Anytime Algorithms for Many-affine BBOB Functions
Deadline: 2025-07-06
Webpage: https://iohprofiler.github.io/competitions/mabbob25
Description
The many-affine BBOB function suite MA-BBOB extends the classic BBOB benchmark suite of the COCO environment by combining its 24 base functions. With this competition, we solicit algorithms designed to optimize anytime performance on the MA-BBOB functions.
The MA-BBOB suite, introduced in https://dl.acm.org/doi/abs/10.1145/3673908, is available as part of the IOHprofiler, https://iohprofiler.github.io/.
Participation is simple: we provide 1,000 training instances for which we have already evaluated some baseline algorithms. You are free to use these and you can use as many training instances from the MA-BBOB framework as you like. We also provide a notebook to process and compare your results to these baselines. To participate in the competition, you submit a link to your code that we then execute on the test instances, sampled from the same distribution as the training instances. For details about the computational budget, problem dimensions, test instances, etc., please confer the dedicated competition website at https://iohprofiler.github.io/competitions/mabbob25
We particularly welcome submissions that are accompanied by a short paper or technical report (up to 2 pages following the GECCO formatting requirements) describing the key approaches used by the submitted algorithms. Each team can submit a total of up to two algorithms.
Organizers
Diederick Vermetten is a PhD student at LIACS. He is part of the core development team of IOHprofiler, with a focus on the IOHanalyzer. His research interests include benchmarking of optimization heuristics, dynamic algorithm selection and configuration as well as hyperparameter optimization.
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.
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.
Jacob de Nobel is a PhD student at LIACS, and is currently one of the core developers for the IOHexperimenter. His research concerns the real world application of optimization algorithms for finding better speech encoding strategies for cochlear implants, which are neuroprosthesis for people with profound hearing loss.