@inproceedings{60219,
  author       = {{Vermetten, Diederick and Rook, Jeroen and Preuß, Oliver Ludger and de Nobel, Jacob and Doerr, Carola and López-Ibáñez, Manuel and Trautmann, Heike and Bäck, Thomas}},
  booktitle    = {{Evolutionary Multi-Criterion Optimization - 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025, Proceedings, Part I}},
  editor       = {{Singh, Hemant K. and Ray, Tapabrata and Knowles, Joshua D. and Li, Xiaodong and Branke, Juergen and Wang, Bing and Oyama, Akira}},
  pages        = {{242–256}},
  publisher    = {{Springer}},
  title        = {{{MO-IOHinspector: Anytime Benchmarking of Multi-objective Algorithms Using IOHprofiler}}},
  doi          = {{10.1007/978-981-96-3506-1_17}},
  volume       = {{15512}},
  year         = {{2025}},
}

@inproceedings{60813,
  author       = {{Seiler, Moritz and Preuß, Oliver Ludger and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025}},
  editor       = {{Filipic, Bogdan}},
  pages        = {{76–84}},
  publisher    = {{ACM}},
  title        = {{{RandOptGen: A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces}}},
  doi          = {{10.1145/3712256.3726478}},
  year         = {{2025}},
}

@inproceedings{60812,
  author       = {{Preuß, Oliver Ludger and Mensendiek, Carolin and Rook, Jeroen and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025}},
  editor       = {{Filipic, Bogdan}},
  pages        = {{58–66}},
  publisher    = {{ACM}},
  title        = {{{Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes}}},
  doi          = {{10.1145/3712256.3726481}},
  year         = {{2025}},
}

@inbook{52759,
  author       = {{Preuß, Oliver Ludger and Rook, Jeroen and Trautmann, Heike}},
  booktitle    = {{Applications of Evolutionary Computation}},
  isbn         = {{9783031568510}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems}}},
  doi          = {{10.1007/978-3-031-56852-7_20}},
  year         = {{2024}},
}

@inproceedings{52749,
  author       = {{Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{2023 IEEE Symposium Series on Computational Intelligence (SSCI)}},
  publisher    = {{IEEE}},
  title        = {{{Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}}},
  doi          = {{10.1109/ssci52147.2023.10372008}},
  year         = {{2024}},
}

@inproceedings{60131,
  author       = {{Preuß, Oliver Ludger and Rook, Jeroen and Trautmann, Heike}},
  booktitle    = {{Applications of Evolutionary Computation - 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5, 2024, Proceedings, Part I}},
  editor       = {{Smith, Stephen L. and Correia, João and Cintrano, Christian}},
  pages        = {{305–321}},
  publisher    = {{Springer}},
  title        = {{{On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems}}},
  doi          = {{10.1007/978-3-031-56852-7_20}},
  volume       = {{14634}},
  year         = {{2024}},
}

@inproceedings{48898,
  abstract     = {{Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin\textendash Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100000 TSP instances with 2000 nodes each \textemdash currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets.}},
  author       = {{Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{2023 IEEE Symposium Series on Computational Intelligence (SSCI)}},
  pages        = {{361 -- 368}},
  title        = {{{Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}}},
  doi          = {{10.1109/SSCI52147.2023.10372008}},
  year         = {{2023}},
}