On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems
O.L. Preuß, J. Rook, H. Trautmann, in: Applications of Evolutionary Computation, Springer Nature Switzerland, Cham, 2024.

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
M. Vinzent Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2024.

Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features
R.P. Prager, K. Dietrich, L. Schneider, L. Schäpermeier, B. Bischl, P. Kerschke, H. Trautmann, O. Mersmann, in: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2023, pp. 129–139.

Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features
R.P. Prager, H. Trautmann, in: J. Correia, S. Smith, R. Qaddoura (Eds.), Applications of Evolutionary Computation, Springer Nature Switzerland, Cham, 2023, pp. 411–425.

Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets
L. Schäpermeier, P. Kerschke, C. Grimme, H. Trautmann, in: M. Emmerich, A. Deutz, H. Wang, A.V. Kononova, B. Naujoks, K. Li, K. Miettinen, I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization, Springer Nature Switzerland, Cham, 2023, pp. 291–304.

Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python
R.P. Prager, H. Trautmann, Evolutionary Computation (2023) 1–25.

A study on the effects of normalized TSP features for automated algorithm selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145.

Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems
R.P. Prager, H. Trautmann, in: S. Silva, L. Paquete (Eds.), Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023, ACM, 2023, pp. 451–454.

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
M.V. Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), n.d., pp. 361–368.

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

Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption
D. Assenmacher, H. Trautmann, in: T. et al. Tran (Ed.), Intelligent Information and Database Systems, Springer International Publishing, Cham, 2022, pp. 3–16.

Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem
D. Assenmacher, D. Weber, M. Preuss, A.C. Valdez, A. Bradshaw, B. Ross, S. Cresci, H. Trautmann, F. Neumann, C. Grimme, Social Science Computer Review 40 (2022) 1496–1522.

HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis
L. Schneider, L. Schäpermeier, R.P. Prager, B. Bischl, H. Trautmann, P. Kerschke, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 575–589.

A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes
M.V. Seiler, R.P. Prager, P. Kerschke, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 657–665.

Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods
R.P. Prager, M.V. Seiler, H. Trautmann, P. Kerschke, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 3–17.

Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches
J.S. Pohl, D. Assenmacher, M.V. Seiler, H. Trautmann, C. Grimme, in:  for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), AAAI Press, Palo Alto, CA, USA, 2022, pp. 1–10.

On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems
J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: J. Fieldsend, M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359–356–359.

Process-Oriented Stream Classification Pipeline: A Literature Review
L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 1–44.

The objective that freed me: a multi-objective local search approach for continuous single-objective optimization
P. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, C. Grimme, Natural Computing 1 (2022) 1–15.

BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems
J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann, in: G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 192–206.