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10 Publications
2023 | Journal Article | LibreCat-ID: 46310
Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science. 2023;940:123-145. doi:https://doi.org/10.1016/j.tcs.2022.10.019
LibreCat
| DOI
2021 | Book Chapter | LibreCat-ID: 48881
Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association for Computing Machinery; 2021:1–15.
LibreCat
2020 | Journal Article | LibreCat-ID: 46334
Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Applied Soft Computing. 2020;88:105901. doi:https://doi.org/10.1016/j.asoc.2019.105901
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 48897
Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: Parallel Problem Solving from {Nature} (PPSN XVI). Springer-Verlag; 2020:48–64. doi:10.1007/978-3-030-58112-1_4
LibreCat
| DOI
2020 | Journal Article | LibreCat-ID: 48848
Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing. 2020;88(C). doi:10.1016/j.asoc.2019.105901
LibreCat
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2019 | Conference Paper | LibreCat-ID: 48875
Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. Learning and Intelligent Optimization. Lecture Notes in Computer Science. Springer International Publishing; 2019:215–219. doi:10.1007/978-3-030-05348-2_19
LibreCat
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2018 | Conference Paper | LibreCat-ID: 48885
Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO’18. Association for Computing Machinery; 2018:1737–1744. doi:10.1145/3205651.3208233
LibreCat
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2018 | Journal Article | LibreCat-ID: 48884
Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation. 2018;26(4):597–620. doi:10.1162/evco_a_00215
LibreCat
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2016 | Conference Paper | LibreCat-ID: 48873
Bossek J, Trautmann H. Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In: Festa P, Sellmann M, Vanschoren J, eds. Learning and Intelligent Optimization. Lecture Notes in Computer Science. Springer International Publishing; 2016:48–59. doi:10.1007/978-3-319-50349-3_4
LibreCat
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2012 | Conference Paper | LibreCat-ID: 46396
Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation. GECCO ’12. Association for Computing Machinery; 2012:313–320. doi:10.1145/2330163.2330209
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