Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers
P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, Kyoto, Japan, 2018, pp. 1737–1744.
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Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.
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Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion
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1737–1744
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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 (GECCO ’18) Companion. ; 2018:1737–1744. doi:10.1145/3205651.3208233
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 (GECCO ’18) Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233
@inproceedings{Kerschke_Bossek_Trautmann_2018, place={Kyoto, Japan}, title={Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}, DOI={10.1145/3205651.3208233}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion}, author={Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}, year={2018}, pages={1737–1744} }
Kerschke, Pascal, Jakob Bossek, and Heike 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 (GECCO ’18) Companion, 1737–1744. Kyoto, Japan, 2018. https://doi.org/10.1145/3205651.3208233.
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 (GECCO ’18) Companion, 2018, pp. 1737–1744, doi: 10.1145/3205651.3208233.
Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, 2018, pp. 1737–1744, doi:10.1145/3205651.3208233.
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