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Workshops

Most workshops invite paper submissions (submissions are due April 8, 2024). Paper instructions and relevant dates can be found at Call for Workshop Papers. For questions about a specific workshop, please contact the workshop organizers. For general questions about the GECCO workshops, please contact the workshop chairs Nabi Omidvar (m.n.omidvar@leeds.ac.uk) and Nguyen Dang (nttd@st-andrews.ac.uk).

List of Workshops

TitleOrganizers
AABOH — Analysing algorithmic behaviour of optimisation heuristics
  • Anna V Kononova LIACS, Leiden University, The Netherlands
  • Niki van Stein Leiden University
  • Daniela Zaharie West University of Timisoara, Romania
  • Fabio Caraffini Institute of Artificial Intelligence, De Montfort University, Leicester, UK
  • Thomas Bäck LIACS, Leiden University, The Netherlands
BENCH@GECCO24 — Good Benchmarking Practices for Evolutionary Computation
  • Boris Naujoks Cologne University of Applied Sciences, Germany
  • Carola Doerr CNRS and Sorbonne University, France
  • Pascal Kerschke TU Dresden, Germany
  • Mike Preuss Leiden Institute of Advanced Computer Science
  • Vanessa Volz modl.ai (Denmark)
  • Olaf Mersmann Technische Hochschule Köln
EC + DM — Evolutionary Computation and Decision Making
  • Tinkle Chugh University of Exeter, UK
  • Richard Allmendinger The University of Manchester, UK
  • Hadi Akbarzadeh Khorshidi The University of Melbourne
ECADA 2024 — 14th Workshop on Evolutionary Computation for the Automated Design of Algorithms
  • Daniel Tauritz Auburn University, USA
  • John R. Woodward Loughborough University, UK
  • Emma Hart Edinburgh Napier University
ECXAI — Evolutionary Computation and Explainable AI
  • John McCall Robert Gordon University, UK
  • Jaume Bacardit Newcastle University, UK
  • Alexander Brownlee University of Stirling
  • Stefano Cagnoni University of Parma
  • Giovanni Iacca University of Trento, Italy
  • David Walker University of Exeter
EEAI — Embodied and Evolved Artificial Intelligence
  • Yue Xie University of Cambridge
  • David Howard Data61, CSIRO
  • Fumiya Iida University of Cambridge
  • Josie Hughes EPFL
EGML-EC — 3rd GECCO workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML-EC) 2024
  • Jamal Toutouh University of Málaga, Málaga, Spain
  • Una-May O’Reilly MIT, USA
  • João Correia University of Coimbra, Portugal
  • Penousal Machado University of Coimbra, CISUC, DEI
  • Erik Hemberg Massachusetts Institute of Technology, CSAIL, Cambridge, USA
EvoOSS — Open Source Software for Evolutionary Computation
  • Stefan Wagner University of Applied Sciences Upper Austria
  • Michael Affenzeller University of Applied Sciences Upper Austria
GGP — Graph-based Genetic Programming
  • Dennis G. Wilson ISAE-SUPAERO, University of Toulouse, France
  • Roman Kalkreuth Sorbonne University
  • Eric Medvet University of Trieste
  • Giorgia Nadizar Università degli Studi di Trieste, Italy
  • Giovanni Squillero Politecnico di Torino, Italy
  • Alberto Tonda National Institute of Research for Agriculture and Environment (INRAE), and Université Paris-Saclay, France
  • Yuri Lavinas University of Tsukuba
IAM 2024 — 9th Workshop on Industrial Applications of Metaheuristics (IAM 2024)
  • Silvino Fernández Alzueta Arcelormittal, Spain
  • Pablo Valledor Pellicer ArcelorMittal Global R&D
  • Thomas Stützle Université Libre de Bruxelles, Belgium
IWERL — 27th International Workshop on Evolutionary Rule-based Machine Learning
  • Abubakar Siddique Wellington Institute of Technology, Te Pūkenga – Whitireia WelTec
  • Michael Heider Universität Augsburg, Germany
  • Muhammad Iqbal Higher Colleges of Technology
  • Hiroki Shiraishi Yokohama National University, Japan
LAHS — Landscape-Aware Heuristic Search
  • Sarah L. Thomson University of Stirling
  • Nadarajen Veerapen Université de Lille, France
  • Katherine Malan University of South Africa
  • Arnaud Liefooghe University of Littoral, France
  • Sébastien Verel Univ. Littoral Côte d'Opale, France
  • Gabriela Ochoa University of Stirling, UK
LLMfwEC — Large Language Models for and with Evolutionary Computation Workshop
  • Erik Hemberg Massachusetts Institute of Technology, CSAIL, Cambridge, USA
  • Roman Senkerik Tomas Bata University in Zlin, Faculty of Applied Informatics, Czech Republic
  • Joel Lehman IT University of Copenhagen
  • Una-May O’Reilly MIT, USA
  • Pier Luca Lanzi Politecnico di Milano
  • Michal Pluhacek Tomas Bata University in Zlin, A.I.Lab, Czech Republic
  • Tome Eftimov Jožef Stefan Institute, Slovenia
NEWK — Neuroevolution at work
  • Ernesto Tarantino Institute on High Performance Computing - National Research Council of Italy
  • De Falco Ivanoe Institute of High-Performance Computing and Networking (ICAR-CNR), ITALY
  • Antonio Della Cioppa Natural Computation Lab, DIEM, University of Salerno, ITALY
  • Edgar Galvan Naturally Inspired Computation Research Group, Computer Science, Maynooth University, Ireland
  • Scafuri Umberto Institute of High-Performance Computing and Networking (ICAR-CNR), ITALY
  • Mengjie Zhang Victoria University of Wellington, New Zealand
QuantOpt — Quantum Optimization
  • Alberto Moraglio University of Exeter, UK
  • Mayowa Ayodele D-wave Quantum Inc
  • Francisco Chicano University of Malaga, Spain
  • Ofer Shir Tel-Hai College and Migal Institute, Israel
  • Lee Spector Amherst College, Hampshire College, and the University of Massachusetts, Amherst
  • Matthieu Parizy Fujitsu Limited, Japan
  • Markus Wagner Monash University, Australia
SAEOpt — Workshop on Surrogate-Assisted Evolutionary Optimisation
  • Alma Rahat Swansea University
  • Richard Everson University of Exeter
  • Jonathan Fieldsend University of Exeter, UK
  • Handing Wang Xidian University, China
  • Yaochu Jin Bielefeld University, Germany
  • Tinkle Chugh University of Exeter, UK
SWINGA — Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges
  • Roman Senkerik Tomas Bata University in Zlin, Faculty of Applied Informatics, Czech Republic
  • Ivan Zelinka VSB - Technical University of Ostrava
  • Pavel Kromer VSB Technical University of Ostrava, Czech Republic
  • Swagatam Das Indian Statistical Institute
SymReg — Symbolic Regression
  • Gabriel Kronberger University of Applied Sciences Upper Austria
  • William La Cava Harvard, Boston Children’s Hospital, USA
  • Steven Gustafson Noonum, Inc

AABOH — Analysing algorithmic behaviour of optimisation heuristics

aaboh.nl

Summary

Optimisation and Machine Learning tools are among the most used tools in the modern world with their omnipresent computing devices. Yet, while both these tools rely on search processes (search for a solution or a model able to produce solutions), their dynamics have not been fully understood. This scarcity of knowledge on the inner workings of heuristic methods is largely attributed to the complexity of the underlying processes, which cannot be subjected to a complete theoretical analysis. However, this is also partially due to a superficial experimental setup and, therefore, a superficial interpretation of numerical results. In fact, researchers and practitioners typically only look at the final result produced by these methods. Meanwhile, a great deal of information is wasted in the run. In light of such considerations, it is now becoming more evident that such information can be useful and that some design principles should be defined that allow for online or offline analysis of the processes taking place in the population and their dynamics. Hence, with this workshop, we call for both theoretical and empirical achievements identifying the desired features of optimisation and machine learning algorithms, quantifying the importance of such features, spotting the presence of intrinsic structural biases and other undesired algorithmic flaws, studying the transitions in algorithmic behaviour in terms of convergence, any-time behaviour, traditional and alternative performance measures, robustness, exploration vs exploitation balance, diversity, algorithmic complexity, etc., with the goal of gathering the most recent advances to fill the aforementioned knowledge gap and disseminate the current state-of-the-art within the research community. Thus, we encourage submissions exploiting carefully designed experiments or data-heavy approaches that can come to help in analysing primary algorithmic behaviours and modelling internal dynamics causing them.

Workshop format: invited talks, paper presentations, and a panel discussion.

Organizers

Anna V Kononova

Anna V. Kononovais an Assistant Professor at the Leiden Institute of Advanced ComputerScience. She received her MSc degree in Applied Mathematics from Yaroslavl State University (Russia) in 2004 and PhD degree in Computer Science from University of Leeds (UK) in 2010. After a total of 5 years of postdoctoral experiences at Technical University Eindhoven (The Netherlands) and Heriot-Watt University (Edinburgh, UK), Anna has spent a number of years working as a mathematician in industry. Her current research interests include analysis of optimisation algorithms and machine learning.

 

Niki van Stein

Niki van Stein received her PhD degree in Computer Science in 2018, from the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands. From 2018 until 2021 she was a Postdoctoral Researcher at LIACS, Leiden University and she is currently an Assistant Professor at LIACS. Her research interests lie in explainable AI for EC and ML, surrogate-assisted optimisation and surrogate-assisted neural architecture search, usually applied to complex industrial applications.

Daniela Zaharie

Daniela Zaharie is a Professor at the Department of Computer Science from the West University of Timisoara (Romania) with a PhD degree on a topic related to stochastic modelling of neural networks and a Habilitation thesis on the analysis of the behaviour of differential evolution algorithms. Her current research interests include analysis and applications of metaheuristic algorithms, interpretable machine learning models and data mining.

Fabio Caraffini

Fabio Caraffini is an Associate Professor in Computer Science at De Montfort University (Leicester, UK). Fabio holds a PhD in Computer Science (De Montfort University, UK, 2014) and a PhD in Mathematical Information Technology (University of Jyväkylä, Finland, 2016) and was awarded a BSc in ``Electronics Engineering and an MSc in ``Telecommunications Engineering by the University of Perugia (Italy) in 2008 and 2011 respectively. His research interests include theoretical and applied computational intelligence with a strong emphasis on metaheuristics for optimisation.

Thomas Bäck

Thomas Bäck is Full Professor of Computer Science at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands, where he is head of the Natural Computing group since 2002. He received his PhD (adviser: Hans-Paul Schwefel) in computer science from Dortmund University, Germany, in 1994, and then worked for the Informatik Centrum Dortmund (ICD) as department leader of the Center for Applied Systems Analysis. From 2000 - 2009, Thomas was Managing Director of NuTech Solutions GmbH and CTO of NuTech Solutions, Inc. He gained ample experience in solving real-life problems in optimization and data mining through working with global enterprises such as BMW, Beiersdorf, Daimler, Ford, Honda, and many others. Thomas Bäck has more than 350 publications on natural computing, 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 most recently, the Handbook of Natural Computing. He is also editorial board member and associate editor of a number of journals on evolutionary and natural computing. Thomas received the best dissertation award from the German Society of Computer Science (Gesellschaft für Informatik, GI) in 1995 and the IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award in 2015.

BENCH@GECCO24 — Good Benchmarking Practices for Evolutionary Computation

https://sites.google.com/view/benchmarking-network/home/activities/gecco-2024-workshop

Summary

Benchmarking plays a vital role in understanding the performance and search behavior of sampling-based optimization techniques such as evolutionary algorithms. This workshop will continue our workshop series on good benchmarking practices at different conferences in the context of EC that we started in 2020. The core theme is on benchmarking evolutionary computation methods and related sampling-based optimization heuristics, but each year, the focus is changed.

The focus starting in 2024 will be on benchmarking in different EC subfields. This goes back to the origin of the workshop as it was primarily intended to highlight benchmarking in these subfields with the intention to learn from each other and, thus, improve benchmarking in all areas.

Two EC subfields have been identified for the GECCO 2024 workshop being

1) Quality Diversity (QD) and
2) Multi-Criteria Optimization (MCO).

While the first is rather new within the family of EC subfields, the later is an established subfield with its own well-known mainstream algorithms and an established conference series. Nevertheless, both fields have several things in common, in particular as quality and diversity are regularly two conflicting goals during the process of optimization.

The workshop organizers plan to invite one highly esteemed researcher from each of the above fields to provide an overview on the latest progress and developments on benchmarking in the fields. The decent time allowed for discussion after each presentation is expected to be used for bridging the gap between the subfields and to see what one field can learn from the other, in particular with respect to benchmarking.

Organizers

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.

Carola Doerr

Carola Doerr, formerly Winzen, is a CNRS research director at Sorbonne Université in Paris, France. Carola's main research activities are in the analysis of black-box optimization algorithms, both by mathematical and by empirical means. Carola is associate editor of IEEE Transactions on Evolutionary Computation, ACM Transactions on Evolutionary Learning and Optimization (TELO) and board member of the Evolutionary Computation journal. She is/was program chair for the BBSR track at GECCO 2024, the GECH track at GECCO 2023, for PPSN 2020, FOGA 2019 and for the theory tracks of GECCO 2015 and 2017. She has organized Dagstuhl seminars and Lorentz Center workshops. Together with Pascal Kerschke, Carola leads the 'Algorithm selection and configuration' working group of COST action CA22137. Carola's works have received several awards, among them the CNRS bronze medal, the Otto Hahn Medal of the Max Planck Society, best paper awards at GECCO, CEC, and EvoApplications.

Pascal Kerschke

Pascal Kerschke is professor of Big Data Analytics in Transportation at TU Dresden, Germany. His research interests cover various topics in the context of benchmarking, data science, machine learning, and optimization - including automated algorithm selection, Exploratory Landscape Analysis, as well as continuous single- and multi-objective optimization. Moreover, he is the main developer of 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 and the COSEAL group.

 

Mike Preuss

Mike Preuss is assistant professor at LIACS, the Computer Science department of Leiden University. He works in AI, namely game AI, natural computing, and social media computing. Mike received his PhD in 2013 from the Chair of Algorithm Engineering at TU Dortmund, Germany, and was with ERCIS at the WWU Muenster, Germany, from 2013 to 2018. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multi-modal and multi-objective optimization, and on computational intelligence and machine learning methods for computer games. Recently, he is also involved in Social Media Computing, and he is publications chair of the upcoming multi-disciplinary MISDOOM conference 2019. He is associate editor of the IEEE ToG journal and has been member of the organizational team of several conferences in the last years, in various functions, as general co-chair, proceedings chair, competition chair, workshops chair.

Vanessa Volz

Vanessa Volz is an AI researcher at modl.ai (Copenhagen, Denmark), with focus in computational intelligence in games. She received her PhD in 2019 from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. She holds B.Sc. degrees in Information Systems and in Computer Science from WWU Münster, Germany. She received an M.Sc. with distinction in Advanced Computing: Machine Learning, Data Mining and High Performance Computing from University of Bristol, UK, in 2014. Her current research focus is on employing surrogate-assisted evolutionary algorithms to obtain balance and robustness in systems with interacting human and artificial agents, especially in the context of games.

Olaf Mersmann

Olaf Mersmann is a Professor for Data Science at TH Köln - University of Applied Sciences. He received his BSc, MSc and PhD in Statistics from TU Dortmund. His research interests include using statistical and machine learning methods on large benchmark databases to gain insight into the structure of the algorithm choice problem.

EC + DM — Evolutionary Computation and Decision Making

https://sites.exeter.ac.uk/ecmcdm/

Summary

Solving real-world optimisation problems typically involve an expert or decision-maker. Decision making (DM) 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 DM techniques for several applications. This has raised amongst others the need to account for explainability, fairness, ethics and privacy aspects in optimisation and DM. This workshop will showcase research that is at the interface of EC and DM.

The workshop on Evolutionary Computation and Decision Making (EC + DM) to be held in GECCO 2024 aims to promote research on theory and applications in the field. Topics of interest include:

• Interactive multiobjective optimisation or decision-maker in the loop
• Visualisation to support DM in EC
• Aggregation/trade-off operators & algorithms to integrate decision maker preferences
• Fuzzy logic-based DM techniques
• Bayesian and other DM techniques
• Interactive multiobjective optimisation for (computationally) expensive problems
• Using surrogates (or metamodels) in DM
• Hybridisation of EC and DM
• Scalability in EC and DM
• DM and machine learning
• DM in a big data context
• DM in real-world applications
• Use of psychological tools to aid the decision-maker
• Fairness, ethics and societal considerations in EC and DM
• Explainability in EC and DM
• Accounting for trust and security in EC and DM

Organizers

Tinkle Chugh

Dr Tinkle Chugh is a Lecturer in Computer Science at the University of Exeter. He is the Associate Editor of the Complex and Intelligent Systems journal. Between Feb 2018 and June 2020, he worked as a Postdoctoral Research Fellow in the BIG data methods for improving windstorm FOOTprint prediction project funded by Natural Environment Research Council 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 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 Allmendinger is Professor of Applied AI at the Alliance Manchester Business School (AMBS) and Associate Dean for Business Engagement of the Faculty of Humanities, The University of Manchester (UoM), and Turing Fellow at the Alan Turing Institute. Richard is also an Advisor for River Capital Ltd, and Senior Scientist at Eharo Ltd. His expertise is in the development and application of sequential decision-making methods to problems with multiple objectives, uncertainties and resourcing issues arising in areas such as healthcare, manufacturing, engineering, music, sports, and finance. Richard has attracted £39M+ in grant funding as PI/co- I from UKRI, industry, and other sources, is an Editor for several AI journals, and has served in numerous chair roles for different AI conferences. He is an External Examiner at Warwick Business School and a former UoM Director of the ESRC- funded CDT in Data Analytics & Society.

Hadi Akbarzadeh Khorshidi

Hadi A. Khorshidi is a Senior Research Fellow in Cancer Health Service Research and an Adjunct Senior Fellow in School of Computing and Information Systems at the University of Melbourne. He has extensive research experiences in medical data mining, optimisation, machine learning, and uncertainty. He completed his PhD in Applied and Computational Mathematics at Monash University in 2016. He is an Associate Investigator at ARC training centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTiMA). He has published more than 55 peer-reviewed journal articles and conference papers (Google citations 980+, H-index 18). He is a chief investigator in a joint research project awarded by Manchester-Melbourne Research Fund, and recipient of funding for Frailty Dynamic Simulation Modelling from NSW Agency for Clinical Innovation. He is an associate editor in International Journal of System Assurance Engineering and Management. He has been a member of editorial boards in International Journal of Quality and Reliability Management and The TQM Journal. He has served as a guest editor for IEEE Transactions on Evolutionary Computation (IEEE TEVC) and Information Systems and Operations Research (INFOR).

ECADA 2024 — 14th Workshop on Evolutionary Computation for the Automated Design of Algorithms

https://bonsai.auburn.edu/ecada/

Summary

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.


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 R. Tauritz is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University (AU), the Director for National Laboratory Relationships in AU's Samuel Ginn College of Engineering, the founding Head of AU’s Biomimetic Artificial Intelligence Research Group (BioAI Group), the founding director of AU’s Biomimetic National Security Artificial Intelligence Laboratory (BONSAI Lab), 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, competitive coevolution, and parameter control, and the application of computational intelligence techniques in security and defense. He was granted a US patent for an artificially intelligent rule-based system to assist teams in becoming more effective by improving the communication process between team members.

John R. Woodward

John Woodward is a Reader and Head of Computer Science at Loughborough University. He has organized workshops at GECCO including Metaheuristic Design Patterns and ECADA, Evolutionary Computation for the Automated Design of Algorithms which has run for 8 years. He has also given tutorials on the same topic at PPSN, CEC, and GECCO. He currently holds a grant examining how Genetic Improvement techniques can be used to adapt scheduling software for airport runways. With his PhD Student, Saemundur Haraldsson (who this proposal is in collaboration with), won a best paper award in a GI workshop at GECCO. He has also organized a GI workshop at UCL as part of their very successful Crest Open Workshops.

Emma Hart

Prof. Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems. She moved to Edinburgh Napier University in 2000 as a lecturer, and was promoted to a Chair in 2008 where she leads a group in Nature-Inspired Intelligent Systems, specialising in optimisation and learning algorithms applied in domains that range from combinatorial optimisation to robotics. Her work mainly involves development of algorithms inspired by biological evolution to discover novel solutions to challenging problems. She was appointed as Editor-in-Chief of Evolutionary Computation (MIT Press) in 2017. She has been invited to give keynotes at major international conferences including CLAIO 2020, IEEE CEC 2019, EURO 2016 and UKCI 2015 and was General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She is an elected member of the Executive Board of the ACM SIG on Evolutionary Computation. More broadly, she invited member of the UK Operations Research Society Research Panel, and in Scotland, co-leads the Artificial Intelligence theme within SICSA. She was appointed as a panel member for REF2021 (UoA11 Computer Science). In 2020 she was appointed to the Steering Committee that developed Scotland's AI Strategy published in 2021 . She has a sustained track record of obtaining funding from the EU, EPSRC and of engaging with industry via KTP projects and consultancy, and participates enthusiastically in public-engagement activity, e.g Pint of Science. Her work in evolutionary robotics has attracted significant media attention, e.g. in New Scientist, the Guardian, Telegraph and the Conversation. In 2021, she gave a TED Talk on Evolutionary Robotics, available online

ECXAI — Evolutionary Computing and Explainable AI

https://ecxai.github.io/ecxai/

Summary

‘Explainable AI’ is an umbrella term that covers research on methods designed to provide human-understandable explanations of the decisions made/knowledge captured by AI models. Within the AI field, this is currently a very active research area. Evolutionary Computation (EC) draws from concepts found in nature to drive development in evolution-based systems such as genetic algorithms and evolution systems. Alongside other nature-inspired metaheuristics, such as swarm intelligence, the path to a solution is driven by stochastic processes. This creates barriers to explainability: algorithms may return different solutions when re-run from the same input and technical descriptions of these processes are often a barrier to end-user understanding and acceptance. On the other hand, very often XAI methods require the fitting of some kind of model, and hence EC methods have the potential to play a role in this area. This workshop will focus on the bidirectional interplay between XAI and EC. That is, how XAI can help EC research, and how EC can be used within XAI methods.

Recent growth in the adoption of black-box solutions including EC-based methods into domains such as medical diagnosis, manufacturing, and transport & logistics has led to greater attention being given to the generation of explanations and their accessibility to end-users. This increased attention has helped create a fertile environment for the application of XAI techniques in the EC domain for both end-user and researcher-focused explanation generation. Furthermore, many approaches to XAI in machine learning are based on search algorithms (e.g., Local Interpretable Model-Agnostic Explanations / LIME) that have the potential to draw on the expertise of the EC community; and many of the broader questions (such as what kinds of explanation are most appealing or useful to end users) are faced by XAI researchers in general.

From an application perspective, important questions have arisen, for which XAI may be crucial: Is the system biased? Has the problem been formulated correctly? Is the solution trustworthy and fair? The goal of XAI and related research is to develop methods to interrogate AI processes with the aim of answering these questions. This can support decision-makers while also building trust in AI decision-support through more readily understandable explanations.

Proposed Content

We seek contributions on a range of topics relating evolutionary computation (in all its forms) with explainability. Topics of interest include but are not limited to:
· Interpretability vs explainability in EC and their quantification
· Landscape analysis and XAI
· Contributions of EC to XAI in general
· Use of EC to generate explainable/interpretable models
· XAI in real-world applications of EC
· Possible interplay between XAI and EC theory
· Applications of existing XAI methods to EC
· Novel XAI methods for EC
· Legal and ethical considerations
· Case studies / applications of EC & XAI technologies

Organizers

John McCall

John McCall is Head of Research for the National Subsea Centre at Robert Gordon University. He has researched in machine learning, search and optimisation for 25 years, making novel contributions to a range of nature-inspired optimisation algorithms and predictive machine learning methods, including EDA, PSO, ACO and GA. He has 150+ peer-reviewed publications in books, international journals and conferences. These have received over 2400 citations with an h-index of 22. John and his research team specialise in industrially-applied optimization and decision support, working with major international companies including BT, BP, EDF, CNOOC and Equinor as well as a diverse range of SMEs. Major application areas for this research are: vehicle logistics, fleet planning and transport systems modelling; predictive modelling and maintenance in energy systems; and decision support in industrial operations management. John and his team attract direct industrial funding as well as grants from UK and European research funding councils and technology centres. John is a founding director and CEO of Celerum, which specialises in freight logistics. He is also a founding director and CTO of PlanSea Solutions, which focuses on marine logistics planning. John has served as a member of the IEEE Evolutionary Computing Technical Committee, an Associate Editor of IEEE Computational Intelligence Magazine and the IEEE Systems, Man and Cybernetics Journal, and he is currently an Editorial Board member for the journal Complex And Intelligent Systems. He frequently organises workshops and special sessions at leading international conferences, including several GECCO workshops in recent years.

Jaume Bacardit

Jaume Bacardit is Reader in Machine Learning at Newcastle University in the UK. He has receiveda BEng, MEng in Computer Engineering and a PhD in Computer Science from Ramon Llull University, Spain in 1998, 2000 and 2004, respectively. Bacardit’s research interests include the development of machine learning methods for large-scale problems, the design of techniques to extract knowledge and improve the interpretability of machine learning algorithms, known currently as Explainable AI, and the application of these methods to a broad range of problems, mostly in biomedical domains. He leads/has led the data analytics efforts of several large interdisciplinary consortiums: D-BOARD (EU FP7, €6M, focusing on biomarker identification), APPROACH (EI-IMI €15M, focusing on disease phenotype identification) and PORTABOLOMICS (UK EPSRC £4.3M focusing on synthetic biology). Within GECCO he has organised several workshops (IWLCS 2007-2010, ECBDL’14), been co-chair of the EML track in 2009, 2013, 2014, 2020 and 2021, and Workshops co-chair in 2010 and 2011. He has 90+ peer-reviewed publications that have attracted 4600+ citations and a H-index of 31 (Google Scholar).

Alexander Brownlee

Alexander (Sandy) Brownlee is a Lecturer in the Division of Computing Science and Mathematics at the University of Stirling. His main topics of interest are in search-based optimisation methods and machine learning, with a focus on decision support tools, and applications in civil engineering, transportation and software engineering. He has published over 70 peer-reviewed papers on these topics. He has worked with several leading businesses including BT, KLM, and IES on industrial applications of optimisation and machine learning. He serves as a reviewer for several journals and conferences in evolutionary computation, civil engineering and transportation, and is currently an Editorial Board member for the journal Complex And Intelligent Systems. He has been an organiser of several workshops and tutorials at GECCO, CEC and PPSN on genetic improvement of software.

Stefano Cagnoni

Stefano Cagnoni graduated in Electronic Engineering at the University of Florence, Italy, where he also obtained a PhD in Biomedical Engineering and was a postdoc until 1997. In 1994 he was a visiting scientist at the Whitaker College Biomedical Imaging and Computation Laboratory at the Massachusetts Institute of Technology. Since 1997 he has been with the University of Parma, where he has been Associate Professor since 2004. Recent research grants include: a grant from Regione Emilia-Romagna to support research on industrial applications of Big Data Analysis, the co-management of industry/academy cooperation projects: the development, with Protec srl, of a computer vision-based fruit sorter of new generation and, with the Italian Railway Network Society (RFI) and Camlin Italy, of an automatic inspection system for train pantographs; a EU-funded “Marie Curie Initial Training Network" grant for a four-year research training project in Medical Imaging using Bio-Inspired and Soft Computing. He has been Editor-in-chief of the "Journal of Artificial Evolution and Applications" from 2007 to 2010. From 1999 to 2018, he was chair of EvoIASP, an event dedicated to evolutionary computation for image analysis and signal processing, then a track of the EvoApplications conference. From 2005 to 2020, he has co-chaired MedGEC, a workshop on medical applications of evolutionary computation at GECCO. Co-editor of journal special issues dedicated to Evolutionary Computation for Image Analysis and Signal Processing. Member of the Editorial Board of the journals “Evolutionary Computation” and “Genetic Programming and Evolvable Machines”. He has been awarded the "Evostar 2009 Award" in recognition of the most outstanding contribution to Evolutionary Computation.

Giovanni Iacca

Giovanni Iacca is an Associate Professor in Information Engineering at the Department of Information Engineering and Computer Science of the University of Trento, Italy, where he founded the Distributed Intelligence and Optimization Lab (DIOL). Previously, he worked as a postdoctoral researcher in Germany (RWTH Aachen, 2017-2018), Switzerland (University of Lausanne and EPFL, 2013-2016), and The Netherlands (INCAS3, 2012-2016), as well as in industry in the areas of software engineering and industrial automation. He is co-PI of the PATHFINDER-CHALLENGE project "SUSTAIN" (2022-2026). Previously, he was co-PI of the FET-Open project "PHOENIX" (2015-2019). He has received two best paper awards (EvoApps 2017 and UKCI 2012). His research focuses on computational intelligence, distributed systems, and explainable AI applied e.g. to medicine. In these fields, he co-authored more than 150 peer-reviewed publications. He is actively involved in organizing tracks and workshops at some of the top conferences on computational intelligence, and he regularly serves as a reviewer for several journals and conference committees. He is an Associate Editor for IEEE Transactions on Evolutionary Computation, Applied Soft Computing, and Frontiers in Robotics and AI.

 

David Walker

David Walker is a Senior Lecturer in Computer Science at the University of Exeter. He obtained a PhD in Computer Science in 2013 for work on visualising solution sets in many-objective optimisation. His research focuses on developing new approaches to solving hard optimisation problems with Evolutionary Algorithms (EAs), as well as identifying ways in which the use of Evolutionary Computation can be expanded within industry, and he has published journal papers in all of these areas. His recent work considers the visualisation of algorithm operation, providing a mechanism for visualising algorithm performance to simplify the selection of EA parameters. While working as a postdoctoral research associate at the University of Exeter his work involved the development of hyper-heuristics and, more recently, investigating the use of interactive EAs in the water industry. Since joining Plymouth Dr Walker’s research group includes a number of PhD students working on optimisation and machine learning projects. He is active in the EC field, having run an annual workshop on visualisation within EC at GECCO since 2012 in addition to his work as a reviewer for journals such as IEEE Transactions on Evolutionary Computation, Applied Soft Computing, and the Journal of Hydroinformatics. He is a member of the IEEE Taskforce on Many-objective Optimisation.

EEAI — Embodied and Evolved Artificial Intelligence

Summary

Embodied Artificial Intelligence is a cutting-edge field at the intersection of AI, robotics, and bioengineering. One of the fundamental challenges is how to infuse artificial systems, e.g., robotics, with intelligence and the ability to produce rich, emergent interactions with their environments. The philosophy of embodied cognition is the driving force behind this: that intelligence is intrinsically intertwined with embodiment, and that biological or artificial agents must be embodied and interact in tangible physical and social realms to exhibit genuine intelligence. A standout success of application of this vision is in soft robotics, where evolutionary computing continues to be successfully applied to explore questions around the proper integration of sensing, passive and active mechanics, movement, and control into anatomies retaining global compliance and deformability. However, we note that soft robotics is not the only field in which this philosophy can have impact.

Motivated by the great potential of evolutionary paradigms and the various links to embodiment and intelligence, this workshop aims to bridge the gap between Embodied Artificial Intelligence and Evolutionary Computation research and to elucidate how the notion of embodiment as a design paradigm aligns with and enriches the entire domain of evolutionary computation. The core idea of our workshop is to drive discussion around the interactions between evolutionary computation and embodied cognition, using soft robotics as a touchpoint. Through carefully selected invited talks and the solicitation of cutting-edge presentations covering both theory and practice, we strive to catalyze research that fully explores the potential of embodied artificial intelligence as a problem-solving tool and philosophy. We will further support researchers to bring physical items and demonstrations to the workshop, to provide talking points and illustrative examples to drive exploration of this topic area.

We will invite a broad church of interested researchers, underpinned by world-leading invited speakers, to explore fundamental questions and form a research vision together with a series of actionable points to further develop the key takeaways from the workshop following GECCO. We especially encourage younger researchers and early-career researchers to participate in the discussions and present their work in the workshop.

List of potential topics:
• Integration of evolutionary approaches with traditional design techniques
• Bio-inspired Soft Robot Evolution
• Embodied and mechanical intelligence in soft robots
• Real-world embodied AI through bio-inspired mechanisms

Organizers

Yue Xie

Yue Xie is a Marie Sklodowska-Curie Future Roads Fellow in the University of Cambridge. She received her Ph.D. in Computer Science from the University of Adelaide in 2021, where her research focused on evolutionary computation through both theoretical analysis and real-world applications. Prior to joining University of Cambridge, she held a postdoctoral research fellowship at the Optimization and Logistics group at the University of Adelaide, and a CERC Post-doctoral Fellow in the CSIRO Data61. Yue’s research interested are the embodied artificial intelligence and bio-inspired optimization and she has applied knowledge to tackle real-world problems, such as smart traffic and soft robotics.

 

David Howard

David leads the Robotic Design and Interaction Group and is a Principal Research Scientist in the Cyber Physical Systems program at CSIRO, Australia's national science body. He leads multiple projects at the intersection of soft robotics, evolutionary machine learning, and the computational design of novel physical objects. He currently leads the AI4Design portfolio. His interests include nature-inspired algorithms, learning, autonomy, soft robotics, the reality gap, and evolution of form. His work has been featured in local and national media.
He received his BSc in Computing from the University of Leeds in 2005, and the MSc in Cognitive Systems at the same institution in 2006. In 2011 he received his PhD from the University of the West of England. He is a member of the IEEE and ACM, and an avid proponent of education, STEM, and outreach activities. His work has been published in IEEE and Nature journals.

 

Fumiya Iida

Fumiya Iida is a Professor of Robotics at Department of Engineering, University of Cambridge. He received his bachelor and master degrees in mechanical engineering at Tokyo University of Science in Japan, and Dr. sc. nat. in Informatics at University of Zurich in Switzerland. During his PhD project, he was also engaged in biomechanics research of human locomotion at Locomotion Laboratory, University of Jena in Germany. While he worked as a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology in USA, he awarded the Fellowship for Prospective Researchers from the Swiss National Science Foundation, and then, the Swiss National Science Foundation Professorship hosted by ETH Zurich. In 2014 he moved to the University of Cambridge as the director of Bio-Inspired Robotics Laboratory.

 

Josie Hughes

Josie Hughes is an Assistant Professor at EPFL where she established the CREATE Lab in the Institute of Mechanical Engineering in 2021. She undertook her undergraduate, masters and PhD studies at the University of Cambridge as part of the Sensor CDT, joining the Bio-inspired Robotics Lab (BIRL). Her PhD focused on examining the role of passivity in bio-inspired manipulators, and methodologies for exploiting morphology for soft large area soft sensing. Following this, she worked as a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology in USA in the Distributed Robotics Lab. Her research focuses on developing novel design paradigms for designing robot structures that exploit their physicality and interactions with the environment. This includes the development of robotic hands, soft manipulators and automation systems for applications focused on sustainability and science. Her group explore applications for agri-food, human collaboration, robot scientists and also environmental monitoring. Her work has been published in journals including Science Robotics and Nature Machine Intelligence, and she has won numerous International Robotics Competitions Awards.

EGML-EC — 3rd GECCO workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML-EC) 2024

https://sites.google.com/view/egml-ec2024

Summary

Generative Machine Learning has become a key field in machine learning and deep learning. In recent years, this field of research has proposed many deep generative models (DGMs) that range from a broad family of methods such as large language models (LLMs), generative adversarial networks (GANs), variational autoencoders (VAEs), Transformers, autoregressive (AR) models and stable diffusion models (SD). Although these methods have achieved state-of-the-art results in the generation of synthetic data of different types, such as images, speech, text, molecules, video, etc., Deep generative models are still difficult to train, optimize, and fine tune.

There are still open problems, such as the vanishing gradient and mode collapse in DGMs, which limit their performance. Although there are strategies to minimize the effect of those problems, they remain fundamentally unsolved. In recent years, evolutionary computation (EC) and related bio-inspired techniques (e.g. particle swarm optimization) and in the form of Evolutionary Machine Learning approaches have been successfully applied to mitigate the problems that arise when training DGMs, leveraging the quality of the results to impressive levels. Among other approaches, these new solutions include LLM, GAN, VAE, AR, and SD training methods or fine tuning optimization based on evolutionary and coevolutionary algorithms, the combination of deep neuroevolution with training approaches, and the evolutionary exploration of latent space.

The workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML-EC) aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for researchers focused on DGMs and the EC community. Bringing these two communities together will be essential for making significant advances in this research area. Thus, this workshop provides a critical forum for disseminating the experience on the topic of enhancing generative modelling with EC, presenting new and ongoing research in the field, and to attract new interest from our community.



Particular topics of interest are (not exclusively):
.Evolutionary prompt optimization for large language models
.Evolutionary operators based on large language models
.Evolutionary and co-evolutionary algorithms to train deep generative models
.EC-based optimization of hyper-parameters for deep generative models
.Neuroevolution applied to train deep generative architectures
.Dynamic EC-based evolution of deep generative models training parameters
.Evolutionary latent space exploration (e.g. LVEs)
.Real-world applications of EC-based deep generative models solutions
.Multi-criteria adversarial training of deep generative models
.Evolutionary generative adversarial learning models
.Software libraries and frameworks for deep generative models applying EC

Organizers

 

Jamal Toutouh

Jamal Toutouh is a Researcher Assistant Professor at the University of Málaga (Spain). Previously, he was a Marie Skłodowska Curie Postdoctoral Fellow at Massachusetts Institute of Technology (MIT) in the USA, at the MIT CSAIL Lab. He obtained his Ph.D. in Computer Engineering at the University of Malaga (Spain), which was awarded the 2018 Best Spanish Ph.D. Thesis in Smart Cities. His dissertation focused on the application of Machine Learning methods inspired by Nature to address Smart Mobility problems. His current research explores the combination of Nature-inspired gradient-free and gradient-based methods to address Generative Modelling and Adversarial Machine Learning. The main idea is to devise new algorithms to improve the efficiency and efficacy of the state-of-the-art methodology by mainly applying evolutionary computation and related techniques, such as particle swarm optimization in the form of Evolutionary Machine Learning approaches. Besides, he is on the application of Machine Learning to address problems related to Smart Mobility, Smart Cities, and Climate Change.

Una-May O’Reilly

Dr. Una-May O'Reilly is the leader of ALFA Group at Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Lab. An evolutionary computation researcher for 20+ years, she is broadly interested in adversarial intelligence — the intelligence that emerges and is recruited while learning and adapting in competitive settings. Her interest has led her to study settings where security is under threat, for which she has developed machine learning algorithms that variously model the arms races of tax compliance and auditing, malware and its detection, cyber network attacks and defenses, and adversarial paradigms in deep learning. She is passionately interested in programming and genetic programming. She is a recipient of the EvoStar Award for Outstanding Achievements in Evolutionary Computation in Europe and the ACM SIGEVO Award Recognizing Outstanding Achievements in Evolutionary Computation. Devoted to the field and committed to its growth, she served on the ACM SIGEVO executive board from SIGEVO's inception and held different officer positions before retiring from it in 2023. She co-founded the annual workshops for Women@GECCO and has proudly watched their evolution to Women+@GECCO. She was on the founding editorial boards and continues to serve on the editorial boards of Genetic Programming and Evolvable Machines, and ACM Transactions on Evolutionary Learning and Optimization. She has received a GECCO best paper award and an GECCO test of time award. She is honored to be a member of SPECIES, a member of the Julian Miller Award committee, and to chair the 2023 and 2024 committees selecting SIGEVO Awards Recognizing Outstanding Achievements in Evolutionary Computation.

João Correia

João Correia is an Assistant Professor at the University of Coimbra, a researcher of the Computational Design and Visualization Lab. and a member of the Evolutionary and Complex Systems (ECOS) of the Centre for Informatics and Systems of the same university. He holds a PhD in Information Science and Technology from the University of Coimbra and an MSc and BS in Informatics Engineering from the same university. His main research interests include Evolutionary Computation, Machine Learning, Adversarial Learning, Computer Vision and Computational Creativity. He is involved in different international program committees of international conferences in the areas of Evolutionary Computation, Artificial Intelligence, Computational Art and Computational Creativity, and he is a reviewer for various conferences and journals for the mentioned areas, namely GECCO and EvoStar, served as remote reviewer for the European Research Council Grants and is an executive board member of SPECIES. He was also the publicity chair and chair of the International Conference of Evolutionary Art Music and Design conference, currently the publicity chair for EvoStar - The Leading European Event on Bio-Inspired Computation and chair of EvoApplications, the International Conference on the Applications of Evolutionary Computation. Furthermore, he has authored and co-authored several articles at the different International Conferences and journals on Artificial Intelligence and Evolutionary Computation. He is involved in national and international projects concerning Evolutionary Computation, Machine Learning, Generative Models, Computational Creativity and Data Science.

Penousal Machado

Penousal Machado leads the Cognitive and Media Systems group at the University of Coimbra. His research interests include Evolutionary Computation, Computational Creativity, and Evolutionary Machine Learning. In addition to the numerous scientific papers in these areas, his works have been presented in venues such as the National Museum of Contemporary Art (Portugal) and the “Talk to me” exhibition of the Museum of Modern Art, NY (MoMA).

Erik Hemberg

Erik Hemberg is a Research Scientist in the AnyScale Learning For All (ALFA) group at Massachusetts Institute of Technology Computer Science and Artificial Intelligence Lab, USA. He has a PhD in Computer Science from University College Dublin, Ireland and an MSc in Industrial Engineering and Applied Mathematics from Chalmers University of Technology, Sweden. His research interests involves coevolutionary algorithms, grammatical representations and generative models. His work focuses on developing autonomous, pro-active cyber defenses that are anticipatory and adapt to counter attacks. He is also interested in automated semantic parsing of law, and data science for education and healthcare.

EvoOSS — Open Source Software for Evolutionary Computation

https://evooss.heuristiclab.com

Summary

Evolutionary computation (EC) methods are applied in many different domains. Therefore, soundly engineered, reusable, flexible, user-friendly, interoperable, and open software for EC is needed more than ever 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 software is both, time consuming and complex. Consequently, many EC researchers implement custom, highly specialized, closed source and often throw-away software which focuses on a specific research question and is used only once to produce results for the next paper. It is not yet standard in the EC community that the software used to produce the presented results is also made available as open source software in each publication, let alone that this software is also engineered in such a way that others can easily base their research work on it or apply it in practice. This significantly hinders the comparability and reproducibility of research results in the field.

This workshop promotes the development and dissemination of open source software for evolutionary computation and provides a platform for EC researchers to present their latest open source software libraries, frameworks, and tools for the development, analysis, and application of evolutionary algorithms.

Please note that submissions to this workshop will only be accepted if they describe open source software for EC that has already been released and is publically available. The URL to the source code repository must be included in the paper. Therefore, contributions to this workshop have not to be submitted in anonymized form, as the identity of the authors is usually very easy to determine from the repository.

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.

GGP — Graph-based Genetic Programming

https://graphgp.com/

Summary

While the classical way to represent programs in Genetic Programming (GP) is using an expression tree, different GP variants with graph-based representations have been proposed and studied throughout the years. Graph-based representations have led to novel applications of GP in circuit design, cryptography, image analysis, and more. This workshop aims to encourage this form of GP by considering graph-based methods from a unified perspective and to bring together researchers in this subfield of GP research.

The scope of the workshop includes the following GGP-related topics, but is not limited to:
• Genetic operators
• Representation models
• Theoretical results
• Applications
• Implementations
• Search and runtime performance
• Hyperparameter optimization
• Benchmarking
• Self adaption
• Phenotype space and semantic analysis
• Fitness landscape analysis

Organizers

Dennis G. Wilson

Dennis G. Wilson is an Assistant Professor of AI and Data Science at ISAE-SUPAERO in Toulouse, France. He obtained his PhD at the Institut de Recherche en Informatique de Toulouse (IRIT) on the evolution of design principles for artificial neural networks. Prior to that, he worked in the Anyscale Learning For All group in CSAIL, MIT, applying evolutionary strategies and developmental models to the problem of wind farm layout optimization. His current research focuses on genetic programming, neural networks, and the evolution of learning.

 

Roman Kalkreuth

Roman Kalkreuth is a postdoctoral researcher at the CNRS Computer Lab of Paris 6 (LIP6) which belongs to Sorbonne University in Paris, France. His research primarily focuses on the analysis, development and benchmarking of graph-based genetic programming representation models and related algorithms.

 

Eric Medvet

Eric Medvet is an Associate Professor at the University of Trieste, Italy. His research interests include embodied AI, artificial life, and evolutionary optimization.

Giorgia Nadizar

Giorgia Nadizar is a third year PhD student at the University of Trieste, Italy. Her research interests lie at the intersection of embodied AI and explainable/interpretable AI.

Giovanni Squillero

Giovanni Squillero is an associate professor of computer science at Politecnico di Torino, Department of Control and Computer Engineering. His research mixes computational intelligence and machine learning, with particular emphasis on evolutionary computation, bio-inspired meta-heuristics, and multi-agent systems; in more down-to-earth activities, he studies approximate optimization techniques able to achieve acceptable solutions with reasonable resources. The industrial applications of his work range from electronic CAD to bio-informatics. Up to April 2022, he is credited as an author in 3 books, 36 journal articles, 11 book chapters, and 154 papers in conference proceedings; he is also listed among the editors in 16 volumes.

Squillero has been a Senior Member of the IEEE since 2014; currently, he is serving in the technical committee of the IEEE Computational Intelligence Society Games, and in the editorial board of Genetic Programming and Evolvable Machines. He was the program chair of the European Conference on the Applications of Evolutionary Computation in 2016 and 2017, and he is now a member of the EvoApplications steering committee. In 2018 he co-organized EvoML, the workshop on Evolutionary Machine Learning at PPSN; in 2016 and 2017, MPDEA, the workshop on Measuring and Promoting Diversity in Evolutionary Algorithms at GECCO; and from 2004 to 2014, EvoHOT, the Workshops on Evolutionary Hardware Optimization Techniques.

Since 1998, Squillero lectured 66 university courses (15 Ph.D. and 51 M.S./B.Sc.; 36 in Italian and 30 in English); he contributed to additional 37 courses as an assistant or in other subsidiary roles. He was given the opportunity to present his research in 14 international events among invited talks, seminars and tutorials.

Alberto Tonda

Alberto Tonda received his Ph.D. degree in Computer Science Engineering from Politecnico di Torino, Italy, in 2011. Currently, he is a Permanent Researcher (CRCN) at the National Institute of Research for Agriculture and Environment (INRAE), and Université Paris-Saclay, Paris, France. His research interests include semi-supervised modeling of complex systems, evolutionary optimization and machine learning, with main applications in food science and biology. He led COST Action CA15118 FoodMC, a 4-year European networking project on in-silico modelling in food science. He published over 30 contributions in peer-reviewed journals, and over 60 conference papers. He was part of the program committee of 10 conferences of the domain, and he is currently an editorial board member of the journal Genetic Programming and Evolvable Machines.

Yuri Lavinas

I’m a PhD candidate, in the final year, at the University of Tsukuba, Japan. My research interests are related to Computational Intelligence, such as Evolutionary Computation and Artificial Life, especially Evolutionary Algorithms (EC), with a recent focus on multi-objective optimization. I’m engaged in cooperating in projects for using EC as a tool to solve problems in any field.

EC are heuristics based on ideas from biological evolution. The goal of EC is to learn solutions to challenging computational problems using ideas taken from the evolutionary process. EC can solve a few examples of problems: parameter optimization, product design, expert systems design, linear and non-linear regression, and automated control.

Specialities: Genetic Algorithms, Artificial Intelligence, Evolutionary Computation, and Artificial Life.

IAM 2024 — 9th Workshop on Industrial Applications of Metaheuristics (IAM 2024)

https://sites.google.com/view/iam-workshop/home

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, inventory management and others.

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.
As in the previous edition, the workshop together with the rest of the conference will be held in a hybrid mode promoting the participation.

Topic areas of IAM 2024 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.

Organizers

Silvino Fernández Alzueta

He 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

He 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.

IWERL — 27th International Workshop on Evolutionary Rule-based Machine Learning

https://iwlcs.organic-computing.de

Summary

Modern machine learning systems offer significant potential for addressing real-world challenges. However, a notable limitation of the majority of these systems is their ``black-box'' nature. The decision-making process of these models is often difficult to interpret, making it challenging for users to understand how a model arrived at a particular decision. The interpretability of decisions is critical in many real-world applications such as defense, biomedical, and lawsuits. Moreover, many modern systems require extensive memory, huge computational resources, and enormous training data, which can be resource-intensive and hinder their widespread adoption.

Evolutionary rule-based machine learning (ERL) stands out for its ability to provide interpretable decisions. The majority of ERL systems generate niche-based solutions, require less memory, and can be trained using comparatively small data sets. A key factor that makes these models interpretable is the generation of human-readable rules. Consequently, the decision-making process of the ERL systems is interpretable, which is an important step toward eXplainable AI (XAI).

The International Workshop on Evolutionary Rule-based Machine Learning (IWERL), previously known as the International Workshop on Learning Classifier Systems (IWLCS), stands as a cornerstone within the vibrant history of GECCO. Celebrating its 27th edition, IWERL is one of the pioneer and successful workshops at GECCO. This workshop plays an important role in nurturing the future of evolutionary rule-based machine learning. It serves as a beacon for the next generation of researchers, inspiring them to delve deep into evolutionary rule-based machine learning, with a particular focus on Learning Classifier Systems (LCSs).

ERL represents a collection of machine learning techniques that leverage the strengths of various metaheuristics to find an optimal set of rules to solve a problem. These methods have been developed using a diverse array of learning paradigms, including supervised learning, unsupervised learning, and reinforcement learning. ERL encompasses several prominent categories, such as Learning Classifier Systems, Ant-Miner, artificial immune systems, and fuzzy rule-based systems. The modes or model structures of these systems are optimized using evolutionary, symbolic, or swarm-based methods. The hallmark characteristic of the ERL models is their innate comprehensibility, which encompasses traits like explainability, transparency, and interpretability. This property has garnered significant attention within the machine learning community, aligning with the broader interest of Explainable AI.

This workshop is designed to provide a platform for sharing the research trends in the realm of ERL. It aims to highlight modern implementations of ERL systems for real-world applications and to show the effectiveness of ERL in creating flexible and eXplainable AI systems. Moreover, this workshop seeks to attract new interest in this alternative and often advantageous modelling paradigm.

Topics of interest include but are not limited to:

- Advances in ERL methods: local models, problem space partitioning, rule mixing, …

- Applications of ERL: medical, navigation, bioinformatics, computer vision, games, cyber-physical systems, …

- State-of-the-art analysis: surveys, sound comparative experimental benchmarks, carefully crafted reproducibility studies, …

- Formal developments in ERL: provably optimal parametrization, time bounds, generalization, …

- Comprehensibility of evolved rule sets: knowledge extraction, visualization, interpretation of decisions, eXplainable AI, …

- Advances in ERL paradigms: Michigan/Pittsburgh style, hybrids, iterative rule learning, …

- Hyperparameter optimization for ERL: hyperparameter selection, online self-adaptation, …

- Optimizations and parallel implementations: GPU acceleration, matching algorithms, …


Due to the rather disjointed ERL research community, in addition to full papers (8 pages excluding references) on novel ERL research, we plan to allow submission of extended abstracts (2 pages excluding references) that summarize recent high-value ERL research by the authors, showcasing its practical significance. These will then be presented in a dedicated short paper segment with short presentations.

Organizers

Abubakar Siddique

Dr. Siddique's main research lies in creating novel machine learning systems, inspired by the principles of cognitive neuroscience, to provide efficient and scalable solutions for challenging and complex problems in different domains, such as Boolean, computer vision, navigation, and Bioinformatics. He has shared his expertise by delivering five tutorials and talks at various forums, including the Genetic and Evolutionary Computation Conference (GECCO). Additionally, he serves the academic community as an author for prestigious journals and international conferences, including IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, and GECCO.

During his academic journey, Dr. Siddique received the "Student Of The Session" Award, the VUWSA Gold Award, and the "Emerging Research Excellence" Medal. Prior to joining academia, he spent nine years at Elixir Technologies Pakistan, a California (USA) based leading software company. His last designation was a Principal Software Engineer where he led a team of software developers. He developed enterprise-level software for customers such as Xerox, IBM, and Adobe.

Michael Heider

Michael Heider is a doctoral candidate 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/explainability he focuses on compact and simple rule sets. Besides that, his research interests include optimization techniques and unsupervised learning (e.g. for data augmentation or feature extraction). He is an elected organizing committee member of the International Workshop on Learning Classifier Systems since 2021.

 

Muhammad Iqbal

Dr Iqbal has more than 20 years of teaching and research experience in computer science. His main research interests are in the area of computational intelligence. His research focuses on pattern recognition and document recognition problem domains using computational intelligence and transfer learning techniques. Currently, Dr Iqbal is serving as an Assistant Professor at Higher Colleges of Technology in the United Arab Emirates. Previously, he was leading the research and development team at 'Xtracta Limited, New Zealand' to develop artificial intelligent powered information extraction systems capable of extracting data from scanned, photographed, or digital documents with a focus on the financial domain. He has been serving as a peer reviewer for international journals and conferences. To date, he has authored more than 40 international publications, including top journal publications in IEEE Transactions on Evolutionary Computation, Evolutionary Computation, and Pattern Recognition, and two best paper awards at the Genetic and Evolutionary Computation Conference in 2013 and 2014 in the Evolutionary Machine Learning track.

Hiroki Shiraishi

Hiroki Shiraishi was born in Chiba, Japan, in 1999. He received a B.E. degree in informatics and an M.E. degree in informatics from the University of Electro-Communications, Tokyo, Japan, in 2021 and 2023, respectively. Since 2023, he has been a Ph.D. candidate in the Department of Electrical Engineering and Computer Science at Yokohama National University, Yokohama, Japan. From 2023 to 2024, he was a visiting student at the Department of Computer Science and Engineering at the Southern University of Science and Technology, Shenzhen, China. His research interests include fuzzy systems and evolutionary machine learning, with a specific focus on Learning Classifier Systems (LCSs) and Learning Fuzzy Classifier Systems (LFCSs). He received a Best Paper Award at GECCO in 2022 for his work on LCSs and a nomination for the Best Paper Award at GECCO in 2023 for his work on LFCSs. He has been a co-track chair for the International Workshop on Evolutionary Rule-Based Machine Learning (IWERL) at GECCO since 2024.

LAHS — Landscape-Aware Heuristic Search

https://sites.google.com/view/lahs-workshop/

Summary

This workshop will run in hybrid format. 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:

  • Exploiting problem structure
  • Informed search strategies
  • Performance and failure prediction
  • Proposal of new landscape features
  • Applications of landscape analysis to real-world problems


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

Sarah L. Thomson

Sarah L. Thomson is a lecturer at the University of Stirling in Scotland. Her PhD was in fitness landscape analysis, with a strong focus on algorithm performance prediction. She has published extensively in this field and her work has received recognitions of its quality (shortlisted nominee for best SICSA PhD thesis in Scotland; best paper nomination at EvoCOP; being named an outstanding student of EvoSTAR on two occasions). Her research interests include fractal analysis of landscapes, explainable artificial intelligence, and real-world evolutionary computation applications.

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 25 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. She is editor-in-chief of South African Computer Journal, associate editor for Engineering Applications of Artificial Intelligence, and has served as a reviewer for over 20 Web of Science journals.

Arnaud Liefooghe

Arnaud Liefooghe is a Professor of Artificial Intelligence at the University of the Littoral Opal Coast (ULCO), France. He is the co-director of the MODŌ international lab between France and Japan. Previously, he was an associate professor at the University of Lille. In 2010, he was a postdoctoral researcher at the University of Coimbra. In 2020, he was on a CNRS sabbatical leave at JFLI and an Invited Professor at the University of Tokyo. Since 2021, he has been appointed as a Collaborative Professor at Shinshu University, Japan. His research lies in the foundations, design and analysis of local search and evolutionary algorithms, with a particular interest in multi-objective optimization and landscape analysis. He has co-authored over a hundred peer-reviewed scientific papers in international journals and conferences. He was the recipient of a best paper award at EvoCOP 2011, GECCO 2015 and GECCO 2023. He was the co-Program Chair of EvoCOP (in 2018 and 2019) and held various responsibilities for GECCO (Proceedings Chair in 2018, co-EMO Track Chair in 2019, Virtualization Chair in 2021, and co-Hybrid Scheduling Chair in 2023). He is currently the Reproducibility Chair for the ACM Transactions on Evolutionary Learning and Optimization (TELO) and the co-Track Chair of the new Benchmarking, Benchmarks, Software, and Reproducibility (BBSR) track for GECCO 2024.

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 of Computing Science at the University of Stirling in Scotland, UK. Her research lies in the foundations and applications of evolutionary algorithms and metaheuristics, with emphasis on adaptive search, fitness landscape analysis and visualisation. She holds a PhD from the University of Sussex, UK, and has worked at the University Simon Bolivar, Venezuela, and the University of Nottingham, UK. Her Google Scholar h-index is 40, and her work on network-based models of computational search spans several domains and has obtained 4 best-paper awards and 8 other nominations. She collaborates cross-disciplines to apply evolutionary computation in healthcare and conservation. She has been active in organisation and editorial roles in venues such as the Genetic and Evolutionary Computation Conference (GECCO), Parallel Problem Solving from Nature (PPSN), the Evolutionary Computation Journal (ECJ) and the ACM Transactions on Evolutionary Learning and Optimisation (TELO). She is a member of the executive board for the ACM interest group in evolutionary computation, SIGEVO, and the editor of the SIGEVOlution newsletter. In 2020, she was recognised by the leading European event on bio-inspired algorithms, EvoStar, for her outstanding contributions to the field.

LLMfwEC — Large Language Models for and with Evolutionary Computation Workshop

https://sites.google.com/view/llmfwec-2024

Summary

Large language models (LLMs), along with other Foundational Models (generative AI methods), have disrupted conventional expectations of Artificial Intelligence and Machine Learning systems. An LLM processes natural language text prompts as input and responds with the resulting pattern matching and sequence completion with output in natural language text. In contrast, Evolutionary Computation(EC) is inspired by Neo-Darwinian evolution and they conduct black-box search and optimization. What brings these two approaches together?
One answer is evolutionary search heuristics, with operators that use LLMs to fulfill their function. This hybridization turns the conventional paradigm that ECs use on its head, and in turn, sometimes yields high performing, and novel EC systems.
Another answer is using LLM for EC. Many fields have experienced significant growth, with numerous nature-inspired algorithms being developed to solve complex problems. EC has become the target or source of many hybrid approaches and analyses, combinations of the advantages of multiple algorithms, the introduction of adaptive techniques that improve their performance, and special tools. LLMs may help researchers in the selection of feasible candidates from the pool of algorithms based on user- specified goals and provide a basic description of the methods, or propose novel hybrid methods. Further, the models can help identify and describe distinct components suitable for adaptive enhancement, or hybridization, and finally provide a pseudo-code, implementation, and reasoning for the proposed methodology.
This workshop calls for papers at the intersection of EC and LLMs, an area we call "EC with LLM" and "LLM for EC". We invite original research papers discussing from the connection between LLMs and EC. This workshop is focused on algorithms that were developed on a solid foundation of theory, analyses, evidence, well defined balancing between exploration and exploitation like Genetic Algorithms (GA), Genetic Programming (GP), Evolution Strategies (ES), Differential Evolution (DE), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and more.

It includes(but is not restricted to the following topics):
- How can an EA using an LLM evolve different of units of evolution, e.g. code, strings, images, multi-modal candidates?
- How can an EA using an LLM solve prompt composition or other LLM development and use challenges?
- How can an EA using an LLM integrate design explorations related to cooperation, modularity, reuse, or competition?
- How can an EA using an LLM model biology?
- How can an EA using an LLM intrinsically, or with guidance, support open-ended evolution?
- What new variants hybridizing EC and/or another search heuristic are possible and in what respects are they advantageous?
- What are new ways of using LLMs for evolutionary operators, e.g. new ways of generating variation through LLMs, as with LMX or ELM, or new ways of using LLMs for selection, as with e.g. Quality-Diversity through AI Feedback)
- How well does an EA using an LLM scale with population size and problem complexity?
- What is the most accurate computational complexity of an EA using an LLM?
- What makes good EA plus LLM benchmark?
- Better understanding, fine tuning, and adaptation of Large Language Models for EC. How large do LLMs need to be? Are there benefits for using larger/smaller ones? Ones trained on different datasets or in different ways?
- Generating methodology for population dynamics analysis, population diversity measures, control, and analysis and visualization.
- Generating rules for EC (boundary and constraints handling strategies).
- The performance improvement, testing, and efficiency of the improved algorithms.
- Reasoning for component-wise analysis of algorithms.
- Understanding and generation of relations between Complex systems, Randomness, Chaos, and Fractals in EC.
- Connection of LLM and other ML techniques for EC (Reinforcement learning, AutoML)
- Generation and reasoning for parallel approaches for EC for swarm algorithms
- Applications of LLM and EC (not limited to):
+ constrained optimization
+ multi-objective optimization
+ expensive and surrogate assisted optimization
+ dynamic and uncertain optimization
+ large-scale optimization
+ combinatorial/discrete optimization

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 PhD in Computer Science from University College Dublin, Ireland and an MSc in Industrial Engineering and Applied Mathematics from Chalmers University of Technology, Sweden. His research interests involves coevolutionary algorithms, grammatical representations and generative models. His work focuses on developing autonomous, pro-active cyber defenses that are anticipatory and adapt to counter attacks. He is also interested in automated semantic parsing of law, and data science for education and healthcare.

 

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.

 

Joel Lehman

Joel Lehman is a machine learning researcher interested in algorithmic creativity, evolutionary algorithms, artificial life, and AI for wellbeing. Most recently he was a research scientist at OpenAI co-leading the Open-Endedness team (studying algorithms that can innovate endlessly). Previously he was a founding member of Uber AI Labs, first employee of Geometric Intelligence (acquired by Uber), and a tenure track professor at the IT University of Copenhagen. He co-wrote with Kenneth Stanley a popular science book called "Why Greatness Cannot Be Planned" on what AI search algorithms imply for individual and societal accomplishment.

Una-May O’Reilly

Dr. Una-May O'Reilly is the leader of ALFA Group at Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Lab. An evolutionary computation researcher for 20+ years, she is broadly interested in adversarial intelligence — the intelligence that emerges and is recruited while learning and adapting in competitive settings. Her interest has led her to study settings where security is under threat, for which she has developed machine learning algorithms that variously model the arms races of tax compliance and auditing, malware and its detection, cyber network attacks and defenses, and adversarial paradigms in deep learning. She is passionately interested in programming and genetic programming. She is a recipient of the EvoStar Award for Outstanding Achievements in Evolutionary Computation in Europe and the ACM SIGEVO Award Recognizing Outstanding Achievements in Evolutionary Computation. Devoted to the field and committed to its growth, she served on the ACM SIGEVO executive board from SIGEVO's inception and held different officer positions before retiring from it in 2023. She co-founded the annual workshops for Women@GECCO and has proudly watched their evolution to Women+@GECCO. She was on the founding editorial boards and continues to serve on the editorial boards of Genetic Programming and Evolvable Machines, and ACM Transactions on Evolutionary Learning and Optimization. She has received a GECCO best paper award and an GECCO test of time award. She is honored to be a member of SPECIES, a member of the Julian Miller Award committee, and to chair the 2023 and 2024 committees selecting SIGEVO Awards Recognizing Outstanding Achievements in Evolutionary Computation.

 

Pier Luca Lanzi

Pier Luca Lanzi received the Laurea degree in computer science from the Université degli Studi di Udine and the Ph.D. degree in Computer and Automation Engineering from the Politecnico di Milano. He is an associate professor at the Politecnico di Milano, Dept. of Electronics and Information. His research areas include genetic and evolutionary computation, reinforcement learning, and machine learning. He is interested in applications to data mining and autonomous agents. He is member of the editorial board of the "Evolutionary Computation Journal" and Editor in chief of SIGEVOlution, the ACM Newsletter of SIGEVO, the Special Interest Group on Genetic and Evolutionary Computation.

 

Michal Pluhacek

Assoc. prof Michal Pluhacek received his Ph.D. degree in Information Technologies in 2016 with the dissertation topic: Modern method of development and modifications of evolutionary computational techniques. He became an assoc. prof. in 2023 after successfully defending his habilitation thesis on the topic „Inner Dynamics of Evolutionary Computation Techniques: Meaning for Practice.“ He currently works as a senior researcher at the Regional Research Centre CEBIA-Tech of Tomas Bata University in Zlin, Czech Republic. He is the author of many journal and conference papers on Particle Swarm Optimization and related topics. His research focus includes swarm intelligence theory and applications and artificial intelligence in general. In 2019, he finished six-months long research stay at New Jersey Institute of Technology, USA, focusing on swarm intelligence and swarm robotics. Recently, he is focusing his research on the interconnection of evolutionary computing and the large language models. More info: https://ailab.fai.utb.cz/our-team/

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.

NEWK — Neuroevolution at work

https://newk-gecco.github.io/

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 development.
Firstly, many of its paradigms suffer from a lack of parameter-space diversity, meaning a failure to provide diversity in the behaviors generated by the different networks.
Moreover, harnessing neuroevolution to optimize deep neural networks requires noticeable computational power and, consequently, the investigation of new trends in enhancing 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 various 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 of the current state of the art.archers 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 insight into future trends, but 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 ideal for informal contacts, exchanging 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
- adopt evolutionary multi-objective and many-objective optimisation techniques in neuroevolution
- propose new benchmark problems for 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.

Antonio Della Cioppa

Antonio Della Cioppa received the Laurea degree in Physics and the Ph.D. degree in Computer Science, both from University of Naples “Federico II,” Naples, Italy, in 1993 and 1999, respectively. From 1999 to 2003, he was a Postdoctoral Fellow at the Department of Computer Science and Electrical Engineering, University of Salerno, Salerno, Italy. In 2004, he joined the Department of 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.

 

Edgar Galvan

Edgar Galvan is a Senior Researcher in the Department of Computer Science, Maynooth University. He is the Artificial Intelligence and Machine Learning Cluster Leader at the Innovation Value Institute and at the Naturally Inspired Computation Research Group. Prior to this, he held multiple research positions in Essex University, University College Dublin, Trinity College Dublin and INRIA Paris-Saclay. He is an expert in the properties of encodings, such as neutrality and locality, in Genetic Programming as well as a pioneer in the study of Semantic-based Genetic Programming. His research interests also include applications to combinatorial optimisation, games, software engineering and deep neural networks. Dr. Edgar Galvan has independently ranked as one of the all-time top 1% researchers in Genetic Programming, according to University College London. He has published in excess of nearly 70 peer-reviewed publications. Edgar has over 2,300 citations and a H-index of 27.

 

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.

Mengjie Zhang

Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, and currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, evolutionary deep learning and transfer learning, job shop scheduling, multi-objective optimisation, and clustering and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 700 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the Evolutionary Computation Journal (MIT Press), ACM Transactions on Evolutionary Learning and Optimisation, Genetic Programming and Evolvable Machines (Springer), IEEE Transactions on Emergent Topics in Computational Intelligence, Applied Soft Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for eight international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html). He is the Tutorial Chair for GECCO 2014, an AIS-BIO Track Chair for GECCO 2016, an EML Track Chair for GECCO 2017, and a GP Track Chair for GECCO 2020 and 2021. Since 2012, he has been co-chairing several parts of IEEE CEC, SSCI, and EvoIASP/EvoApplications conference (he has been involving major EC conferences such as GECCO, CEC, EvoStar, SEAL). Since 2014, he has been co-organising and co-chairing the special session on evolutionary feature selection and construction at IEEE CEC and SEAL, and also delivered a keynote/plenary talk for IEEE CEC 2018,IEEE ICAVSS 2018, DOCSA 2019, IES 2017 and Chinese National Conference on AI in Law 2017. Prof Zhang was the Chair of the IEEE CIS Intelligent Systems Applications, the IEEE CIS Emergent Technologies Technical Committee, and the IEEE CIS Evolutionary Computation Technical Committee; a Vice-Chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the IEEE CIS Task Force on Evolutionary Deep Learning and Applications; and also the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.

QuantOpt — Workshop on Quantum Optimization

https://sites.google.com/view/quantopt2024

Summary

Scope

Quantum computers are rapidly becoming more powerful and increasingly applicable to solve problems in the real world. They have the potential to solve extremely hard computational problems, which are currently intractable by conventional computers. Quantum optimization is an emerging field that focuses on using quantum computing technologies to solve hard optimization problems.

There are two main types of quantum computers, quantum annealers and quantum gate computers.

Quantum annealers are specially tailored to solve combinatorial optimization problems: they have a simpler architecture, and are more easily manufactured and are currently able to tackle larger problems as they have a larger number of qubits. These computers find (near) optimum solutions of a combinatorial optimization problem via quantum annealing, which is similar to traditional simulated annealing. Whereas simulated annealing uses ‘thermal’ fluctuations for convergence to the state of minimum energy (optimal solution), in quantum annealing the addition of quantum tunnelling provides a faster mechanism for moving between states and faster processing.

Quantum gate computers are general purpose quantum computers. These use quantum logic gates, a basic quantum circuit operating on a small number of qubits, for computation. Constructing an algorithm involves a fixed sequence of quantum logic gates. Some quantum algorithms, e.g., Grover's algorithm, have provable quantum speed-up. These computers can be used to solve combinatorial optimization problems using the quantum approximate optimization algorithm.

Quantum computers have also given rise to quantum-inspired computers and quantum-inspired optimisation algorithms.

Quantum-inspired computers use dedicated conventional hardware technology to emulate/simulate quantum computers. These computers offer a similar programming interface of quantum computers and can currently solve much larger combinatorial optimization problems when compared to quantum computers and much faster than traditional computers.

Quantum-inspired optimisation algorithms use classical computers to simulate some physical phenomena such as superposition and entanglement to perform quantum computations, in an attempt to retain some of its benefit in conventional hardware when searching for solutions.

To solve optimization problems on a quantum annealer or on a quantum gate computer using the quantum approximate optimization algorithm, we need to reformulate them in a format suitable for the quantum hardware, in terms of qubits, biases and couplings between qubits. In mathematical terms, this requirement translates to reformulating the optimization problem as a Quadratic Unconstrained Binary Optimisation (QUBO) problem. This is closely related to the renowned Ising model. It constitutes a universal class, since in principle all combinatorial optimization problems can be formulated as QUBOs. In practice, some classes of optimization problems can be naturally mapped to a QUBO, whereas others are much more challenging to map. In quantum gates computers, Grover’s algorithm can be used to optimize a function by transforming the optimization problem into a series of decision problems. The most challenging part in this case is to select an appropriate representation of the problem to obtain the quadratic speedup of Grover’s algorithm compared to the classical computing algorithms for the same problem.

Content

A major application domain of quantum computers is solving hard combinatorial optimization problems. This is the emerging field of quantum optimization. The aim of the workshop is to provide a forum for both scientific presentations and discussion of issues related to quantum optimization.

As the algorithms quantum that computers use for optimization can be regarded as general types of heuristic optimization algorithms, there are potentially great benefits and synergy to bringing together the communities of quantum computing and heuristic optimization for mutual learning.

The workshop aims to be as inclusive as possible, and welcomes contributions from all areas broadly related to quantum optimization, and by researchers from both academia and industry.

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

- Formulation of optimisation problems as QUBOs (including handling of non-binary representations and constraints)
- Fitness landscape analysis of QUBOs
- Novel search algorithms to solve QUBOs
- Experimental comparisons on QUBO benchmarks
- Theoretical analysis of search algorithms for QUBOs
- Speed-up experiments on traditional hardware vs quantum(-inspired) hardware
- Decomposition of optimisation problems for quantum hardware
- Application of the quantum approximate optimization algorithm
- Application of Grover's algorithm to solve optimisation problems
- Novel quantum-inspired optimisation algorithms
- Optimization/discovery of quantum circuits
- Quantum optimisation for machine learning problems
- Optical Annealing
- Dealing with noise in quantum computing
- Quantum Gates’ optimisation, Quantum Coherent Control



Organizers

Alberto Moraglio

Alberto Moraglio is a Senior Lecturer at the University of Exeter, UK. He holds a PhD in Computer Science from the University of Essex and Master and Bachelor degrees (Laurea) in Computer Engineering from the Polytechnic University of Turin, Italy. He is the founder of a Geometric Theory of Evolutionary Algorithms, which unifies Evolutionary Algorithms across representations and has been used for the principled design and rigorous theoretical analysis of new successful search algorithms. He gave several tutorials at GECCO, IEEE CEC and PPSN, and has an extensive publication record on this subject. He has served as co-chair for the GP track, the GA track and the Theory track at GECCO. He also co-chaired twice the European Conference on Genetic Programming, and is an associate editor of Genetic Programming and Evolvable Machines journal. He has applied his geometric theory to derive a new form of Genetic Programming based on semantics with appealing theoretical properties which is rapidly gaining popularity in the GP community. In the last three years, Alberto has been collaborating with Fujitsu Laboratories on Optimisation on Quantum Annealing machines. He has formulated dozens of Combinatorial Optimisation problems in a format suitable for the Quantum hardware. He is also the inventor of a software (a compiler) aimed at making these machines usable without specific expertise by automating the translation of high-level description of combinatorial optimisation problems to a low-level format suitable for the Quantum hardware (patented invention).

Mayowa Ayodele

Mayowa Ayodele holds a PhD in Evolutionary Computation from Robert Gordon University, Scotland. She works as a Senior Solutions Architect at D-wave Quantum Inc. In this role, she specialises in addressing customer challenges through the utilisation of D-wave's quantum, hybrid, and classical optimisation solvers. Previously, she held the position of Principal Researcher at Fujitsu Research of Europe, United Kingdom, dedicating three years to investigating quantum-inspired techniques for solving optimisation problems.

Over the past decade, a significant portion of her research has revolved around the application of diverse algorithm categories, including, evolutionary algorithms for tackling problems in logistics, including the scheduling of trucks, trailers, ships, and platform supply vessels. In recent years, her focus has shifted towards formulating single and multi-objective constrained optimisation problems as Quadratic Unconstrained Binary Optimization (QUBO) as well as application quantum optimisation techniques to practical problems.

Francisco Chicano

Francisco Chicano holds a PhD in Computer Science from the University of Málaga and a Degree in Physics from the National Distance Education University. Since 2008 he is with the Department of Languages and Computing Sciences of the University of Málaga. His research interests include quantum computing, the application of search techniques to Software Engineering problems and the use of theoretical results to efficiently solve combinatorial optimization problems. He is in the editorial board of Evolutionary Computation Journal, Engineering Applications of Artificial Intelligence, Journal of Systems and Software, ACM Transactions on Evolutionary Learning and Optimization and Mathematical Problems in Engineering. He has also been programme chair and Editor-in-Chief in international events.

Ofer Shir

Ofer Shir is an Associate Professor of Computer Science at Tel-Hai College and a Principal Investigator at Migal-Galilee Research Institute – both located in the Upper Galilee, Israel. Ofer Shir holds a BSc in Physics and Computer Science from the Hebrew University of Jerusalem, Israel (conferred 2003), and both MSc and PhD in Computer Science from Leiden University, The Netherlands (conferred 2004, 2008; PhD advisers: Thomas Bäck and Marc Vrakking). Upon his graduation, he completed a two-years term as a Postdoctoral Research Associate at Princeton University, USA (2008-2010), hosted by Prof. Herschel Rabitz in the Department of Chemistry – where he specialized in computational aspects of experimental quantum systems. He then joined IBM-Research as a Research Staff Member (2010-2013), which constituted his second postdoctoral term, and where he gained real-world experience in convex and combinatorial optimization as well as in decision analytics. His current topics of interest include Statistical Learning within Optimization and Deep Learning in Practice, Self-Supervised Learning, Algorithmically-Guided Experimentation, Combinatorial Optimization and Benchmarking (White/Gray/Black-Box), Quantum Optimization and Quantum Machine Learning.

Lee Spector

Dr. Lee Spector is a Professor of Computer Science at Amherst College, an Adjunct Professor and member of the graduate faculty in the College of Information and Computer Sciences at the University of Massachusetts, Amherst, and an affiliated faculty member at Hampshire College, where he taught for many years before moving to Amherst College. He received a B.A. in Philosophy from Oberlin College in 1984, and a Ph.D. from the Department of Computer Science at the University of Maryland in 1992. At Hampshire College he held the MacArthur Chair, served as the elected faculty member of the Board of Trustees, served as the Dean of the School of Cognitive Science, served as Co-Director of Hampshire’s Design, Art and Technology program, supervised the Hampshire College Cluster Computing Facility, and served as the Director of the Institute for Computational Intelligence. At Amherst College he teaches computer science and directs an initiative on Artificial Intelligence and the Liberal Arts. My research and teaching focus on artificial intelligence and intersections of computer science with cognitive science, philosophy, physics, evolutionary biology, and the arts. He is the Editor-in-Chief of the Springer journal Genetic Programming and Evolvable Machines and a member of the editorial boards of the MIT Press journal Evolutionary Computation and the ACM journal Transactions on Evolutionary Learning and Optimization. He is a member of the Executive Committee of the ACM Special Interest Group on Evolutionary Computation (SIGEVO) and he has produced over 100 scientific publications. He serves regularly as a reviewer and as an organizer of professional events, and his research has been supported by the U.S. National Science Foundation and DARPA among other funding sources. Among the honors that he has received is the highest honor bestowed by the U.S. National Science Foundation for excellence in both teaching and research, the NSF Director's Award for Distinguished Teaching Scholars.

 

Matthieu Parizy

Matthieu Parizy is a Research Director at Fujitsu Limited in Kawasaki, Japan where he has been working since 2008. Over the last 5 years, in the Digital Annealer Project, he has led the development of visualization and tuning techniques for quantum inspired Ising machines. He holds a M.Eng. from ESIEE Paris (2008) and a D.Eng. degree in computer engineering from Waseda University (2023). His thesis is on the topic of maximizing performance of Ising machines from the application layer, including formalization techniques for non-binary problems as well as automated hyperparameter tuning techniques. Previously, he had been doing research on VLSI IC design techniques.

Markus Wagner

Markus Wagner is an Associate Professor at Monash University, Australia. He has done his PhD studies at the Max Planck Institute for Informatics in Saarbruecken, Germany and at the University of Adelaide, Australia. For the outcomes of his research, he has received a University Doctoral Research Medal, four best paper awards, a best poster award, a best presentation award, and a Humies Gold Award. His research topics range from mathematical runtime analysis of heuristic optimisation algorithms and theory-guided algorithm design to applications of heuristic methods to renewable energy production, professional team cycling and software engineering. So far, he has been a program committee member 80+ times, and he has written 150+ articles with 200+ different co-authors. He is an ACM Lifetime Member, is on SIGEVO's Executive Board and serves as the first ever Sustainability Officer. He has contributed to GECCOs as Workshop Chair and Competition Chair, and he has chaired several education-related committees within the IEEE CIS, where he also served as founding chair of task forces on benchmarking and on energy.

SAEOpt — Workshop on Surrogate-Assisted Evolutionary Optimisation

https://saeopt.bitbucket.io/

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 in 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 Rahat is an Associate Professor of Data Science. His expertise is in evolutionary and Bayesian search and optimisation. Particularly, he has worked on developing effective acquisition functions for optimising single and multi-objective problems and locating the feasible space of solutions. He has a strong track record of working with industry on a broad range of optimisation problems, which resulted in numerous articles in top journals and conferences, including a best paper in the Real-World Applications track at GECCO, and a patent with Hydro International Ltd. Recently, he has been actively contributing to the Welsh Government's response to the pandemic using his expertise in machine learning and parameter optimisation with funding from both the Welsh Government (Co-PI and Co-I; £750k) and EPSRC (EP/W01226X/1, PI; £230k). His work, with colleagues at Swansea, has resulted in generating medium-term projections of admissions and deaths every week for the First Minister of Wales, and the UK Health Security Agency.

He is one of 24 members of the IEEE Computational Intelligence Society Task Force on Data-Driven Evolutionary Optimization of Expensive Problems. He has been the lead organiser for the Surrogate-Assisted Evolutionary Optimisation (SAEOpt) workshop at GECCO since 2016, and was the Proceedings Chair for GECCO 2022. Furthermore, he successfully led Swansea University's application to join the Turing University Network in 2023, and he is currently the Turing Academic Liaison for the university.

Currently, he is interested in developing methods for optimising constrained and expensive single and multi-objective problems, and active learning, that may be applied in different contexts, e.g. engineering design, educational technology, computational modelling, decision-making, and policy exploration.

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 completed a Postgraduate Certificate in Teaching in Higher Education at Swansea University, and he is now a fellow of the Higher Education Academy (FHEA). 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, Editor-in-Chief University of Exeter, UK 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, with particular interests in multiple-objective optimisation, and the interface between optimisation and machine learning. Over the years, he has been a co-organiser of a number of different Workshops at GECCO (VizGEC, SAEOpt and EAPwU), as well as EMO Track Chair in GECCO 2019 and GECCO 2020. He is an Associate Editor of IEEE Transactions on Evolutionary Computation, and ACM Transactions on Evolutionary Learning and Optimization, and on the Editorial Board of Complex and Intelligence Systems. 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.

 

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.

Tinkle Chugh

Dr Tinkle Chugh is a Lecturer in Computer Science at the University of Exeter. He is the Associate Editor of the Complex and Intelligent Systems journal. Between Feb 2018 and June 2020, he worked as a Postdoctoral Research Fellow in the BIG data methods for improving windstorm FOOTprint prediction project funded by Natural Environment Research Council 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 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.

SWINGA — Swarm Intelligence Algorithms: Foundations, Perspectives and Challenges

Summary

Evolutionary computation, drawing inspiration from the principles of natural selection outlined by Darwin and the genetic inheritance mechanisms identified by Mendel, along with algorithms modeled on the collective behavior of natural swarms, has become a cornerstone for tackling diverse optimization challenges. These computational strategies are increasingly being refined through the integration of innovative techniques that enhance their effectiveness. Furthermore, the development of specialized tools and frameworks is crucial for optimizing their configurations and for investigating behaviors they manifest. Contemporary research in this field is intensely focused on advancing our understanding of these algorithms by examining aspects such as their operational efficiency, convergence rates, population diversity, internal dynamics, and innovative visualization methods for a wide array of swarm-based and evolutionary computational models.

The SWINGA workshop concerns original research papers discussing new results and novel algorithmic improvements tested on widely accepted benchmark tests. This workshop aims to bring together experts from fundamental research and various application fields to develop and introduce a fusion of techniques, deeper insights into population dynamics, and automatic configuration tools. Such research has become a vitally important part of science and engineering at the theoretical and practical levels. 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. This workshop is focused on swarm intelligence algorithms, like Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Self-Organizing Migrating Algorithm (SOMA), Artificial Bee Colony (ABC), 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, and more.
The scope will be focused on, but not limited to, the below-listed topics:

List of topics:

• The theoretical aspect of the swarm intelligence.
• The performance improvement, testing, and efficiency of the swarm intelligence based algorithms.
• Autoconfiguration for swarm algorithms
• Component-wise analysis of swarm algorithms
• Population dynamics analysis for swarm algorithms.
• Boundary and constraints handling strategies.
• Visualization of population dynamics in swarms.
• Explorative landscape analysis and relation with swarm algorithm performance.
• Reinforcement learning and swarm algorithms.
• Population diversity measure, control, and analysis.
• Complex systems for swarm algorithms.
• Original models of population dynamics.
• Swarm intelligence and its parallelization
• Swarm intelligence for discrete optimization
• Mutual relations amongst swarm dynamics, complex networks, and its analysis.
• Randomness, chaos, and fractals in evolutionary dynamics and their impact on algorithm performance.
• Recent advances in better understanding, fine-tuning, and adaptation for swarm/evolutionary algorithms.
• Applications (not limited to):
-- constrained optimization
-- multi-objective optimization
-- many-objective optimization
-- multimodal optimization and niching
-- expensive and surrogate-assisted optimization
-- dynamic and uncertain optimization
-- large-scale optimization
-- combinatorial optimization

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.

 

Pavel Kromer

Pavel Krömer graduated in Computer Science from the Faculty of Electrical Engineering and Computer Science (FEECS) of VSB-Technical University of Ostrava (VSB-TUO). He worked as an analyst, developer, and trainer in the private sector between 2005 and 2010. Since 2010, he has been with the Department of Computer Science, FEECS VSB-TUO. In 2014, he was a Postdoctoral Fellow at the University of Alberta. In 2015, he was awarded the title Assoc. Professor of Computer Science. He was a Researcher at the IT4Innovations (National Supercomputing Center) between 2011 and 2016 and has been a member of its scientific council since February 2017. Since September 1, 2017, he has been the Vice Dean for International Cooperation at FEECS. Since 2018, he is a Senior Member of the IEEE. In his research, he focuses on computational intelligence, information retrieval, data mining, machine learning, soft computing, and real-world applications of intelligent methods. He was the principal contributor to a broad range of research projects with results published in high-impact international journals such as Soft Computing (Springer), and others published by Elsevier, Oxford University Press, and Wiley. In this field, he has contributed to a number of major conferences organized by the IEEE and ACM. He has been a reviewer for Information Sciences, IEEE Transactions on Evolutionary Computation, Swarm and Evolutionary Computation, Neurocomputing, Scientific Reports, and other scientific journals. He also acts as a project reviewer for the Research Agency (Slovakia), National Science Centre (Poland), National Research Foundation (South Africa), and the European Commission (DG CONNECT, REA).

 

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.

SymReg — Symbolic Regression Workshop

https://heal.heuristiclab.com/research/symbolic-regression-workshop

Summary

Symbolic regression is the search for symbolic models that describe a relationship in provided data. Symbolic regression has been one of the first applications of genetic programming and as such is tightly connected to evolutionary algorithms. However, in recent years several non-evolutionary techniques for solving symbolic regression have emerged. Especially with the focus on interpretability and explainability in AI research, symbolic regression takes a leading role among machine learning methods, whenever model inspection and understanding by a domain expert is desired. Examples where symbolic regression already produces outstanding results include modeling where interpretability is desired, modeling of non-linear dependencies, modeling with small data sets or noisy data, modeling with additional constraints, or modeling of differential equation systems.

The focus of this workshop is to further advance the state-of-the-art in symbolic regression by gathering experts in the field of symbolic regression and facilitating an exchange of novel research ideas. Therefore, we encourage submissions presenting novel techniques or applications of symbolic regression, theoretical work on issues of generalization, size and interpretability of the models produced, or algorithmic improvements to make the techniques more efficient, more reliable and generally better controlled. Furthermore, we invite participants of the symbolic regression competition to present their algorithms and results in detail at this workshop.

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

  • evolutionary and non-evolutionary algorithms for symbolic regression
  • improving stability of symbolic regression algorithms
  • uncertainty estimation in symbolic regression
  • integration of side-information (physical laws, constraints, ...)
  • benchmarking symbolic regression algorithms
  • symbolic regression for scientific machine learning
  • innovative symbolic regression applications

Organizers

Gabriel Kronberger

Gabriel Kronberger is professor at the University of Applied Sciences Upper Austria and has been working on algorithms for symbolic regression since more than 15 years. From 2018 until 2022 he lead the Josef Ressel Center for Symbolic Regression (https://symreg.at), a five-year project focused on developing symbolic regression methods and applications in collaboration with several Austrian company partners. His current research interests are symbolic regression for scientific machine learning and industrial applications. Gabriel has (co-)authored more than 100 publications (SCOPUS) and has been a member of the Program Committee for the GECCO Genetic Programming track since 2016.

William La Cava

William La Cava is an Assistant Professor in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital and Harvard Medical School. He received his PhD from UMass Amherst with a focus on interpretable modeling of dynamical systems. Prior to joining CHIP, he was a post-doctoral fellow and research associate in the Institute for Biomedical Informatics at the University of Pennsylvania.

 

Steven Gustafson

Steven Gustafson received his PhD in Computer Science and Artificial Intelligence, and shortly thereafter was awarded IEEE Intelligent System's "AI's 10 to Watch" for his work in algorithms that discover algorithms. For 10+ years at GE's corporate R&D center he was a leader in AI, successful technical lab manager, all while inventing and deploying state-of-the-art AI systems for almost every GE business, from GE Capital to NBC Universal and GE Aviation. He has over 50 publications, 13 patents, was a co-founder and Technical Editor in Chief of the Memetic Computing Journal. Steven has chaired various conferences and workshops, including the first Symbolic Regression and Modeling (SRM) Workshop at GECCO 2009 and subsequent workshops from 2010 to 2014. As the Chief Scientist at Maana, a Knowledge Platform software company, he invented and architected new AutoML and NLP techniques with publications in AAAI and IJCAI. Steven is currently the CTO of Noonum, an investment intelligence company, that is pushing the state-of-the-art of large scale knowledge graph, NLP and machine learning decision support systems.