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.

M. Vinzent Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,”10.1109/ssci52147.2023.10372008__2024, doi:10.1109/ssci52147.2023.10372008 .

R. P. Prager et al., “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features,”10.1145/3594805.3607136__in Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023, pp. 129–139, doi:10.1145/3594805.3607136 .

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.

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.

R. P. Prager and H. Trautmann, “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python,”10.1162/evco_a_00341__Evolutionary Computation, pp. 1–25, 2023, doi:10.1162/evco_a_00341 .

J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “A study on the effects of normalized TSP features for automated algorithm selection,”https://doi.org/10.1016/j.tcs.2022.10.019__Theoretical Computer Science, vol. 940, pp. 123–145, 2023, doi:https://doi.org/10.1016/j.tcs.2022.10.019 .

R. P. Prager and H. Trautmann, “Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems,”10.1145/3583133.3590757__in Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023, 2023, pp. 451–454, doi:10.1145/3583133.3590757 .

M. V. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,”10.1109/SSCI52147.2023.10372008__in 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 361–368, doi:10.1109/SSCI52147.2023.10372008 .

M. Niemann, D. Assenmacher, J. Brunk, D. M. Riehle, J. Becker, and H. Trautmann, “(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse,” in Hate Speech — Definitionen, Ausprägungen, Lösungen, G. Weitzel and S. Mündges, Eds. Wiesbaden: VS Verlag für Sozialwissenschaften, 2022, pp. 249–274.

D. Assenmacher and H. Trautmann, “Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption,”10.1007/978-3-031-21743-2_1__in Intelligent Information and Database Systems, 2022, pp. 3–16, doi:10.1007/978-3-031-21743-2_1 .

D. Assenmacher et al., “Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem,”10.1177/08944393211012268__Social Science Computer Review, vol. 40, no. 6, pp. 1496–1522, 2022, doi:10.1177/08944393211012268 .

L. Schneider, L. Schäpermeier, R. P. Prager, B. Bischl, H. Trautmann, and P. Kerschke, “HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis,”10.1007/978-3-031-14714-2_40__in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 575–589, doi:10.1007/978-3-031-14714-2_40 .

M. V. Seiler, R. P. Prager, P. Kerschke, and H. Trautmann, “A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes,”10.1145/3512290.3528834__in Proceedings of the Genetic and Evolutionary Computation Conference, 2022, pp. 657–665, doi:10.1145/3512290.3528834 .

R. P. Prager, M. V. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods,”10.1007/978-3-031-14714-2_1__in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 3–17, doi:10.1007/978-3-031-14714-2_1 .

J. S. Pohl, D. Assenmacher, M. V. Seiler, H. Trautmann, and C. Grimme, “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,”10.36190/2022.91__in Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), 2022, pp. 1–10, doi:10.36190/2022.91 .

J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,”10.1145/3520304.3528998__in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, pp. 356–359–356–359, doi:10.1145/3520304.3528998 .

L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented Stream Classification Pipeline: A Literature Review,”10.3390/app12189094__Applied Sciences, vol. 12, no. 8, pp. 1–44, 2022, doi:10.3390/app12189094 .

P. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, and C. Grimme, “The objective that freed me: a multi-objective local search approach for continuous single-objective optimization,”10.1007/s11047-022-09919-w__Natural Computing, vol. 1, pp. 1–15, 2022, doi:10.1007/s11047-022-09919-w .

J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann, “BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 192–206.