Moritz Seiler

moritz.seiler@uni-paderborn.de
ID
105520

13 Publications

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[13]
2024 | Conference Paper | LibreCat-ID: 54643
Seiler, M., Skvorc, U., Cenikj, G., Doerr, C., & Trautmann, H. (2024). Learned Features vs. Classical ELA on Affine BBOB Functions. In M. Affenzeller, S. Winkler, A. Kononova, H. Trautmann, T. Tušar, P. Machado, & T. Baeck (Eds.), Parallel Problem Solving from Nature — PPSN XVIII (pp. 1–14). Springer International Publishing.
LibreCat
 
[12]
2024 | Conference Paper | LibreCat-ID: 52749
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 | DOI
 
[11]
2024 | Conference Paper | LibreCat-ID: 58335
Seiler, M., Skvorc, U., Doerr, C., & Trautmann, H. (2024). Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection. In P. Festa, D. Ferone, T. Pastore, & O. Pisacane (Eds.), Learning and Intelligent Optimization - 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers (Vol. 14990, pp. 361–376). Springer. https://doi.org/10.1007/978-3-031-75623-8_29
LibreCat | DOI
 
[10]
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 | DOI
 
[9]
2023 | Conference Paper | LibreCat-ID: 48898
Seiler, M., 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 | DOI
 
[8]
2022 | Conference Paper | LibreCat-ID: 46307
Seiler, M., 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
LibreCat | DOI
 
[7]
2022 | Conference Paper | LibreCat-ID: 46304
Prager, R. P., Seiler, M., 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
LibreCat | DOI
 
[6]
2022 | Conference Paper | LibreCat-ID: 46303
Pohl, J. S., Assenmacher, D., Seiler, M., 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
LibreCat | DOI
 
[5]
2021 | Conference Paper | LibreCat-ID: 46315
Prager, R. P., Seiler, M., Trautmann, H., & Kerschke, P. (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI50451.2021.9660174
LibreCat | DOI
 
[4]
2021 | Conference Paper | LibreCat-ID: 46312
Assenmacher, D., Niemann, M., Müller, K., Seiler, M., Riehle, D. M., & Trautmann, H. (2021). RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), 1–14.
LibreCat
 
[3]
2021 | Conference Paper | LibreCat-ID: 46313
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/3450218.3477308
LibreCat | DOI
 
[2]
2020 | Conference Paper | LibreCat-ID: 46331
Seiler, M., Trautmann, H., & Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207338
LibreCat | DOI
 
[1]
2020 | Conference Paper | LibreCat-ID: 46330
Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI) (pp. 48–64). https://doi.org/10.1007/978-3-030-58112-1_4
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13 Publications

Mark all

[13]
2024 | Conference Paper | LibreCat-ID: 54643
Seiler, M., Skvorc, U., Cenikj, G., Doerr, C., & Trautmann, H. (2024). Learned Features vs. Classical ELA on Affine BBOB Functions. In M. Affenzeller, S. Winkler, A. Kononova, H. Trautmann, T. Tušar, P. Machado, & T. Baeck (Eds.), Parallel Problem Solving from Nature — PPSN XVIII (pp. 1–14). Springer International Publishing.
LibreCat
 
[12]
2024 | Conference Paper | LibreCat-ID: 52749
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 | DOI
 
[11]
2024 | Conference Paper | LibreCat-ID: 58335
Seiler, M., Skvorc, U., Doerr, C., & Trautmann, H. (2024). Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection. In P. Festa, D. Ferone, T. Pastore, & O. Pisacane (Eds.), Learning and Intelligent Optimization - 18th International Conference, LION 18, Ischia Island, Italy, June 9-13, 2024, Revised Selected Papers (Vol. 14990, pp. 361–376). Springer. https://doi.org/10.1007/978-3-031-75623-8_29
LibreCat | DOI
 
[10]
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 | DOI
 
[9]
2023 | Conference Paper | LibreCat-ID: 48898
Seiler, M., 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 | DOI
 
[8]
2022 | Conference Paper | LibreCat-ID: 46307
Seiler, M., 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
LibreCat | DOI
 
[7]
2022 | Conference Paper | LibreCat-ID: 46304
Prager, R. P., Seiler, M., 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
LibreCat | DOI
 
[6]
2022 | Conference Paper | LibreCat-ID: 46303
Pohl, J. S., Assenmacher, D., Seiler, M., 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
LibreCat | DOI
 
[5]
2021 | Conference Paper | LibreCat-ID: 46315
Prager, R. P., Seiler, M., Trautmann, H., & Kerschke, P. (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI50451.2021.9660174
LibreCat | DOI
 
[4]
2021 | Conference Paper | LibreCat-ID: 46312
Assenmacher, D., Niemann, M., Müller, K., Seiler, M., Riehle, D. M., & Trautmann, H. (2021). RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), 1–14.
LibreCat
 
[3]
2021 | Conference Paper | LibreCat-ID: 46313
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/3450218.3477308
LibreCat | DOI
 
[2]
2020 | Conference Paper | LibreCat-ID: 46331
Seiler, M., Trautmann, H., & Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207338
LibreCat | DOI
 
[1]
2020 | Conference Paper | LibreCat-ID: 46330
Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI) (pp. 48–64). https://doi.org/10.1007/978-3-030-58112-1_4
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