Bio: Aneta Neumann is a researcher in the School of Computer and Mathematical Sciences at the University of Adelaide, Australia, and focuses on real world problems using evolutionary computation and machine learning methods. She is also part of the Integrated Mining Consortium at the University of Adelaide. Aneta graduated in Computer Science from the Christian-Albrechts-University of Kiel, Germany, and received her PhD from the University of Adelaide, Australia. She served as the co-chair of the Real-World Applications track at GECCO 2021-2022 and was the co-chair of the Genetic Algorithms track at GECCO 2023-2024. She is co-organizer of the Workshop on AI-based Optimisation 2025 and a publicity co-chair for EMO 2025. Her main research interests are bio-inspired computation methods, with a particular focus on dynamic and stochastic multi-objective optimization for real-world problems that occur in the mining industry, green energy, defence, creative industries, and public health.

Saba Sadeghi Ahouei is a PhD student in computer science at the University of Adelaide. She received her MSc in Industrial Engineering at Sharif University of Technology in 2021. Her main research interests are Stochastic Optimization, Chance-constrained Optimization, Evolutionary Algorithms, Algorithm Selection and Configuration, and Benchmarking Optimization Algorithms.

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.

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.

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.

Frank Neumann is a professor and the leader of the Optimisation and Logistics group at the University of Adelaide and an Honorary Professorial Fellow at the University of Melbourne. His current position is funded by the Australian Research Council through a Future Fellowship and focuses on AI-based optimisation methods for problems with stochastic constraints. Frank has been the general chair of the ACM GECCO 2016 and co-organised ACM FOGA 2013 in Adelaide. He is an Associate Editor of the journals "Evolutionary Computation" (MIT Press) and ACM Transactions on Evolutionary Learning and Optimization. In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of cybersecurity, renewable energy, logistics, and mining.