Preuß, O. L., Rook, J., &; Trautmann, H. (2024). On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems. In Applications of Evolutionary Computation. Springer Nature Switzerland.https://doi.org/10.1007/978-3-031-56852-7_20 
LibreCat: /record/52759
DOI: 10.1007/978-3-031-56852-7_20

Vinzent Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., &; Trautmann, H. (2024). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI).https://doi.org/10.1109/ssci52147.2023.10372008 
LibreCat: /record/52749
DOI: 10.1109/ssci52147.2023.10372008

Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke, P., Trautmann, H., &; Mersmann, O. (2023). Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 129–139.https://doi.org/10.1145/3594805.3607136 
LibreCat: /record/47522
DOI: 10.1145/3594805.3607136

Prager, R. P., &; Trautmann, H. (2023). Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In J. Correia, S. Smith, &; R. Qaddoura (Eds.), Applications of Evolutionary Computation (pp. 411–425). Springer Nature Switzerland.
LibreCat: /record/46297

Schäpermeier, L., Kerschke, P., Grimme, C., &; Trautmann, H. (2023). Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In M. Emmerich, A. Deutz, H. Wang, A. V. Kononova, B. Naujoks, K. Li, K. Miettinen, &; I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization (pp. 291–304). Springer Nature Switzerland.
LibreCat: /record/46298

Prager, R. P., &; Trautmann, H. (2023). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evolutionary Computation, 1–25.https://doi.org/10.1162/evco_a_00341 
LibreCat: /record/46299
DOI: 10.1162/evco_a_00341

Bossek, J., Neumann, A., &; Neumann, F. (2023). On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 248–256.https://doi.org/10.1145/3583131.3590384 
LibreCat: /record/48869
DOI: 10.1145/3583131.3590384

Bossek, J., &; Sudholt, D. (2023). Runtime Analysis of Quality Diversity Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, 1546–1554.https://doi.org/10.1145/3583131.3590383 
LibreCat: /record/48872
DOI: 10.1145/3583131.3590383

Marrero, A., Segredo, E., Hart, E., Bossek, J., &; Neumann, A. (2023). Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions of Feature-Space. Proceedings of the Genetic and Evolutionary Computation Conference, 312–320.https://doi.org/10.1145/3583131.3590504 
LibreCat: /record/48886
DOI: 10.1145/3583131.3590504

Bossek, J., &; Sudholt, D. (2023). Do Additional Target Points Speed Up Evolutionary Algorithms? Theoretical Computer Science, 113757.https://doi.org/10.1016/j.tcs.2023.113757 
LibreCat: /record/48871
DOI: 10.1016/j.tcs.2023.113757

Bossek, J., &; Grimme, C. (2023). On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. Evolutionary Computation, 1–35.https://doi.org/10.1162/evco_a_00335 
LibreCat: /record/48859
DOI: 10.1162/evco_a_00335

Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &; Kerschke, P. (2023). A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science, 940, 123–145.https://doi.org/10.1016/j.tcs.2022.10.019 
LibreCat: /record/46310
DOI: https://doi.org/10.1016/j.tcs.2022.10.019

Prager, R. P., &; Trautmann, H. (2023). Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems. 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 (pp. 451–454). ACM.https://doi.org/10.1145/3583133.3590757 
LibreCat: /record/52530
DOI: 10.1145/3583133.3590757

Seiler, M. V., Rook, J., Heins, J., Preuß, O. L., Bossek, J., &; Trautmann, H. (n.d.). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361–368.https://doi.org/10.1109/SSCI52147.2023.10372008 
LibreCat: /record/48898
DOI: 10.1109/SSCI52147.2023.10372008

Niemann, M., Assenmacher, D., Brunk, J., Riehle, D. M., Becker, J., &; Trautmann, H. (2022). (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In G. Weitzel &; S. Mündges (Eds.), Hate Speech — Definitionen, Ausprägungen, Lösungen (pp. 249–274). VS Verlag für Sozialwissenschaften.https://doi.org/10.1007/978-3-658-35658-3_13 
LibreCat: /record/46300
DOI: 10.1007/978-3-658-35658-3_13

Assenmacher, D., &; Trautmann, H. (2022). Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In T. et al. Tran (Ed.), Intelligent Information and Database Systems (pp. 3–16). Springer International Publishing.https://doi.org/10.1007/978-3-031-21743-2_1 
LibreCat: /record/46301
DOI: 10.1007/978-3-031-21743-2_1

Assenmacher, D., Weber, D., Preuss, M., Valdez, A. C., Bradshaw, A., Ross, B., Cresci, S., Trautmann, H., Neumann, F., &; Grimme, C. (2022). Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. Social Science Computer Review, 40(6), 1496–1522.https://doi.org/10.1177/08944393211012268 
LibreCat: /record/46316
DOI: 10.1177/08944393211012268

Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H., &; Kerschke, P. (2022). HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &; T. Tusar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 575–589). Springer International Publishing.https://doi.org/10.1007/978-3-031-14714-2_40 
LibreCat: /record/46306
DOI: 10.1007/978-3-031-14714-2_40

Seiler, M. V., Prager, R. P., Kerschke, P., &; Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. Proceedings of the Genetic and Evolutionary Computation Conference, 657–665.https://doi.org/10.1145/3512290.3528834 
LibreCat: /record/46307
DOI: 10.1145/3512290.3528834

Prager, R. P., Seiler, M. V., Trautmann, H., &; Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &; T. Tusar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Springer International Publishing.https://doi.org/10.1007/978-3-031-14714-2_1 
LibreCat: /record/46304
DOI: 10.1007/978-3-031-14714-2_1