conference paper Jakob Bossek author 1029790000-0002-4121-4668 Heike Trautmann author RobertoBattiti editor MauroBrunato editor IliasKotsireas editor Panos M.Pardalos editor 819 department A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques. Springer International Publishing2019 eng Algorithm selectionPerformance measurement 978-3-030-05348-210.1007/978-3-030-05348-2_19 215–219 yes Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:215–219. doi:<a href="https://doi.org/10.1007/978-3-030-05348-2_19">10.1007/978-3-030-05348-2_19</a> Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 215–219. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href="https://doi.org/10.1007/978-3-030-05348-2_19">https://doi.org/10.1007/978-3-030-05348-2_19</a>. J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in <i>Learning and Intelligent Optimization</i>, 2019, pp. 215–219, doi: <a href="https://doi.org/10.1007/978-3-030-05348-2_19">10.1007/978-3-030-05348-2_19</a>. @inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}, DOI={<a href="https://doi.org/10.1007/978-3-030-05348-2_19">10.1007/978-3-030-05348-2_19</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019}, pages={215–219}, collection={Lecture Notes in Computer Science} } J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 215–219. Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 215–219, doi:<a href="https://doi.org/10.1007/978-3-030-05348-2_19">10.1007/978-3-030-05348-2_19</a>. Bossek, J., &#38; Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 215–219). Springer International Publishing. <a href="https://doi.org/10.1007/978-3-030-05348-2_19">https://doi.org/10.1007/978-3-030-05348-2_19</a> 488752023-11-14T15:58:57Z2023-12-13T10:47:32Z