[{"year":"2018","title":"Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers","author":[{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","full_name":"Bossek, Jakob"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"publication_identifier":{"isbn":["978-1-4503-5764-7"]},"date_updated":"2023-12-13T10:48:38Z","series_title":"GECCO’18","language":[{"iso":"eng"}],"doi":"10.1145/3205651.3208233","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","extern":"1","abstract":[{"lang":"eng","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."}],"date_created":"2023-11-14T15:58:59Z","type":"conference","keyword":["algorithm selection","optimization","performance measures","transportation","travelling salesperson problem"],"department":[{"_id":"819"}],"status":"public","page":"1737–1744","publisher":"Association for Computing Machinery","_id":"48885","user_id":"102979","citation":{"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.","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>.","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>","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>.","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>","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} }","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>."},"place":"New York, NY, USA"}]