13 Publications
2024 | Conference Paper | LibreCat-ID: 54643
M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, and H. Trautmann, “Learned Features vs. Classical ELA on Affine BBOB Functions,” in Parallel Problem Solving from Nature — PPSN XVIII, 2024, pp. 1–14.
LibreCat
2024 | Conference Paper | LibreCat-ID: 52749
M. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,” 2024, doi: 10.1109/ssci52147.2023.10372008.
LibreCat
| DOI
2024 | Conference Paper | LibreCat-ID: 58335
M. Seiler, U. Skvorc, C. Doerr, and H. Trautmann, “Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection,” in Learning and Intelligent Optimization - 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers, 2024, vol. 14990, pp. 361–376, doi: 10.1007/978-3-031-75623-8_29.
LibreCat
| DOI
2023 | Journal Article | LibreCat-ID: 46310
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “A study on the effects of normalized TSP features for automated algorithm selection,” Theoretical Computer Science, vol. 940, pp. 123–145, 2023, doi: https://doi.org/10.1016/j.tcs.2022.10.019.
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 48898
M. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,” in 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 361–368, doi: 10.1109/SSCI52147.2023.10372008.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46307
M. Seiler, R. P. Prager, P. Kerschke, and H. Trautmann, “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, 2022, pp. 657–665, doi: 10.1145/3512290.3528834.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46304
R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 3–17, doi: 10.1007/978-3-031-14714-2_1.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46303
J. S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, and C. Grimme, “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,” in Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), 2022, pp. 1–10, doi: 10.36190/2022.91.
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46315
R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8, doi: 10.1109/SSCI50451.2021.9660174.
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46312
D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D. M. Riehle, and H. Trautmann, “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, pp. 1–14.
LibreCat
2021 | Conference Paper | LibreCat-ID: 46313
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI), 2021, pp. 1–15, doi: 10.1145/3450218.3477308.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46331
M. Seiler, H. Trautmann, and P. Kerschke, “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8, doi: 10.1109/IJCNN48605.2020.9207338.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46330
M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.
LibreCat
| DOI
13 Publications
2024 | Conference Paper | LibreCat-ID: 54643
M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, and H. Trautmann, “Learned Features vs. Classical ELA on Affine BBOB Functions,” in Parallel Problem Solving from Nature — PPSN XVIII, 2024, pp. 1–14.
LibreCat
2024 | Conference Paper | LibreCat-ID: 52749
M. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,” 2024, doi: 10.1109/ssci52147.2023.10372008.
LibreCat
| DOI
2024 | Conference Paper | LibreCat-ID: 58335
M. Seiler, U. Skvorc, C. Doerr, and H. Trautmann, “Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection,” in Learning and Intelligent Optimization - 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers, 2024, vol. 14990, pp. 361–376, doi: 10.1007/978-3-031-75623-8_29.
LibreCat
| DOI
2023 | Journal Article | LibreCat-ID: 46310
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “A study on the effects of normalized TSP features for automated algorithm selection,” Theoretical Computer Science, vol. 940, pp. 123–145, 2023, doi: https://doi.org/10.1016/j.tcs.2022.10.019.
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 48898
M. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,” in 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 361–368, doi: 10.1109/SSCI52147.2023.10372008.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46307
M. Seiler, R. P. Prager, P. Kerschke, and H. Trautmann, “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, 2022, pp. 657–665, doi: 10.1145/3512290.3528834.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46304
R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 3–17, doi: 10.1007/978-3-031-14714-2_1.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46303
J. S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, and C. Grimme, “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,” in Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), 2022, pp. 1–10, doi: 10.36190/2022.91.
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46315
R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8, doi: 10.1109/SSCI50451.2021.9660174.
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46312
D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D. M. Riehle, and H. Trautmann, “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, pp. 1–14.
LibreCat
2021 | Conference Paper | LibreCat-ID: 46313
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI), 2021, pp. 1–15, doi: 10.1145/3450218.3477308.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46331
M. Seiler, H. Trautmann, and P. Kerschke, “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8, doi: 10.1109/IJCNN48605.2020.9207338.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46330
M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.
LibreCat
| DOI