Dr. Moritz Seiler

Machine Learning and Optimisation

PostDoc

E-Mail:
moritz.seiler@uni-paderborn.de
Phone:
+49 5251 60-6311
Office Address:
Fürstenallee 11
33102 Paderborn
Room:
FU.227

Publications

Latest Publications

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.

DOI

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.

DOI

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.

DOI

A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes

M. Seiler, R.P. Prager, P. Kerschke, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 657–665.

DOI

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