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130 Publications


2024 | Book Chapter | LibreCat-ID: 52759
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
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2024 | Conference Paper | LibreCat-ID: 52749
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
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2023 | Conference Paper | LibreCat-ID: 47522
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
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2023 | Conference Paper | LibreCat-ID: 46297
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.
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2023 | Conference Paper | LibreCat-ID: 46298
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.
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2023 | Journal Article | LibreCat-ID: 46299
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
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2023 | Journal Article | LibreCat-ID: 46310
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
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2023 | Conference Paper | LibreCat-ID: 52530
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
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2023 | Conference Paper | LibreCat-ID: 48898
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
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2022 | Book Chapter | LibreCat-ID: 46300
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
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2022 | Conference Paper | LibreCat-ID: 46301
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
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2022 | Journal Article | LibreCat-ID: 46316
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
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2022 | Conference Paper | LibreCat-ID: 46306
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. Tušar (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
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2022 | Conference Paper | LibreCat-ID: 46307
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
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2022 | Conference Paper | LibreCat-ID: 46304
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. Tušar (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
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2022 | Conference Paper | LibreCat-ID: 46303
Pohl, J. S., Assenmacher, D., Seiler, M. V., Trautmann, H., & Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM) (pp. 1–10). AAAI Press. https://doi.org/10.36190/2022.91
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2022 | Conference Paper | LibreCat-ID: 46305
Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In J. Fieldsend & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 356–359–356–359). Association for Computing Machinery. https://doi.org/10.1145/3520304.3528998
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2022 | Journal Article | LibreCat-ID: 46309
Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(8), 1–44. https://doi.org/10.3390/app12189094
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2022 | Journal Article | LibreCat-ID: 46308
Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., Trautmann, H., & Grimme, C. (2022). The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Natural Computing, 1, 1–15. https://doi.org/10.1007/s11047-022-09919-w
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2022 | Conference Paper | LibreCat-ID: 46302
Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 192–206). Springer International Publishing.
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