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190 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
LibreCat
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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
LibreCat
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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.
LibreCat
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.
LibreCat
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 | Conference Paper | LibreCat-ID: 48869
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
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2023 | Conference Paper | LibreCat-ID: 48872
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
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2023 | Conference Paper | LibreCat-ID: 48886
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
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2023 | Journal Article | LibreCat-ID: 48871
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
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2023 | Journal Article | LibreCat-ID: 48859
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
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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
LibreCat
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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
LibreCat
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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
LibreCat
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2023 | Conference Paper | LibreCat-ID: 52863
Ŝkvorc, U., Eftimov, T., & Koro]ec, P. (2023). Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization. 2023 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci52147.2023.10371868
LibreCat
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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
LibreCat
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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
LibreCat
| DOI
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
LibreCat
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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
LibreCat
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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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