Please note that LibreCat no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
13 Publications
2024 | Conference Paper | LibreCat-ID: 54643
Seiler M, Skvorc U, Cenikj G, Doerr C, Trautmann H. Learned Features vs. Classical ELA on Affine BBOB Functions. In: Affenzeller M, Winkler S, Kononova A, et al., eds. Parallel Problem Solving from Nature — PPSN XVIII. Springer International Publishing; 2024:1-14.
LibreCat
2024 | Conference Paper | LibreCat-ID: 52749
Seiler M, Rook J, Heins J, Preuß OL, Bossek J, Trautmann H. Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2024. doi:10.1109/ssci52147.2023.10372008
LibreCat
| DOI
2024 | Conference Paper | LibreCat-ID: 58335
Seiler M, Skvorc U, Doerr C, Trautmann H. Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection. In: Festa P, Ferone D, Pastore T, Pisacane O, eds. Learning and Intelligent Optimization - 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers. Vol 14990. Lecture Notes in Computer Science. Springer; 2024:361–376. doi:10.1007/978-3-031-75623-8_29
LibreCat
| DOI
2023 | Journal Article | LibreCat-ID: 46310
Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science. 2023;940:123-145. doi:https://doi.org/10.1016/j.tcs.2022.10.019
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 48898
Seiler M, Rook J, Heins J, Preuß OL, Bossek J, Trautmann H. Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). ; :361-368. doi:10.1109/SSCI52147.2023.10372008
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46307
Seiler M, Prager RP, Kerschke P, Trautmann H. A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery; 2022:657–665. doi:10.1145/3512290.3528834
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46304
Prager RP, Seiler M, Trautmann H, Kerschke P. Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. Parallel Problem Solving from Nature — PPSN XVII. Springer International Publishing; 2022:3–17. doi:10.1007/978-3-031-14714-2_1
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46303
Pohl JS, Assenmacher D, Seiler M, Trautmann H, Grimme C. Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In: the Advancement of Artificial Intelligence (AAAI) Association for, ed. Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM). AAAI Press; 2022:1–10. doi:10.36190/2022.91
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46315
Prager RP, Seiler M, Trautmann H, Kerschke P. Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). ; 2021:1-8. doi:10.1109/SSCI50451.2021.9660174
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46312
Assenmacher D, Niemann M, Müller K, Seiler M, Riehle DM, Trautmann H. RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021). ; 2021:1–14.
LibreCat
2021 | Conference Paper | LibreCat-ID: 46313
Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: Computing Machinery Association for, ed. Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI). Association for Computing Machinery; 2021:1–15. doi:10.1145/3450218.3477308
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46331
Seiler M, Trautmann H, Kerschke P. Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN). ; 2020:1–8. doi:10.1109/IJCNN48605.2020.9207338
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46330
Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: Bäck T, Preuss M, Deutz A, et al., eds. Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI). ; 2020:48–64. doi:10.1007/978-3-030-58112-1_4
LibreCat
| DOI