13 Publications
2024 | Conference Paper | LibreCat-ID: 54643
Seiler, Moritz, Urban Skvorc, G Cenikj, C Doerr, and Heike Trautmann. “Learned Features vs. Classical ELA on Affine BBOB Functions.” In Parallel Problem Solving from Nature — PPSN XVIII, edited by M Affenzeller, S Winkler, A Kononova, H Trautmann, T Tušar, P Machado, and T Baeck, 1–14. Cham: Springer International Publishing, 2024.
LibreCat
2024 | Conference Paper | LibreCat-ID: 52749
Seiler, Moritz, Jeroen Rook, Jonathan Heins, Oliver Ludger Preuß, Jakob Bossek, and Heike Trautmann. “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP.” In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2024. https://doi.org/10.1109/ssci52147.2023.10372008.
LibreCat
| DOI
2024 | Conference Paper | LibreCat-ID: 58335
Seiler, Moritz, Urban Skvorc, Carola Doerr, and Heike 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, edited by Paola Festa, Daniele Ferone, Tommaso Pastore, and Ornella Pisacane, 14990:361–376. Lecture Notes in Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-75623-8_29.
LibreCat
| DOI
2023 | Journal Article | LibreCat-ID: 46310
Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.” Theoretical Computer Science 940 (2023): 123–45. https://doi.org/10.1016/j.tcs.2022.10.019.
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 48898
Seiler, Moritz, Jeroen Rook, Jonathan Heins, Oliver Ludger Preuß, Jakob Bossek, and Heike Trautmann. “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP.” In 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361–68, n.d. https://doi.org/10.1109/SSCI52147.2023.10372008.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46307
Seiler, Moritz, Raphael Patrick Prager, Pascal Kerschke, and Heike 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, 657–665. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3512290.3528834.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46304
Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal 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, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 3–17. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_1.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46303
Pohl, Janina Susanne, Dennis Assenmacher, Moritz Seiler, Heike Trautmann, and Christian 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), edited by for the Advancement of Artificial Intelligence (AAAI) Association, 1–10. Palo Alto, CA, USA: AAAI Press, 2022. https://doi.org/10.36190/2022.91.
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46315
Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8, 2021. https://doi.org/10.1109/SSCI50451.2021.9660174.
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46312
Assenmacher, Dennis, Marco Niemann, Kilian Müller, Moritz Seiler, Dennis M. Riehle, and Heike 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), 1–14. Virtual Event, 2021.
LibreCat
2021 | Conference Paper | LibreCat-ID: 46313
Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal 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), edited by for Computing Machinery Association, 1–15. Dornbirn, Austria: Association for Computing Machinery, 2021. https://doi.org/10.1145/3450218.3477308.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46331
Seiler, Moritz, Heike Trautmann, and Pascal Kerschke. “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries.” In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1–8. Glasgow, UK, 2020. https://doi.org/10.1109/IJCNN48605.2020.9207338.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46330
Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike 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), edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, and Heike Trautmann, 48–64. Leiden, The Netherlands, 2020. https://doi.org/10.1007/978-3-030-58112-1_4.
LibreCat
| DOI
Search
Filter Publications
Display / Sort
Export / Embed
13 Publications
2024 | Conference Paper | LibreCat-ID: 54643
Seiler, Moritz, Urban Skvorc, G Cenikj, C Doerr, and Heike Trautmann. “Learned Features vs. Classical ELA on Affine BBOB Functions.” In Parallel Problem Solving from Nature — PPSN XVIII, edited by M Affenzeller, S Winkler, A Kononova, H Trautmann, T Tušar, P Machado, and T Baeck, 1–14. Cham: Springer International Publishing, 2024.
LibreCat
2024 | Conference Paper | LibreCat-ID: 52749
Seiler, Moritz, Jeroen Rook, Jonathan Heins, Oliver Ludger Preuß, Jakob Bossek, and Heike Trautmann. “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP.” In 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2024. https://doi.org/10.1109/ssci52147.2023.10372008.
LibreCat
| DOI
2024 | Conference Paper | LibreCat-ID: 58335
Seiler, Moritz, Urban Skvorc, Carola Doerr, and Heike 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, edited by Paola Festa, Daniele Ferone, Tommaso Pastore, and Ornella Pisacane, 14990:361–376. Lecture Notes in Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-75623-8_29.
LibreCat
| DOI
2023 | Journal Article | LibreCat-ID: 46310
Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.” Theoretical Computer Science 940 (2023): 123–45. https://doi.org/10.1016/j.tcs.2022.10.019.
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 48898
Seiler, Moritz, Jeroen Rook, Jonathan Heins, Oliver Ludger Preuß, Jakob Bossek, and Heike Trautmann. “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP.” In 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361–68, n.d. https://doi.org/10.1109/SSCI52147.2023.10372008.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46307
Seiler, Moritz, Raphael Patrick Prager, Pascal Kerschke, and Heike 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, 657–665. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3512290.3528834.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46304
Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal 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, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 3–17. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_1.
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 46303
Pohl, Janina Susanne, Dennis Assenmacher, Moritz Seiler, Heike Trautmann, and Christian 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), edited by for the Advancement of Artificial Intelligence (AAAI) Association, 1–10. Palo Alto, CA, USA: AAAI Press, 2022. https://doi.org/10.36190/2022.91.
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46315
Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8, 2021. https://doi.org/10.1109/SSCI50451.2021.9660174.
LibreCat
| DOI
2021 | Conference Paper | LibreCat-ID: 46312
Assenmacher, Dennis, Marco Niemann, Kilian Müller, Moritz Seiler, Dennis M. Riehle, and Heike 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), 1–14. Virtual Event, 2021.
LibreCat
2021 | Conference Paper | LibreCat-ID: 46313
Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal 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), edited by for Computing Machinery Association, 1–15. Dornbirn, Austria: Association for Computing Machinery, 2021. https://doi.org/10.1145/3450218.3477308.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46331
Seiler, Moritz, Heike Trautmann, and Pascal Kerschke. “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries.” In Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1–8. Glasgow, UK, 2020. https://doi.org/10.1109/IJCNN48605.2020.9207338.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 46330
Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike 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), edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, and Heike Trautmann, 48–64. Leiden, The Netherlands, 2020. https://doi.org/10.1007/978-3-030-58112-1_4.
LibreCat
| DOI