II"" in 1985. He currently works as a technologist at the Institute of High Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). His research activity is basically devoted to parallel and distributed architectures and evolutionary models.
PDEIM — Parallel and Distributed Evolutionary Inspired Methods
https://www.pdeim2021.icar.cnr.it/
Summary
Nature inspired methods include all paradigms of evolutionary computation such as genetic algorithms, evolution strategies, genetic programming, ant algorithms, particle swarm systems and so on. These methods are being more and more frequently used to face real-world problems characterized by a huge number of possible solutions, thus their execution often requires large amounts of time. Therefore, they can highly benefit from parallel and distributed implementations, in terms of both reduction in execution time and improvement in quality of the achieved solutions.
The workshop aims at creating a forum of excellence on the use of parallel models of evolutionary computation methods. This can be achieved by bringing together for an exchange of ideas researchers from a variety of different areas, ranging from computer scientists and engineers on the one hand to application-devoted researchers like biologists, chemists, physicians on the other hand.
Since we are going to increasingly observe a trend towards parallelization of evolutionary models in the next years, not only will a Workshop on this topic be of immediate relevance, it will also provide a platform for encouraging such implementations.
Researchers putting emphasis on parallel issues in their work with evolutionary systems are encouraged to submit their work. This event is the ideal place for informal contact, exchange of ideas and discussions with fellow researchers.
The scope of the workshop is to receive high-quality contributions on topics related to parallel and distributed versions of evolutionary methods, ranging from theoretical work to innovative applications in the context of (but not limited to):
1. Theoretical and experimental studies on parallel and distributed model implementations (population size, synchronization, homogeneity, communication, topology, speedup, etc.)
2. New trends in parallel and distributed evolutionary computation including Grid and Cloud Computing, Internet Computing, General Purpose Computation on Graphics Processing Units (GPGPU), multi-core architectures and supercomputers.
3. New parallel and distributed evolutionary models.
4. Parallel and distributed implementation of evolutionary-fuzzy, evolutionary-neuro and evolutionary-neuro-fuzzy hybrids.
5. Parallel and distributed evolutionary algorithms for data
mining on big data and machine learning.
6. Parallel and distributed evolutionary deep learning.
7. Parallel and distributed multi-objective evolutionary algorithms.
8. Real-world applications of parallel and distributed evolutionary algorithms.
Organizers
Ernesto Tarantino
Ernesto Tarantino was born in S. Angelo a Cupolo, Italy, in 1961. He received the Laurea degree in Electrical Engineering in 1988 from University of Naples, Italy. He is currently a researcher at National Research Council of Italy. After completing his studies, he conducted research in parallel and distributed computing. During the past decade his research interests have been in the fields of theory and application of evolutionary techniques and related areas of computational intelligence. He is author of more than 100 scientific papers in international journal, book and conferences. He has served as referee and organizer for several international conferences in the area of evolutionary computation.
De Falco Ivanoe
Ivanoe De Falco received his degree in Electrical Engineering “cum laude” from the University of Naples “Federico II”, Naples, Italy, in 1987. He is currently a senior researcher at the Institute for High-Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR), where he is the Responsible of the Innovative Models for Machine Learning (IMML) research group. His main fields of interest include Computational Intelligence, with particular attention to Evolutionary Computation, Swarm Intelligence and Neural Networks, Machine Learning, Parallel Computing, and their application to real-world problems, especially in the medical domain. He is a member of the World Federation on Soft Computing (WFSC), the IEEE SMC Technical Committee on Soft Computing, the IEEE ComSoc Special Interest Research Group on Big Data for e-Health, the IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing, and is an Associate Editor of Applied Soft Computing Journal (Elsevier). He is the author of more than 120 papers in international journals and in the proceedings of international conferences.
Della Cioppa Antonio
Antonio Della Cioppa received the Laurea degree in Physics and the Ph.D. degree in Computer Science, both from University of Naples “Federico II”, Naples, Italy, in 1993 and 1999, respectively. From 1999 to 2003, he was a Postdoctoral Fellow at the Department of Computer Science and Electrical Engineering, University of Salerno, Salerno, Italy. In 2004, he joined the Department of Information Engineering, Electrical Engineering and Mathematical Applications, University of Salerno, where he is currently Associate Professor of Computer Science and Artificial Intelligence. His main fields of interest are in the Computational Intelligence area, with particular attention to Evolutionary Computation, Swarm Intelligence and Neural Networks, Machine Learning, Parallel Computing, and their application to real-world problems. Prof. Della Cioppa is a member of the Association for Computing Machinery (ACM), the ACM Special Interest Group on Genetic and Evolutionary Computation, the IEEE Computational Intelligence Society and the IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing. He serves as Associate Editor for the Applied Soft Computing journal (Elsevier), Evolutionary Intelligence (Elsevier), Algorithms (MDPI). He has been part of the Organizing or Scientific Committees for tens of international conferences or workshops, and has authored or co-authored about 100 papers in international journals, books, and conference proceedings.
Scafuri Umberto
Umberto Scafuri was born in Baiano (AV) on May 21, 1957. He got his Laurea degree in Electrical Engineering at the University of Naples ""Federico II"" in 1985. He currently works as a technologist at the Institute of High Performance Computing and Networking (ICAR) of the National Research Council of Italy (CNR). His research activity is basically devoted to parallel and distributed architectures and evolutionary models.
RWACMO — Real-World Applications of Continuous and Mixed-integer Optimization
http://www.ifs.tohoku.ac.jp/shimoyama/gecco-rwacmo/2021/about/
Summary
Continuous and mixed-integer optimization are two fields where evolutionary computation (EC) and related techniques (e.g. particle swarm optimization and differential evolution) have been successfully applied in disciplines such as engineering design, robotics, and bioinformatics. Real-world continuous and mixed-integer problems possess unique challenges that cannot be fully replicated by algebraic and artificial problems, where characteristics of these problems could be different across a variety of scientific fields. Some of these characteristics are expensive function evaluations, huge design spaces, multi/many-objective optimization, correlated variables, etc. Besides optimization, EC/related techniques also frequently work hand-in-hand with machine learning and data mining tools to explore trade-offs and to infer important knowledge that are highly 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 applications of EC/related techniques. It is important that a special focus needs to be given on real-world applications to synergize the research in EC/related techniques with real-world applications in both industry and academia, which, in turn, will also benefit research in algorithmic development.
This workshop aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for 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) one/two invited talks from researchers/practitioners with a successful record on applications of EC for solving continuous/mixed integer problems, (2) presentations of submission-based papers, and (3) final discussion with the speakers and audiences to talk about future challenges. The workshop encourages submission from various disciplines to stimulate multidisciplinary research discussion. The invited speakers are expected to deliver talks with the following topics: (1) current advancements of EC/related techniques in handling real-world problems, (2) interaction and synergy between algorithmic development and real-world problem solving, or (3) successful application of EC/related techniques in boosting the productivity and efficiency in industry.
Organizers
Pramudita Palar
Pramudita Satria Palar is an assistant professor 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. He was also a visiting researcher at the University of Cambridge in 2015 and Leiden University in 2017. 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.
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, USA, and an invited professor at Ecole Centrale De Lyon, France. His research interests are multi-objective optimization, Bayesian optimization, and uncertainty quantification for fluid machinery design. 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 a 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 the Hokkaido University of Science. His study interest is innovative contrivances for next-generation aerospace vehicles via design informatics, including multidisciplinary and many-objective optimizations by evolutionary computations and data analyses.
SAEOPT — Surrogate-Assisted Evolutionary Optimisation
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):
- Bayesian optimisation
- 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
Organizers
Alma Rahat
Dr Alma Rahat is a Lecturer in Data Science at Swansea University. He is an expert in Bayesian search and optimisation for computationally expensive problems (for example, geometry optimisation using computational fluid dynamics). His particular expertise is in developing effective acquisition functions for single and multi-objective problems, and locating the feasible space. He is one of the twenty four members of the IEEE Computational Intelligence Society Task Force on Data-Driven Evolutionary Optimization of Expensive Problems, and he has been the lead organiser for the popular Surrogate-Assisted Evolutionary Optimisation workshop at the prestigious Genetic and Evolutionary Computation Conference (GECCO) since 2016. He has a strong track record of working with industry on a broad range of optimisation problems. His collaborations have resulted in numerous articles in top journals and conferences, including a best paper in Real World Applications track at GECCO and a patent.
Dr Rahat has a BEng (Hons.) in Electronic Engineering from the University of Southampton, UK, and a PhD in Computer Science from the University of Exeter, UK. He worked as a product development engineer after his bachelor's degree, and held post-doctoral research positions at the University of Exeter. Before moving to Swansea, he was a Lecturer in Computer Science at the University of Plymouth, UK.
Richard Everson
Richard Everson is Professor of Machine Learning and Director of the Institute of Data Science and Artificial Intelligence at the University of Exeter. His research interests lie in statistical machine learning and multi-objective optimisation, and the links between them. Current research is on surrogate methods, particularly Bayesian optimisation, for large expensive-to-evaluate optimisation problems, especially computational fluid dynamics design optimisation.
Jonathan Fieldsend
Jonathan Fieldsend is Professor of 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 over 100 peer-reviewed publications in the evolutionary computation and machine learning domains, and has been working on uncertain problems in evolutionary computation since 2005. This strand of work has mainly been in multi-objective data-driven problems, although more recently has undertaken work on expensive uni-objective robust problems. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Force on Data-Driven Evolutionary Optimisation of Expensive Problems, and sits on the IEEE CIS Task Force on Multi-modal Optimisation and the IEEE CIS Task Force on Evolutionary Many-Objective Optimisation. Alongside EAPwU, he has been a co-organiser of the VizGEC and SAEOpt workshops at GECCO for a number of years.
Handing Wang
Handing Wang received the B.Eng. and Ph.D. degrees from Xidian University, Xi'an, China, in 2010 and 2015. She is currently a professor with School of Artificial Intelligence, Xidian University, Xi'an, China. Dr. Wang is an Associate Editor of IEEE Computational Intelligence Magazine and Complex & Intelligent Systems, chair of the Task Force on Intelligence Systems for Health within the Intelligent Systems Applications Technical Committee of IEEE Computational Intelligence Society. Her research interests include nature-inspired computation, multi- and many-objective optimization, multiple criteria decision making, and real-world problems. She has published over 10 papers in international journal, including IEEE Transactions on Evolutionary Computation (TEVC), IEEE Transactions on Cybernetics (TCYB), and Evolutionary Computation (ECJ).
Yaochu Jin
Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996 respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001. He is Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He is also a Finland Distinguished Professor, University of Jyvaskyla, Finland and a Changjiang Distinguished Professor, Northeastern University, China. His main research interests include evolutionary computation, machine learning, computational neuroscience, and evolutionary developmental systems, with their application to data-driven optimization and decision-making, self-organizing swarm robotic systems, and bioinformatics. He has (co)authored over 200 peer-reviewed journal and conference papers and has been granted eight patents on evolutionary optimization. Dr Jin is the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and Complex & Intelligent Systems. He was an IEEE Distinguished Lecturer (2013-2015) and Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015). He was the recipient of the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology and the 2014 IEEE Computational Intelligence Magazine Outstanding Paper Award. He is a Fellow of IEEE.
SECDEF — Genetic and Evolutionary Computation in Defense, Security, and Risk Management
https://projects.cs.dal.ca/projectx/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 seven 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.
Organizers
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 Ph.D. in Computer Science from University College Dublin, Ireland, and an 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.
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 CPHHI, KSAU-HS. He is specialized in data analysis and Artificial Intelligence. Dr. Alshammari is a member of many IEEE societies and review committees of several IEEE international conferences and journals. He is also an adjunct Faculty at the Faculty of Computer Science, Dalhousie University, Halifax, N.S., Canada
Tokunbo Makanju
SWINGA — Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges
Summary
Evolutionary algorithms, based on the Darwinian theory of evolution and Mendelian theory of genetic processes, as well as swarm algorithms (based on the emergent behavior of natural swarms), are popular and widely used for solving various optimization tasks. Currently, many researchers are investigating performance, efficiency, convergence speed, population diversity, and dynamics, as well as unconventional population models for a broad class of swarm and evolutionary algorithms.
This workshop is mainly focused on the swarm intelligence algorithms, like: well-known algorithms Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly, etc, as well as new promising/recently updated algorithms like Self-Organizing Migrating Algorithm (SOMA), and more original algorithms that were not created only based on the metaphors, but that were built on a solid foundation of balancing between exploration and exploitation, techniques to prevent stagnation in local extremes, competitive-cooperative phases, self-adaptation of movement over the search space, mimicking the mutation process known from the classical evolutionary computing techniques, and more. These algorithms show great performance in both continuous as well as discrete domains. The swarm algorithms have been used successfully on various real-problem optimizations tasks.
This workshop is concern about original research papers discussing new results, as well as novel algorithmic improvements tested on widely accepted benchmark tests. This workshop aims to bring together experts from fundamental research, and from various application fields to develop mutual intersections and fusion of techniques. Also, a discussion of real-problem-solving experiences will be carried out to define new open problems and challenges in this interesting and fast-growing field of research, that is currently undergoing re-exploration of methods due to neuro-evolution. The workshop will focus on, but not limited to, the following topics:
- The theoretical aspect of the swarm intelligence
- Swarm intelligence hybridization with other metaheuristics
- The performance improvement, testing, and efficiency of the swarm intelligence based algorithms
- Swarm intelligence based algorithms for complex optimization scenarios:
-- constrained optimization
-- multi-objective optimization
-- many-objective optimization
-- multimodal optimization and niching
-- expensive and surrogate assisted optimization
-- dynamic and uncertain optimization
-- large-scale optimization
- Swarm intelligence and its parallelization
- Swarm intelligence for discrete optimization
- Randomness, chaos, and its impact on swarm intelligence dynamics and algorithm performance.
- Swarm intelligence in real-world applications
- And more…
Organizers
Roman Senkerik
Roman Senkerik was born in Zlin, the Czech Republic, in 1981. He received an MSc degree in technical cybernetics from the Tomas Bata University in Zlin, Faculty of applied informatics in 2004, the Ph.D. degree also in technical Cybernetics, in 2008, from the same university, and Assoc. prof. Degree in Informatics from VSB – Technical University of Ostrava, in 2013.
From 2008 to 2013 he was a Research Assistant and Lecturer with the Tomas Bata University in Zlin, Faculty of applied informatics. Since 2014 he is an Associate Professor and since 2017 Head of the A.I.Lab https://ailab.fai.utb.cz/ with the Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlin. He is the author of more than 40 journal papers, 250 conference papers, and several book chapters as well as editorial notes. His research interests are the development of evolutionary algorithms, their modifications and benchmarking, soft computing methods, and their interdisciplinary applications in optimization and cyber-security, machine learning, neuro-evolution, data science, the theory of chaos, and complex systems. He is a recognized reviewer for many leading journals in computer science/computational intelligence. He was a part of the organizing teams for special sessions/workshops/symposiums at GECCO, IEEE WCCI, CEC, or SSCI events.
Ivan Zelinka
Ivan Zelinka (born in 1965, ivanzelinka.eu) is currently associated with the Technical University of Ostrava (VSB-TU), Faculty of Electrical Engineering and Computer Science. He graduated consequently at the Technical University in Brno (1995 - MSc.), UTB in Zlin (2001 - Ph.D.) and again at Technical University in Brno (2004 - Assoc. Prof.) and VSB-TU (2010 - Professor). Prof. Zelinka is the responsible supervisor/co-supervisor of several research projects focused on unconventional control of complex systems, security of mobile devices and communication and Laboratory of parallel computing amongst the others. He was also working on numerous grants and two EU projects as a member of the team (FP5 - RESTORM) and supervisor (FP7 - PROMOEVO) of the Czech team. He is also head of research team NAVY http://navy.cs.vsb.cz/. His research interests are computational intelligence, cyber-security, development of evolutionary algorithms, applications of the theory of chaos, controlling of complex systems. Prof. Zelinka was awarded Siemens Award for his Ph.D. thesis, as well as by journal Software news for his book about artificial intelligence. He is a member of the British Computer Society, Machine Intelligence Research Labs (MIR Labs), IEEE (committee of Czech section of Computational Intelligence), a few international program committees of various conferences, and several well respected journals.
Swagatam Das
Swagatam Das received the B. E. Tel. E., M. E. Tel. E (Control Engineering specialization) and Ph. D. degrees, all from Jadavpur University, India, in 2003, 2005, and 2009 respectively. Swagatam Das is currently serving as an associate professor at the Electronics and Communication Sciences Unit of the Indian Statistical Institute, Kolkata, India. His research interests include evolutionary computing, deep learning and non convex optimization in general. Dr. Das has published more than 300 research articles in peer-reviewed journals and international conferences. He is the founding co-editor-in-chief of Swarm and Evolutionary Computation, an international journal from Elsevier. He has also served as or is serving as the associate editors of the IEEE Trans. on Systems, Man, and Cybernetics: Systems, IEEE Computational Intelligence Magazine, Pattern Recognition (Elsevier),Neurocomputing (Elsevier),Engineering Applications of Artificial Intelligence (Elsevier), and Information Sciences (Elsevier). He is a founding Section Editor of Springer Nature Computer Science journal since 2019. Dr. Das has 18000+ Google Scholar citations and an H-index of 63 till date. He has been associated with the international program committees of several regular international conferences including IEEE CEC, IEEE SSCI, SEAL, GECCO, AAAI, and SEMCCO. He has acted as guest editors for special issues in journals like IEEE Transactions on Evolutionary Computation and IEEE Transactions on SMC, Part C. He is the recipient of the 2012 Young Engineer Award from the Indian National Academy of Engineering (INAE). He is also the recipient of the 2015 Thomson Reuters Research Excellence India Citation Award as the highest cited researcher from India in Engineering and Computer Science category between 2010 to 2014.
VIZGEC — Visualisation Methods in Genetic and Evolutionary Computation
Summary
Building on workshops held annually since 2010, the twelfth annual workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC), to be held at GECCO 2021 in Lille, aims 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 science 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 and Pareto fronts
* 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 human 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 in real time.
GEC methods have also recently been applied to the visualisation of data. As the amount of data available to data scientists 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.
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, such as audio.
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.
Organizers
David Walker
Dr David Walker is a Lecturer in Computer Science at the University of Plymouth. He has a PhD in Computer Science from the University of Exeter, where he conducted work on the visualisation of the mutually non-dominating sets generated by many-objective evolutionary algorithms. During his postdoctoral work he conducted research on the development of novel hyper-heuristics and interactive methods for solving optimisation problems. He joined the School of Computing, Electronics and Mathematics at the University of Plymouth in 2018, and is a member of both the Centre for Robotic and Neural Systems and Big Data Group. His research focuses on improving user understanding of nature-inspired algorithms using visualisation techniques, and incorporating humans as components of nature-inspired approaches using interactive evolution. He has organised a GECCO workshop on visualisation for the last eight years, and is active in reviewing for a range of journals.
Richard Everson
Richard Everson is Professor of Machine Learning and Director of the Institute of Data Science and Artificial Intelligence at the University of Exeter. His research interests lie in statistical machine learning and multi-objective optimisation, and the links between them. Current research is on surrogate methods, particularly Bayesian optimisation, for large expensive-to-evaluate optimisation problems, especially computational fluid dynamics design optimisation.
Dr. Rui Wang
Dr. Rui Wang is a Senior Research Scientist at Uber AI. He obtained a B.S. degree from the University of Science and Technology of China, and M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His research interests include evolutionary algorithms, complex systems, evolutionary robotics, reinforcement learning, and visualization methods, distributed systems for machine learning. His work has been published at ICML, GECCO, IJCAI, VizGEC as well as workshops at ICLR and NeurIPS, and has also been covered by Science Magazine, Wired, VentureBeat and Quanta Magazine, etc. His recent work on open-ended coevolution won a Best Paper Award at GECCO 2019.
Prof. Neil Vaughan
Neil Vaughan is research fellow of the Royal Academy of Engineering (RAEng) and held numerous academic positions in Computer Science, including at University of Chester, University of West London and Bournemouth University. His PhD focussed on AI for Healthcare Informatics and Biomedical Engineering. He has published research on neuroevolution for artificial neural networks applied to visualisation of multi-agent systems and robotic control including healthcare applications. He also published models for visualising evolutionary bio-inspired simulation and meta-heuristic solutions for combinatorial optimization including n-queens and travelling salesman problems.