[{"publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:48:38Z","date_created":"2023-11-14T15:58:59Z","author":[{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"},{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"}],"title":"Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers","doi":"10.1145/3205651.3208233","publication_identifier":{"isbn":["978-1-4503-5764-7"]},"year":"2018","place":"New York, NY, USA","citation":{"apa":"Kerschke, P., Bossek, J., &#38; Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1737–1744. <a href=\"https://doi.org/10.1145/3205651.3208233\">https://doi.org/10.1145/3205651.3208233</a>","mla":"Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2018, pp. 1737–1744, doi:<a href=\"https://doi.org/10.1145/3205651.3208233\">10.1145/3205651.3208233</a>.","bibtex":"@inproceedings{Kerschke_Bossek_Trautmann_2018, place={New York, NY, USA}, series={GECCO’18}, title={Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}, DOI={<a href=\"https://doi.org/10.1145/3205651.3208233\">10.1145/3205651.3208233</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}, year={2018}, pages={1737–1744}, collection={GECCO’18} }","short":"P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2018, pp. 1737–1744.","ieee":"P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2018, pp. 1737–1744, doi: <a href=\"https://doi.org/10.1145/3205651.3208233\">10.1145/3205651.3208233</a>.","chicago":"Kerschke, Pascal, Jakob Bossek, and Heike Trautmann. “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1737–1744. GECCO’18. New York, NY, USA: Association for Computing Machinery, 2018. <a href=\"https://doi.org/10.1145/3205651.3208233\">https://doi.org/10.1145/3205651.3208233</a>.","ama":"Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO’18. Association for Computing Machinery; 2018:1737–1744. doi:<a href=\"https://doi.org/10.1145/3205651.3208233\">10.1145/3205651.3208233</a>"},"page":"1737–1744","_id":"48885","user_id":"102979","series_title":"GECCO’18","department":[{"_id":"819"}],"keyword":["algorithm selection","optimization","performance measures","transportation","travelling salesperson problem"],"extern":"1","language":[{"iso":"eng"}],"type":"conference","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","abstract":[{"text":"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.","lang":"eng"}],"status":"public"}]