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,”https://doi.org/10.1016/j.tcs.2022.10.019__Theoretical Computer Science, vol. 940, pp. 123–145, 2023, doi:https://doi.org/10.1016/j.tcs.2022.10.019 .
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DOI: https://doi.org/10.1016/j.tcs.2022.10.019

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.
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J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,”https://doi.org/10.1016/j.asoc.2019.105901__Applied Soft Computing, vol. 88, p. 105901, 2020, doi:https://doi.org/10.1016/j.asoc.2019.105901 .
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DOI: https://doi.org/10.1016/j.asoc.2019.105901

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,”10.1007/978-3-030-58112-1_4__in Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 48–64, doi:10.1007/978-3-030-58112-1_4 .
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DOI: 10.1007/978-3-030-58112-1_4

J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,”10.1016/j.asoc.2019.105901__Applied Soft Computing, vol. 88, no. C, 2020, doi:10.1016/j.asoc.2019.105901 .
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DOI: 10.1016/j.asoc.2019.105901

J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PA and Expected Running Time,”10.1007/978-3-030-05348-2_19__in Learning and Intelligent Optimization, 2019, pp. 215–219, doi:10.1007/978-3-030-05348-2_19 .
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DOI: 10.1007/978-3-030-05348-2_19

P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,”10.1145/3205651.3208233__in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018, pp. 1737–1744, doi:10.1145/3205651.3208233 .
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DOI: 10.1145/3205651.3208233

P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,”10.1162/evco_a_00215__Evolutionary Computation, vol. 26, no. 4, pp. 597–620, 2018, doi:10.1162/evco_a_00215 .
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DOI: 10.1162/evco_a_00215

J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers,”10.1007/978-3-319-50349-3_4__in Learning and Intelligent Optimization, 2016, pp. 48–59, doi:10.1007/978-3-319-50349-3_4 .
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DOI: 10.1007/978-3-319-50349-3_4

B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,”10.1145/2330163.2330209__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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