- E-Mail:
- moritz.seiler@uni-paderborn.de
- Telefon:
- +49 5251 60-6311
- Büroanschrift:
-
Fürstenallee 11
33102 Paderborn - Raum:
- FU.227
Publikationen
Aktuelle Publikationen
Learned Features vs. Classical ELA on Affine BBOB Functions
M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, H. Trautmann, in: M. Affenzeller, S. Winkler, A. Kononova, H. Trautmann, T. Tušar, P. Machado, T. Baeck (Eds.), Parallel Problem Solving from Nature — PPSN XVIII, Springer International Publishing, Cham, 2024, pp. 1–14.
Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
M. Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2024.
Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection
M. Seiler, U. Skvorc, C. Doerr, H. Trautmann, 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, Springer, 2024, pp. 361–376.
A study on the effects of normalized TSP features for automated algorithm selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145.
Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP
M. Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), n.d., pp. 361–368.
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