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10 Publications
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
2021 | Book Chapter | LibreCat-ID: 48881
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 Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 1–15). Association for Computing Machinery.
LibreCat
2020 | Journal Article | LibreCat-ID: 46334
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Applied Soft Computing, 88, 105901. 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. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature} (PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
LibreCat
| DOI
2020 | Journal Article | LibreCat-ID: 48848
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing, 88(C). https://doi.org/10.1016/j.asoc.2019.105901
LibreCat
| DOI
2019 | Conference Paper | LibreCat-ID: 48875
Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 215–219). Springer International Publishing. https://doi.org/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. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233
LibreCat
| DOI
2018 | Journal Article | LibreCat-ID: 48884
Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
LibreCat
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2016 | Conference Paper | LibreCat-ID: 48873
Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp. 48–59). Springer International Publishing. https://doi.org/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. (2012). Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 313–320. https://doi.org/10.1145/2330163.2330209
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