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Workshops



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
BBOB 2025 — Workshop on Black Box Optimization Benchmarking 2025
  • Anne Auger Inria, France
  • Dimo Brockhoff Inria and Ecole Polytechnique, France
  • Tobias Glasmachers Ruhr-Universität Bochum, Germany
  • Nikolaus Hansen Inria and Ecole Polytechnique, France
  • Olaf Mersmann Technische Hochschule Köln
  • Tea Tušar Jožef Stefan Institute, Slovenia
BENCH@GECCO25 — Good Benchmarking Practices for Evolutionary Computation
  • Vanessa Volz CWI, Netherlands
  • Carola Doerr CNRS and Sorbonne University, France
  • Boris Naujoks Cologne University of Applied Sciences, Germany
  • Mike Preuss Leiden Institute of Advanced Computer Science
  • Olaf Mersmann Technische Hochschule Köln
  • Pascal Kerschke TU Dresden, Germany
DTEO — Decomposition Techniques in Evolutionary Optimization
  • Bilel Derbel University of Lille, CRIStAL CNRS, Inria Lille, France
  • Ke Li University of Exeter, UK
  • Xiaodong Li RMIT University, Australia
  • Saúl Zapotecas-Martínez National institute of astrophysics optics and electronics (INAOE)
  • Qingfu Zhang City University of Hong Kong
EC + DM — Evolutionary Computation and Decision Making
  • Tinkle Chugh University of Exeter, UK
  • Richard Allmendinger The University of Manchester, UK
  • Ana B. Ruiz University of Málaga
ECADA 2025 — 15th 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
  • Jaume Bacardit Newcastle University, UK
  • Alexander Brownlee University of Stirling
  • Stefano Cagnoni University of Parma
  • Giovanni Iacca University of Trento, Italy
  • John McCall Robert Gordon University, UK
  • David Walker University of Exeter
EGM — Evolutionary Generative Models
  • João Correia University of Coimbra, Portugal
  • Jamal Toutouh MIT, USA
  • Una-May O’Reilly MIT, USA
  • 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
EvoSelf — Evolving self-organisation
  • Eleni Nisioti IT University of Copenhagen
  • Sebastian Risi IT University of Copenhagen
  • Joachim Winther Pedersen IT University of Copenhagen
  • Ettore Randazzo Google Research in Zürich, Switzerland
  • Alexander Mordvintsev Google Research in Zürich, Switzerland
  • Eyvind Niklasson Google Research in Zürich, Switzerland
GGP — Graph-based Genetic Programming
  • Roman Kalkreuth TU Dortmund University
  • Yuri Lavinas University of Toulouse 1 Capitole, University of Toulouse
  • 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
  • Dennis G. Wilson ISAE-SUPAERO, University of Toulouse, France
IAM 2025 — 10th Workshop on Industrial Applications of Metaheuristics
  • Silvino Fernández Alzueta Arcelormittal, Spain
  • Pablo Valledor Pellicer ArcelorMittal Global R&D
  • Thomas Stützle Université Libre de Bruxelles, Belgium
IWERL — 28th International Workshop on Evolutionary Rule-based Machine Learning
  • Abubakar Siddique Wellington Institute of Technology – Whitireia WelTec, New Zealand
  • Michael Heider Universität Augsburg, Germany
  • Hiroki Shiraishi Yokohama National University, Japan
LAHS 2025 — 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
  • 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
  • Michal Pluhacek Tomas Bata University in Zlin, A.I.Lab, Czech Republic
  • Niki van Stein Leiden University
  • Pier Luca Lanzi Politecnico di Milano
  • 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
  • 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
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
SymReg — Symbolic Regression
  • Gabriel Kronberger University of Applied Sciences Upper Austria
  • Fabricio Olivetti de França Federal University of ABC (UFABC), Brazil
  • William La Cava Harvard, Boston Children’s Hospital, USA
  • Steven Gustafson University of Washington

AABOH — Analysing algorithmic behaviour of optimisation heuristics

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.

BBOB 2025 — Workshop on Black Box Optimization Benchmarking 2025

Summary

Benchmarking optimization algorithms is a crucial aspect for their design and practical application. Since 2009, the Black Box Optimization Benchmarking Workshop has served as a place for discussing general recent advances in benchmarking practices and concrete results from benchmarking experiments with a large variety of (black box) optimizers.

The Comparing Continuous Optimizers platform (COCO, 1, https://github.com/numbbo/coco) was developed in this context to support algorithm developers and practitioners alike by automating benchmarking experiments for black box optimization algorithms in single-
and bi-objective, unconstrained and constrained, continuous and mixed-integer problems in exact and noisy, as well as expensive and non-expensive scenarios.

We welcome *all contributions to black box optimization benchmarking* for the 2025 edition of the workshop, although we would like to put a particular emphasis on:

1) Benchmarking algorithms for problems with underexplored properties (for
example mixed integer, noisy, constrained, multiobjective, ...)
2) Reproducing previous benchmarking results as well as examining performance
improvements or degradations in algorithm implementations over time
(for example with the help of results from earlier BBOB submissions).

Submissions are not limited to the test suites provided by COCO. For convenience, the source code in various languages (C/C++, Matlab/Octave, Java, Python, and Rust) together with all data sets from previous BBOB contributions are provided as an automatized benchmarking pipeline to reduce the time spent for producing the results for:

- single-objective unconstrained problems (the "bbob" test suite)
- single-objective unconstrained problems with noise ("bbob-noisy")
- biobjective unconstrained problems ("bbob-biobj")
- large-scale single-objective problems ("bbob-largescale")
- mixed-integer single- and bi-objective problems ("bbob-mixint" and
"bbob-biobj-mixint")
- almost linearly constrained single-objective problems ("bbob-constrained")
- box-constrained problems ("sbox-cost")

We especially encourage submissions exploring algorithms from beyond the evolutionary computation community, as well as papers analyzing COCO’s extensive, publicly available algorithm datasets (see https://numbbo.github.io/data-archive/).

For details, please see the separate BBOB-2025 web page at https://numbbo.github.io/workshops/BBOB-2025/index.html (available upon acceptance of the workshop)

1 Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar,
and Dimo Brockhoff. "COCO: A platform for comparing continuous
optimizers in a black-box setting." Optimization Methods and
Software (2020): 1-31.

Organizers

Anne Auger

Anne Auger is a research director at the French National Institute for Research in Computer Science and Control (Inria) heading the RandOpt team. She received her diploma (2001) and PhD (2004) in mathematics from the Paris VI University. Before to join INRIA, she worked for two years (2004-2006) at ETH in Zurich. Her main research interest is stochastic continuous optimization including theoretical aspects, algorithm designs and benchmarking. She is a member of ACM-SIGECO executive committee and of the editorial board of Evolutionary Computation. She has been General chair of GECCO in 2019. She has been organizing the biannual Dagstuhl seminar "Theory of Evolutionary Algorithms" in 2008 and 2010 and all seven previous BBOB workshops at GECCO since 2009. She is co-organzing the forthcoming Dagstuhl seminar on benchmarking.

Dimo Brockhoff

Dimo Brockhoff received his diploma in computer science from University of Dortmund, Germany in 2005 and his PhD (Dr. sc. ETH) from ETH Zurich, Switzerland in 2009. After two postdocs at Inria Saclay Ile-de-France (2009-2010) and at Ecole Polytechnique (2010-2011), he joined Inria in November 2011 as a permanent researcher (first in its Lille - Nord Europe research center and since October 2016 in the Saclay - Ile-de-France one). His research interests are focused on evolutionary multiobjective optimization (EMO), in particular on theoretical aspects of indicator-based search and on the benchmarking of blackbox algorithms in general. Dimo has co-organized all BBOB workshops since 2013 and has been EMO track co-chair at GECCO in 2013, 2014, and 2023.

Tobias Glasmachers

Tobias Glasmachers is a professor at the Ruhr-University Bochum, Germany. He received his Diploma and Doctorate degrees in mathematics from the Ruhr-University of Bochum in 2004 and 2008. He joined the Swiss AI lab IDSIA from 2009 to 2011. Then he returned to Bochum, where he was a junior professor for machine learning at the Institute for Neural Computation (INI) from 2012 to 2018. In 2018 he was promoted to a full professor. His research interests are machine learning and optimization.

Nikolaus Hansen

Nikolaus Hansen is a research director at Inria and the Institut Polytechnique de Paris, France. After studying medicine and mathematics, he received a PhD in civil engineering from the Technical University Berlin and the Habilitation in computer science from the University Paris-Sud. His main research interests are stochastic search algorithms in continuous, high-dimensional search spaces, learning and adaptation in evolutionary computation, and meaningful assessment and comparison methodologies. His research is driven by the goal to develop algorithms applicable in practice. His best-known contribution to the field of evolutionary computation is the so-called Covariance Matrix Adaptation (CMA).

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.

Tea Tušar

Tea Tušar is a senior research assistant at the Department of Intelligent Systems of the Jozef Stefan Institute in Ljubljana, Slovenia. She was awarded the PhD degree in Information and Communication Technologies by the Jozef Stefan International Postgraduate School for her work on visualizing solution sets in multiobjective optimization. She has completed a one-year postdoctoral fellowship at Inria Lille in France where she worked on benchmarking multiobjective optimizers. Her research interests include evolutionary algorithms for singleobjective and multiobjective optimization with emphasis on visualizing and benchmarking their results and applying them to real-world problems.

BENCH@GECCO25 — BENCH@GECCO25 - Good Benchmarking Practices for Evolutionary Computation

Summary

Benchmarking plays a vital role in understanding the performance and search behaviour 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.

For GECCO 2025, our focus will be on **“Benchmarking for humans and machines - Differences and Similarities”**.

Many currently popular benchmarks are designed to be interpretable by humans with specific questions in mind. For example, if the algorithm can exploit separability or how it handles disconnected Pareto fronts. As a result, they do not attempt to cover the full space of interesting problems. However, when used in the context of automated algorithm selection, algorithm configuration, and similar machine learning tasks, data requirements may change, as the ability for manual interpretation is no longer a restriction.

At the same time, benchmarking results in publications are often presented as aggregates without heeding the original intent of the benchmark designer. So even without the involvement of machines, the benchmarking data is typically suboptimally presented and interpreted.
In this workshop, we will be addressing the following questions:

  • What are key similarities and differences between benchmarks designed for human vs. machine interpretation?
  • Are there inherent differences between human vs. machine interpretable benchmarking pipelines that require the experimental setup, apart from the size of the generated data sets, to be different?
  • How can we best support the analysis of benchmarking data, for manual interpretation and machine-based learning.

Organizers

Vanessa Volz

Vanessa Volz is currently a tenure track researcher in the Evolutionary Intelligence (EI) group at Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands. She received her PhD in 2019 from TU Dortmund University, Germany, for her work on surrogate-assisted evolutionary algorithms applied to game optimisation. Her current research focus is on transfer learning in the context of evolutionary computation, especially in the context of recurring or otherwise dynamic problems.

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 the Evolutionary Computation journal. She is/was program chair for the BBSR track at GECCO 2025 and 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 been distinguished by several awards, among them the CNRS bronze medal, the Otto Hahn Medal of the Max Planck Society, and best paper awards at GECCO, CEC, and EvoApplications.

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.

 

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.

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.

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.

DTEO — Decomposition Techniques in Evolutionary Optimization

Summary

Decomposition-based optimization involves transforming a complex problem into multiple smaller, more manageable sub-problems that can be solved cooperatively. The evolutionary computing community actively develops methods to explicitly or implicitly design decomposition across four key facets: (i) environmental parameters, (ii) decision variables, (iii) objective functions, and (iv) available computing resources.

This workshop aims to bring together recent advances in the design, analysis, and understanding of evolutionary decomposition techniques. It also provides a platform to discuss challenges in applying decomposition to increasingly large and complex optimization tasks—such as problems with many variables or objectives, multi-modal problems, simulation-based optimization, and uncertain scenarios—while considering modern large-scale computing environments, including massively parallel and decentralized systems.

The workshop focuses on, but is not limited to, the following topics:

      • Large-scale evolutionary decomposition: Decomposition in decision space, gray-box methods, co-evolutionary algorithms, grouping techniques, and cooperative methods for constraint handling.
      • Many- and multi-objective decomposition: Aggregation and scalarization methods, hybrid island-based approaches, and (sub-)population decomposition and mapping.
      • Parallel and distributed evolutionary decomposition: Scalability across decision and objective spaces, decentralized divide-and-conquer strategies, distributed computing efforts, and deployment on heterogeneous, large-scale parallel platforms.
      • New general-purpose decomposition techniques: Machine-learning-assisted decomposition, online and offline configuration, search region decomposition, use of multiple surrogates, and parallel approaches for expensive optimization.
      • Emerging applications of evolutionary techniques based on decomposition.
      • Understanding and benchmarking decomposition techniques.
      • Software tools and libraries for evolutionary decomposition.


In general, this workshop encourages both theoretical and practical contributions, focusing on developmental, implementation, and applied aspects of decomposition techniques in evolutionary optimization.

Organizers

Bilel Derbel

Bilel Derbel is a Professor, at the Department of Computer Science at the University of Lille, France. He is the team leader of BONUS (Big Optimization aNd Ultra-Scale Computing), a joint research group the Inria research center of the University of Lille, and the CRIStAL CNRS Laboratory, France. He is a Collaborative Professor at Shinshu University, Nagano, Japan. His current research topics are on the design and analysis of algorithms for solving single- and multi- objective optimization problems using high-level optimization techniques, such as, stochastic search heuristics, ML-inspired search techniques, fitness landscape, parallel and distributed computing.

Ke Li

Ke Li is a UKRI Future Leaders Fellow, a Turing Fellow, and a Senior Lecturer of Computer Science at the University of Exeter (UoE). He was the founding chair of IEEE Computational Intelligence Society (CIS) Task Force 12 from 2017 to 2021. This is an international consortium that brings together global researchers to promote an active state of themed areas of the decomposition-based techniques in CI. Related activities include workshops and special sessions associated with major conferences in EC (GECCO, CEC and PPSN) and webinars since 2018. He has been a Publication Chair of EMO 2021, a dedicated conference on evolutionary multi-objective optimization and multi-criterion decision-making. Moreover, he has been an academic mentor of early career researchers (ECRs) in IEEE CIS since 2020. He has co-chaired a summer school in 2021 themed on "Data-Driven AI/CI: Theory and Applications" sponsored by IEEE Computational Intelligence Society. This summer school aims to i) provide a unique opportunity to ECRs from around the world to learn about state-of-the-art AI/CI techniques and applications; ii) interact with world-renowned high-profile experts; and iii) inspire new ideas and collaborations. Dr Li has been serving as an Associate Editor of four academic journals including IEEE Trans. Evol. Comput. Moreover, he was a guest editor of a special issues on the topic of semantic computing and personalization in Neurocomputing journal and a special issue on the topic of advances of AI in visual systems in Multimedia Tools and Applications journal.

Xiaodong Li

Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in Artificial Intelligence from University of Otago, Dunedin, New Zealand, respectively. He is a Professor with the School of Computing Technologies, RMIT University, Melbourne, Australia. His research interests include machine learning, evolutionary computation, neural networks, deep learning, data analytics, multiobjective optimization, operational research, and swarm intelligence. He served as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a former vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an IEEE Fellow.

Saúl Zapotecas-Martínez

Saúl Zapotecas-Martínez is an Associate Professor in the Computer Science Department at the National Institute of Astrophysics, Optics, and Electronics (INAOE) in Mexico. He is also a Collaborative Professor at Shinshu University in Nagano, Japan, working with MODO: International Associated Laboratory (Massive Optimization and Computational Intelligence). Dr. Zapotecas serves as an Associate Editor for four academic journals, including IEEE Transactions on Evolutionary Computation and Swarm and Evolutionary Computation. He actively participates in the program committees of numerous international conferences and reviews for several leading journals in evolutionary computation. His research focuses on evolutionary computing, machine learning, neuroevolution, and their applications to complex optimization problems.

 

Qingfu Zhang

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

EC + DM — Evolutionary Computation and Decision Making

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 2025 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 is Professor of Applied Artificial Intelligence, and Associate Dean for Business Engagement, Civic & Cultural Partnerships in the Faculty of Humanities, The University of Manchester, UK. He is also an Editorial Board Member of several international journals, an Alan Turing Fellow Alumni, has served in numerous chair roles for different AI conferences, and is a Senior Scientist at Eharo, and AI Advisor for River Capital (private equity), Ark Biotech (bioprocessing), and GuruAI (music education). Currently, Richard is also creating a UoM software spinout focussed on identifying and repairing security vulnerabilities in source code. Prior to Manchester, he was Honorary Lecturer and Research Associate at the Biochemical Engineering Department, University College London. He studied Business Engineering at the Karlsruhe Institute of Technology and the Royal Melbourne Institute of Technology and completed a PhD in Computer Science (Machine Learning & Optimization) at The University of Manchester. Richard's research interests are 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 a total of £45M+ in grant funding as PI/co- I from UKRI, industry, and other sources, and led the development of several commercially available AI tools.

 

Ana B. Ruiz

Ana B. Ruiz is a Senior Lecturer in the area of Quantitative Methods for Economy at the Department of Applied Economics (Mathematics), at the University of Málaga (Spain). She holds a PhD in Mathematics (2012) from the University of Málaga, where she also received her BSc degree in Mathematics (2006). Her research is focused on multi-objective optimization and multiple-criteria decision-making approaches, such as evolutionary algorithms, interactive methods, and reference point-based techniques, and their applications to decision-making processes arising in different fields, such as education, portfolio, sustainability, or engineering. She has participated in more than 17 research projects financed by international, national and regional institutions, and she has collaborations with several international researchers. Currently, she is one of the main researchers of a partner in a European Project granted for the development of safe and sustainable by design coatings for several industrial sectors. In addition, Ana B. Ruiz teaches graduate courses in Economics, Business Administration, and Marketing, in the Master's Course in Quantitative Methods for Economy, and the PhD program in Economy and Business Administration.

ECADA 2025 — 15th Workshop on Evolutionary Computation for the Automated Design of Algorithms

Summary

Mode: hybrid

Scope

The main objective of this workshop is to discuss hyper-heuristics and algorithm configuration methods for the automated generation and improvement of algorithms, with the goal of producing solutions (algorithms) that apply 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 operates 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 limit the generality of this approach as the primitive set can be selected to be Turing-complete. Typically, however, the initial algorithmic primitive set is composed of primitive components of existing high-performing algorithms for the problems being targeted; this more targeted approach very significantly reduces the initial search space, resulting in a practical approach rather than a mere theoretical curiosity. Iterative refining of the primitives allows for gradual and directed enlarging of the search space until convergence.

As meta-heuristics are themselves a type of algorithm, they too can be automatically designed employing hyper-heuristics. For instance, in 2007, genetic programming was used to evolve mate selection in evolutionary algorithms; in 2011, linear genetic programming was used to evolve crossover operators; more recently, genetic programming was used to evolve complete black-box search algorithms, SAT solvers, and FuzzyART category functions. Moreover, hyper-heuristics may be applied before deploying an algorithm (offline) or while problems are being solved (online), or even continuously learn by solving new problems (life-long). Offline and life-long hyper-heuristics are particularly useful for real-world problem solving where one can afford a large amount of a priori computational time to subsequently solve many problem instances drawn from a specified problem domain, thus amortizing the a priori computational time over repeated problem-solving. Recently, the design of multi-objective evolutionary algorithm components was automated.

Very little is known yet about the foundations of hyper-heuristics, such as the impact of the meta-heuristic exploring algorithm space on the performance of the thus automatically designed algorithm. An initial study compared the performance of algorithms generated by hyper-heuristics powered by five major types of genetic programming. Another avenue for research is investigating the potential performance improvements obtained through the use of asynchronous parallel evolution to exploit the typical large variation in fitness evaluation times when executing hyper-heuristics.


Content

We welcome original submissions on all aspects of Evolutionary Computation for the Automated Design of Algorithms, in particular, evolutionary computation methods and other hyper-heuristics for the automated design, generation or improvement of algorithms that can be applied to any instance of a target problem domain. Relevant methods include methods that evolve whole algorithms given some initial components as well as methods that take an existing algorithm and improve it or adapt it to a specific domain. Another important aspect of 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, and a Guest Scientist at Los Alamos National Laboratory (LANL). He received his Ph.D. in 2002 from Leiden University. His research interests include the design of generative hyper-heuristics, competitive coevolution, evolutionary algorithms for simulating molecular evolution, parameter control in evolutionary algorithms, 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

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. This is currently a very active research area within the AI field. 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 often hinder 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, discuss 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 paid to generating explanations and their accessibility to end-users. This increased attention has helped create a fertile environment for applying 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. Finally, many of the broader questions (such as what kinds of explanations 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.

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

Jaume Bacardit

Jaume Bacardit is Professor of Artificial Intelligence at Newcastle University in the UK. He has received a 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 100+ peer-reviewed publications that have attracted 7800+ citations and a H-index of 40 (Google Scholar).

Alexander Brownlee

Alexander (Sandy) Brownlee is a Senior Lecturer in the Division of Computing Science and Mathematics at the University of Stirling, where he leads the Data Science and Intelligent Systems research group. 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 80 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, explainable AI, and analysis of biomedical data. In these fields, he co-authored more than 180 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.

John McCall

John McCall is Emeritus Professor in Computational Intelligence and Industry Optimisation at Robert Gordon University. He has researched in machine learning, search and optimisation for over 30 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 3500 citations with an h-index of 27. John specialises 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 has attracted 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, and as Associate Editor of IEEE Computational Intelligence Magazine, the IEEE Systems, Man and Cybernetics Journal, and Complex And Intelligent Systems. He frequently organises workshops and special sessions at leading international conferences, including several GECCO workshops in recent years.

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 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, working on the interface between evolutionary computation and explainable AI. 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. 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.

EGM — Evolutionary Generative Models

Summary

Generative Models has become a key field in Artificial Intelligence. Evolutionary generative models refer to generative approaches that employ any type of evolutionary algorithm, whether applied on its own or in conjunction with other methods. In a broader sense we can divide evolutionary generative models into at least three main types:

(i) Evolutionary Computation (EC) as a Generative Model focuses on exploring how EC techniques that serve directly as generative models to produce data, designs, or solutions that fulfill specific criteria or constraints;

(ii) Generative Models Assisting EC consists in modern generative models, such as Generative Adversarial Networks or diffusion models, that enhance the performance and capabilities of EC methods (e.g., using generative models such as surrogate).

(iii) EC Assisting Generative Models discusses the role of EC techniques in enhancing generative models themselves, particularly through optimization and exploration. This includes approaches where EC is used to evolve or optimize the parameters of generative networks, help address generative models issues, or introduce adaptive mechanisms that improve model flexibility and resilience. It also delves into topics related to EC population dynamics such as cooperative or adversarial approaches.

The workshop on Evolutionary Generative Models (EGM) aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for researchers focused on generative models in the EC community. Thus, this workshop provides a critical forum for disseminating the experience on the topic using EC as a generative model, generative models assisting EC and EC assisting generative models, presenting new and ongoing research in the field, and to attract new interest from our community.

Topics:
. Evolutionary Generative Models
. Generative Models in Evolutionary Computation
. Evolutionary Machine Learning Generative Models
. EC-assisted Generative Machine Learning training, generation, hyperparameter optimisation or architecture search.
. Co-operative or Adversarial Generative Models
. Evolutionary latent and embedding space exploration (e.g. LVEs)
. Interaction with Evolutionary Generative Models
. Real-world applications of Evolutionary Generative Models solutions
. Software libraries and frameworks for Evolutionary Generative Models

Organizers

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.

Jamal Toutouh

I am a Marie Skłodowska Currie Postdoctoral Fellow at Massachusetts Institute of Technology (MIT) in the USA, at the MIT CSAIL Lab. I obtained my Ph.D. in Computer Engineering at the University of Malaga (Spain). The dissertation, Natural Computing for Vehicular Networks, was awarded the 2018 Best Spanish Ph.D. Thesis in Smart Cities. My dissertation focused on the application of Machine Learning methods inspired by Nature to address Smart Mobility problems.
My current research explores the combination of Nature-inspired gradient-free and gradient-based methods to address 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 co-evolutionary approaches. Besides, I am working 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.

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

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.

EvoSelf — Evolving self-organisation

Summary

Recent dramatic advances in the problem-solving capabilities and scale of Artificial Intelligence (AI) systems have enabled their successful application in challenging real-world scientific and engineering problems (Abramson et al 2024, Lam et al 2023). Yet these systems remain brittle to small disturbances and adversarial attacks (Su et al 2019, Cully 2014), lack human-level generalisation capabilities (Chollet 2019), and require alarming amounts of human, energy and financial resources (Strubel et al 2019).

Biological systems, on the other hand, seem to have largely solved many of these issues. They are capable of developing into complex organisms from a few cells and regenerating limbs through highly energy-efficient processes shaped by evolution. They do so through self-organisation: collectives of simple components interact locally with each other to give rise to macroscopic properties in the absence of centralised control (Camazine, 2001). This ability to self-organise renders organisms adaptive to their environments and robust to unexpected failures, as the redundancy built in the collective enables repurposing components, crucially, by leveraging the same self-organisation process that created the system in the first place.

Self-organisation lies at the core of many computational systems that exhibit properties such as robustness, adaptability, scalability and open-ended dynamics. Some examples are Cellular Automata (Von Neumann 1966), reaction-diffusion systems (Turing 1992, Mordvintsev 2021), particle systems (Reynolds 1987, Mordvintsev) , and Neural Cellular Automata (Mordvintsev et al 2020), showing promising results in pattern formation in high dimensional spaces such as images . Examples from neuroevolution are indirect encodings of neural networks inspired from morphogenesis such as cellular encodings (Gruau 1992), HyperNEAT (Stanley et al 2009), Hypernetworks (Ha 2016), HyperNCA (Najarro et al 2022) and Neural Developmental Programs (Najarro et al 2023, Nisioti et al 2024), showing improved robustness and generalisation.

Guiding self-organising systems through evolution is a long-standing and promising practise, yet the inherent complexity of the dynamics of these systems complicates their scaling to domains where gradient-based methods or simpler models excel (Risi 2021). If we view self-organising systems as genotype to phenotype mappings, we can leverage techniques developed in the evolutionary optimization community to understand how they alter evolutionary dynamics and guide them better.

The reverse is also possible: evolution can emerge as an inherent property of a self-organising system allowing us to study questions about the origin of life. Investigating under which conditions they appear, and the particular emergent evolutionary behaviours in these systems could afford insights applicable to existing artificial evolutionary approaches, or even directly provide an evolutionary substrate for learning tasks and achieving open-endedness. Early work in this direction (Ray 1992, Agüera y Arcas et al 2024, Fontana 1990, Adami et al 1994, Rasmussen et al 1991) has demonstrated emergent evolution in several computational substrates.

References
J. Abramson et al., “Accurate structure prediction of biomolecular interactions with AlphaFold 3,” Nature, pp. 1–3, May 2024, doi: 10.1038/s41586-024-07487-w.
J. Su, D. V. Vargas, and S. Kouichi, “One pixel attack for fooling deep neural networks,” IEEE Trans. Evol. Computat., vol. 23, no. 5, pp. 828–841, Oct. 2019, doi: 10.1109/TEVC.2019.2890858.
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Call for papers
We invite authors to submit papers through the Gecco submission system focused on the above subjects. We encourage two categories of submissions: papers of up to four pages showcasing early research ideas and papers up to 8 pages presenting more substantial contributions (such as technical contributions, benchmarks, negative results, surveys). Page count excludes references and appendices and submissions should follow the Gecco format . We encourage submissions related to evolution and self-organisation that address the following questions:
- How can we evolve artificial systems that exhibit robustness, generalisation and adaptability?
- What properties are missing from current self-organising systems? Can we design new ones?
- How can we analyse the trainability/navigability of self-organising systems?
- How can evolutionary processes such as self-replication emerge in a self-organising system?
- Which benchmarks/environments will reveal the benefit of self-organising systems?
- Which scientific and engineering domains would benefit from the development of such systems?

Organizers

Eleni Nisioti

Eleni is currently a post-doc with the Robotics, Evolution and Art Lab at IT University of Copenhagen, where she is working on the evolutionary optimization of developmental encodings for reinforcement learning agents. Prior to this, she was a post-doc at Inria, Bordeaux, where she worked on models of cultural evolution and the effect of ecological dynamics on learning.

 

Sebastian Risi

Professor at the IT University of Copenhagen where he co-directs the Robotics, Evolution and Art Lab (REAL). He is currently the principal investigator of a Sapere Aude: DFF Starting Grant (Innate: Adaptive Machines for Industrial Automation). He has won several international scientific awards, including multiple best paper awards, the Distinguished Young Investigator in Artificial Life 2018 award, a Google Faculty Research Award in 2019, and an Amazon Research Award in 2020. More information: sebastianrisi.com

 

Joachim Winther Pedersen

 

Ettore Randazzo

Ettore Randazzo is a Senior Software Engineer and Researcher at Google Research in Zürich, Switzerland. Prior to this he was a Research Assistant at the University of Illinois at Chicago where he acquired a Master’s degree in Computer Engineering. His interests include machine intelligence, complex artificial life, philosophy, ethics, logic and mathematics, gaming, music (including playing electric guitar and piano), and writing.

 

Alexander Mordvintsev

Alexander Mordvintsev is a Senior Software Engineer at Google Research in Zürich where he works on Deep Neural Network visualization, interpretation and understanding.. His interests include on Machine Learning, Computer Graphics and Vision.His primary interested is in visualizing emergent phenomena, his most known creation being DeepDream.

 

Eyvind Niklasson

Eyvind Niklasson is an AI Resident at Google Research in Zürich, Switzerland, where he among other topics conducts research in Neural Cellular Automatas and self-replicating programs. He has previously worked as a data scientist at Gro Intelligence developing unsupervised learning models. Before that, Eyvind worked as a research assistant at Cornell University attached to the Cornell High Energy Synchrotron Source.

GGP — Graph-based Genetic Programming

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.

Organizers

Roman Kalkreuth

Roman Kalkreuth is currently an assistant professor at The Chair of Artificial Intelligence Methodology of Professor Holger Hoos which belongs to RWTH Aachen University in Germany. Primarily, his research focuses on the analysis and development of algorithms for graph-based genetic programming. From 2015 until 2022, he was a research associate of the Computational Intelligence Research Group of Professor Günter Rudolph at TU Dortmund University (Germany). Roman Kalkreuth defended his PhD thesis in July 2021 and then took up a postdoctoral researcher position within Professor Rudolph’s group. From October 2022 to June 2023, he worked in the Natural Computing Research Group of Professor Dr. Thomas Bäck at the Leiden Institute of Advanced Computer Science, which is part of Leiden University. He joined Laboratoire d’Informatique de Paris 6 (LIP6) of Sorbonne University in Paris as a postdoctoral researcher under supervision of Carola Doerr from June 2023 until March 2024. He then took up an assistant professor position at RWTH Aachen University, which started in April 2024.

Yuri Lavinas

I’m an associate professor at the University of Toulouse 1 Capitole, France, at the Institut de Recherche en Informatique de Toulouse (IRIT) and I’m part of the REVA team. I did a postdoc research working with histopathology image analysis for cancer treatment with Genetic Programming in the IRIT@CRCT group. I got my PhD degree from the University of Tsukuba, Japan. Originally, I’m from Brazil, where I did my undergraduate course, at the University of Brasilia.

My research interests are related to Computational Intelligence, such as Evolutionary Computation and Artificial Life, with a greater focus on multi-objective optimization, fitness landscape and Genetic Programming. Overall, I’m interested in programs that can adapt themselves, in applications of Evolutionary Computation (black box optimization, multi-agent systems, games), as well as more speculative use of these Computational Intelligence for Artificial Life ( such as the evolution of virtual creatures and the worlds where the live).

Eric Medvet

Eric Medvet is an Associate Professor in Computer Engineering at the Department of Engineering and Architecture of University of Trieste, Italy. He is the founder and head of the Evolutionary Robotics and Artificial Life lab (ERALLab); he was the co-founder of the Machine Learning Lab. His research activities include evolutionary computation, artificial life, and the application of machine learning techniques to engineering and computer security problems. He authored more than 160 peer-reviewed articles on international journals or conferences, with more than 60 coauthors. He was a recipient of the Google Faculty Research Award 2020.

Giorgia Nadizar

Giorgia Nadizar is a Postdoctoral Research Fellow at the University of Trieste, Italy. She obtained her Ph.D. cum laude from the University of Trieste in 2025, but has explored various research environments through internships and research visits at the Oslo Metropolitan University (Oslo), the Centrum Wiskunde & Informatica (Amsterdam), the ISAE-Supaero (Toulouse), the MIT (Boston), and University of Toulouse Capitole (Toulouse). Her research interests lie at the intersection of embodied AI and explainable/interpretable AI.

Giovanni Squillero

Giovanni Squillero is a full professor of Computer Science at Politecnico di Torino, Department of Control and Computer Engineering. His research combines artificial intelligence and soft computing, in particular bio-inspired meta-heuristics and multi-agent systems. He also designs approximate optimization techniques able to achieve acceptable solutions with reasonable amount of resources. The industrial applications of his work range from electronic CAD to bioinformatics, to the cultural sector. As of October 2024, Squillero is credited as an author in about 200 publications and as an editor in 14 volumes. He has presented several tutorials at top conferences, and he has been invited to speak at international events. Squillero was the Program Chair of EvoSTAR in 2016 and 2017. He (co-)organized the workshops on Graph Genetic Programming (GECCO24); Evolutionary Machine Learning (PPSN18); Measuring and Promoting Diversity in Evolutionary Algorithms (GECCO16-17); Evolutionary Hardware Optimization (EvoSTAR04-14). As an entrepreneur, he co-founded Ominee, S.r.l. in 2014, Bactell, Inc. in 2019, and Ai·Culture, S.r.l. in 2024.

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.

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.

IAM 2025 — 10th Workshop on Industrial Applications of Metaheuristics (IAM 2025)

Summary

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 2025 include (but are not restricted to):

• Success stories for industrial applications of metaheuristics
• Pitfalls of industrial applications of metaheuristics.
• Metaheuristics to optimize dynamic industrial problems.
• Multi-objective optimization in real-world industrial problems.
• Meta-heuristics in very constraint industrial optimization problems: assuring feasibility, constraint-handling techniques.
• Reduction of computing times through parameter tuning and surrogate modelling.
• Parallelism and/or distributed design to accelerate computations.
• Algorithm selection and configuration for complex problem solving.
• Advantages and disadvantages of metaheuristics when compared to other techniques such as integer programming or constraint programming.
• New research topics for academic research inspired by real (algorithmic) needs in industrial applications.

Submission

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

Organizers

Silvino Fernández Alzueta

He is Senior Scientist in Mathematical Optimization at the Global R&D Division of ArcelorMittal with an experience of more than 15 years working in innovative projects applying Evolutionary Computation in real problems. He develops his activity in the ArcelorMittal R&D Centre of Asturias (Spain), in the framework of the Mathematical Optimization team, as part of the Digital Direction. 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 — 28th International Workshop on Evolutionary Rule-based Machine Learning

Summary

Modern machine learning systems, including generative AI and large language models (LLMs), 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 defence, 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 28th 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, …

- Generative AI and LLMs in ERL: integrating generative models and large language models for rule generation, natural language explanations, enhanced interpretability, …


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.

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 2025 — Landscape-Aware Heuristic Search

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 in charge of Electronic Media Affairs for SIGEVO. He has served as Electronic Media Chair for GECCO 2020 and 2021, 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 2017-2024.

Katherine Malan

Katherine Malan is a 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. Prior to this, he was an Associate Professor at the University of Lille from 2010 to 2023, and a Postdoctoral Researcher at the University of Coimbra in 2010. In 2020, he undertook a CNRS sabbatical at JFLI and was an Invited Professor at the University of Tokyo. From 2021, he has been appointed as a Collaborative Professor at Shinshu University, Japan. His research focuses on the foundations, design, and analysis of local search and evolutionary computation 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 received the best paper award at EvoCOP 2011, GECCO 2015, GECCO 2023, and WCCI/CEC 2024. He was the co-Program Chair of EvoCOP in 2018 and 2019, and took on various roles at GECCO: Proceedings Chair in 2018, co-EMO Track Chair in 2019, Virtualization Chair in 2021, co-Hybrid Scheduling Chair in 2023, and co-BBSR Track Chair in 2024. Currently, he serves as the Reproducibility Chair for the ACM Transactions on Evolutionary Learning and Optimization (TELO) and as the co-Track Chair of the Evolutionary Multi-objective Optimization (EMO) track for GECCO 2025.

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, gray-box optimisation, 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 45, and her work has obtained 8 best-paper awards and 11 other nominations. She collaborates cross-disciplines to apply evolutionary computation in healthcare and conservation. She has been active in organisation and editorial roles for: Genetic and Evolutionary Computation Conference (GECCO), Parallel Problem Solving from Nature (PPSN), EvoStar, Evolutionary Computation Journal (ECJ) and 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

Summary

Large language models (LLMs), along with other foundational models in generative AI, have significantly changed the traditional expectations of artificial intelligence and machine learning systems. An LLM takes natural language text prompts as input and generates responses by matching patterns and completing sequences, providing output in natural language. In contrast, evolutionary computation (EC) is inspired by Neo-Darwinian evolution and focuses on black-box search and optimization. But what connects these two approaches?

One answer is evolutionary search heuristics (LLM with EC), 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. LLMs may help researchers select 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 provide a pseudo-code, implementation, and reasoning for the proposed methodology. Finally, LLMs have the potential to transform automated metaheuristic design and configuration by generating codes, iteratively improving the initially designed solutions or algorithm templates (with or without performance or other data-driven feedback), and even guiding implementations.

This workshop aims to encourage innovative approaches that leverage the strengths of LLMs and EC techniques, thus enabling the creation of more adaptive, efficient, and scalable algorithms by integrating evolutionary mechanisms with advanced LLM capabilities. Thanks to the collaborative platform for researchers and practitioners, the workshop may Inspire novel research directions that could reshape AI, specifically LLMs, and optimization fields through this hybridization and achieve a better understanding and explanation of how these two seemingly disparate fields are related and how knowledge of their functions and operations can be leveraged.

It includes (but is not restricted to the following topics):
- Evolutionary Prompt Engineering
- Optimisation of LLM Architectures
- LLM-Guided Evolutionary Algorithms
- 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 a good EA plus LLM benchmark?
- LLMs for (automated) generation of EC.
- 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?
- Implementing/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.
- Connection of LLM and other ML techniques for EC (Reinforcement learning, AutoML)
- Generation and reasoning for parallel approaches for EC algorithms.
- Benchmarking and Comparative Studies of LLM-generated 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.

 

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/

 

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.

 

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.

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

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, investigating new trends in enhancing computational performance.

A closely related and rapidly growing field is Neural Architecture Search (NAS), which aims to automatically design high-performing neural network architectures by employing techniques from evolutionary computation and reinforcement learning. NAS methods strictly rely on neuroevolution principles to explore the vast space of possible neural network configurations and discover novel, efficient architectures. The tight coupling between neuroevolution and NAS highlights their synergistic relationship, with advancements in one field enabling progress in the other.
Although promising, the use of neuroevolution and NAS poses important problems and challenges for their future development.
Firstly, many of their paradigms suffer from a lack of parameter-space diversity, meaning a failure to provide diversity in the behaviours generated by the different networks.
Moreover, harnessing neuroevolution and NAS 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 and NAS in the next few 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 emphasizing neuroevolution and NAS 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 and neural architecture search, ranging from theoretical works to innovative applications in the context of (but not limited to):
• theoretical and experimental studies involving neuroevolution and NAS on machine learning in general, and deep and reinforcement learning in particular
• development of innovative neuroevolution and NAS paradigms
• parallel and distributed neuroevolution and NAS methods
• new search operators for neuroevolution and NAS
• hybrid methods for neuroevolution and NAS
• surrogate models for fitness estimation in neuroevolution and NAS
• adopt evolutionary multi-objective and many-objective optimisation techniques in neuroevolution and NAS
• propose new benchmark problems for neuroevolution and NAS
• applications of neuroevolution and NAS 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.

Mengjie Zhang

Prof Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of Engineering New Zealand, a Fellow of IEEE, an IEEE Distinguished Lecturer, currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation and Machine Learning Research Group. He is the Director of the Centre for Data Science and Artificial Intelligence at the University.

His research is mainly focused on AI, machine learning and big data, particularly in evolutionary learning and optimisation, feature selection/construction and big dimensionality reduction, computer vision and image analysis, scheduling and combinatorial optimisation, classification with unbalanced data and missing data, and evolutionary deep learning and transfer learning. Prof Zhang has published over 900 research papers in refereed international journals and conferences. He has been serving as an associated editor for over ten international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the Evolutionary Computation Journal (MIT Press), and involving many major AI and EC conferences as a chair. He received the “EvoStar/SPECIES Award for Outstanding Contribution to Evolutionary Computation in Europe” in 2023. Since 2007, he has been listed as a top five (currently No. 3) world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html). He is also a Clarivate Highly Cited Researcher in the field of Computer Science — 2023.

He is the Tutorial Chair for GECCO 2014, 2023 and 2024, 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.

Prof Zhang is currently the Chair for IEEE CIS Awards Committee. He is also a past Chair of the IEEE CIS Intelligent Systems Applications Technical Committee, the Emergent Technologies Technical Committee and the Evolutionary Computation Technical Committee, a past Chair for IEEE CIS PubsCom Strategic Planning subcommittee, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.

QuantOpt — Workshop on Quantum Optimization

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. Among other things, 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 and ACM Transactions on Evolutionary Learning and Optimization. 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 Control.

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.

SAEOpt — Workshop on Surrogate-Assisted Evolutionary Optimisation

Summary

In many real-world optimisation problems, evaluating the objective function(s) is expensive, perhaps requiring days of computation for a single evaluation. Surrogate-assisted optimisation attempts to alleviate this problem by employing computationally cheap 'surrogate' models to estimate the objective function(s) or the ranking relationships of the candidate solutions.

Surrogate-assisted approaches have been widely used across the field of evolutionary optimisation, including continuous and discrete variable problems, although little work has been done on combinatorial problems. Surrogates have been employed in solving a variety of optimization problems, such as multi-objective optimisation, dynamic optimisation, and robust optimisation. Surrogate-assisted methods have also found successful applications 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, is a 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 150 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, and Editor-in-Chief of GECCO 2022. He is an Associate Editor of ACM Transactions on Evolutionary Learning and Optimization and is on the IEEE Computational Intelligence Society (CIS) Task Forces on Data-Driven Evolutionary Optimisation of Expensive Problems, on Multi-modal Optimisation, and 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.

SymReg — 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. In recent years several non-evolutionary techniques for solving symbolic regression have emerged, most notably methods based on large language models (LLMs). 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.

The focus of this workshop is to further advance the state-of-the-art in symbolic regression and more general equation learning by gathering experts in the field and facilitating an exchange of research ideas. We encourage submissions presenting novel techniques or applications of symbolic regression, theoretical work, or algorithmic improvements to make the techniques more efficient, more reliable, and generally better controlled.

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 led the Josef Ressel Center for Symbolic Regression. In 2024, he published a book on "Symbolic Regression" together with Burlacu, Kommenda, Winkler, and Affenzeller. His current research interests are symbolic regression for physics-based machine learning and applications in science and engineering. 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. More information: (https://symreg.at)

 

Fabricio Olivetti de França

Fabricio is an Associated Professor at Federal University of ABC (UFABC), Brazil.
He received his MsC and PhD from State University of Campinas (UNICAMP) with a focus on
data clustering and multimodal optimization. His current research focuses on interpretable
models with Symbolic Regression and real-world applications.

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 GECCO2009 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. Dr. Gustafson was the
CTO at Noonum, a FinTech startup that delivers insights on companies and markets using advances in NLP and AI and the Chief Scientist at BigFilter.ai a company focused on AI safety and alignment technology. Currently he is assistant professor at the University of
Washington.