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204 Publications
2025 | Conference Paper | LibreCat-ID: 60219
D. Vermetten et al., “MO-IOHinspector: Anytime Benchmarking of Multi-objective Algorithms Using IOHprofiler,” in Evolutionary Multi-Criterion Optimization - 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025, Proceedings, Part I, 2025, vol. 15512, pp. 242–256, doi: 10.1007/978-981-96-3506-1_17.
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2025 | Journal Article | LibreCat-ID: 60220
M. Seiler, P. Kerschke, and H. Trautmann, “Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems ,” Evolutionary Computation, pp. 1–27, 2025, doi: https://doi.org/10.1162/evco_a_00367.
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2025 | Conference Paper | LibreCat-ID: 60813
M. Seiler, O. L. Preuß, and H. Trautmann, “RandOptGen: A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025, 2025, pp. 76–84, doi: 10.1145/3712256.3726478.
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2025 | Conference Paper | LibreCat-ID: 60812
O. L. Preuß, C. Mensendiek, J. Rook, J. Bossek, and H. Trautmann, “Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025, 2025, pp. 58–66, doi: 10.1145/3712256.3726481.
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2025 | Conference Paper | LibreCat-ID: 60814
E. Schede, M. Seiler, K. Tierney, and H. Trautmann, “Deep reinforcement learning for instance-specific algorithm configuration (GECCO Best Paper Award),” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025, 2025, pp. 1190–1198, doi: 10.1145/3712256.3726480.
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2025 | Conference Paper | LibreCat-ID: 61918
J. Rook, Q. Renau, H. Trautmann, and E. Hart, “Efficient Online Automated Algorithm Selection in the Face of Data-Drift in Optimisation Problem Instances,” in Proceedings of the 18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2025, Leiden, The Netherlands, August 27-29, 2025, 2025, pp. 262–272, doi: 10.1145/3729878.3746615.
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2025 | Journal Article | LibreCat-ID: 63053
C. Hernández, A. E. Rodriguez-Fernandez, L. Schäpermeier, O. Cuate, H. Trautmann, and O. Schütze, “An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2025, doi: 10.1109/TEVC.2025.3637276.
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2024 | Journal Article | LibreCat-ID: 54548
R. P. Prager and H. Trautmann, “Exploratory Landscape Analysis for Mixed-Variable Problems,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2024, doi: 10.1109/TEVC.2024.3399560.
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2024 | Conference Paper | LibreCat-ID: 54642
K. Dietrich, R. Prager, C. Doerr, and H. Trautmann, “Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization,” in Parallel Problem Solving from Nature — PPSN XVIII, 2024, pp. 1–14.
LibreCat
2024 | Conference Paper | LibreCat-ID: 54643
M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, and H. Trautmann, “Learned Features vs. Classical ELA on Affine BBOB Functions,” in Parallel Problem Solving from Nature — PPSN XVIII, 2024, pp. 1–14.
LibreCat
2024 | Book Chapter | LibreCat-ID: 52759
O. L. Preuß, J. Rook, and H. Trautmann, “On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems,” in Applications of Evolutionary Computation, Cham: Springer Nature Switzerland, 2024.
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2024 | Conference Paper | LibreCat-ID: 52749
M. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,” 2024, doi: 10.1109/ssci52147.2023.10372008.
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2024 | Conference Paper | LibreCat-ID: 58335
M. Seiler, U. Skvorc, C. Doerr, and H. Trautmann, “Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection,” in Learning and Intelligent Optimization - 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers, 2024, vol. 14990, pp. 361–376, doi: 10.1007/978-3-031-75623-8_29.
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2023 | Conference Paper | LibreCat-ID: 47522
R. P. Prager et al., “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features,” in Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023, pp. 129–139, doi: 10.1145/3594805.3607136.
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2023 | Conference Paper | LibreCat-ID: 46297
R. P. Prager and H. Trautmann, “Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features,” in Applications of Evolutionary Computation, 2023, pp. 411–425.
LibreCat
2023 | Conference Paper | LibreCat-ID: 46298
L. Schäpermeier, P. Kerschke, C. Grimme, and H. Trautmann, “Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets,” in Evolutionary Multi-Criterion Optimization, 2023, pp. 291–304.
LibreCat
2023 | Journal Article | LibreCat-ID: 46299
R. P. Prager and H. Trautmann, “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python,” Evolutionary Computation, pp. 1–25, 2023, doi: 10.1162/evco_a_00341.
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