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10 Publications
2023 | Journal Article | LibreCat-ID: 46310
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “A study on the effects of normalized TSP features for automated algorithm selection,” Theoretical Computer Science, vol. 940, pp. 123–145, 2023, doi: https://doi.org/10.1016/j.tcs.2022.10.019.
LibreCat
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2021 | Book Chapter | LibreCat-ID: 48881
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.
LibreCat
2020 | Journal Article | LibreCat-ID: 46334
J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,” Applied Soft Computing, vol. 88, p. 105901, 2020, doi: https://doi.org/10.1016/j.asoc.2019.105901.
LibreCat
| DOI
2020 | Conference Paper | LibreCat-ID: 48897
M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “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), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.
LibreCat
| DOI
2020 | Journal Article | LibreCat-ID: 48848
J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,” Applied Soft Computing, vol. 88, no. C, 2020, doi: 10.1016/j.asoc.2019.105901.
LibreCat
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2019 | Conference Paper | LibreCat-ID: 48875
J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization, 2019, pp. 215–219, doi: 10.1007/978-3-030-05348-2_19.
LibreCat
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2018 | Conference Paper | LibreCat-ID: 48885
P. Kerschke, J. Bossek, and H. Trautmann, “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, 2018, pp. 1737–1744, doi: 10.1145/3205651.3208233.
LibreCat
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2018 | Journal Article | LibreCat-ID: 48884
P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,” Evolutionary Computation, vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.
LibreCat
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2016 | Conference Paper | LibreCat-ID: 48873
J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers,” in Learning and Intelligent Optimization, 2016, pp. 48–59, doi: 10.1007/978-3-319-50349-3_4.
LibreCat
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2012 | Conference Paper | LibreCat-ID: 46396
B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 2012, pp. 313–320, doi: 10.1145/2330163.2330209.
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