Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time
J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P. Pardalos (Eds.), Learning and Intelligent Optimization, Springer, Cham, 2019, pp. 215–219.
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Conference Paper
| English
Author
Bossek, J;
Trautmann, HeikeLibreCat 
Editor
Battiti, R;
Brunato, M;
Kotsireas, I;
Pardalos, P
Abstract
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.
Publishing Year
Proceedings Title
Learning and Intelligent Optimization
forms.conference.field.series_title_volume.label
Lecture Notes in Computer Science
Volume
11353
Page
215–219
ISBN
LibreCat-ID
Cite this
Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos P, eds. Learning and Intelligent Optimization. Vol 11353. Lecture Notes in Computer Science. Springer; 2019:215–219.
Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. Pardalos (Eds.), Learning and Intelligent Optimization (Vol. 11353, pp. 215–219). Springer.
@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}, volume={11353}, booktitle={Learning and Intelligent Optimization}, publisher={Springer}, author={Bossek, J and Trautmann, Heike}, editor={Battiti, R and Brunato, M and Kotsireas, I and Pardalos, P}, year={2019}, pages={215–219}, collection={Lecture Notes in Computer Science} }
Bossek, J, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In Learning and Intelligent Optimization, edited by R Battiti, M Brunato, I Kotsireas, and P Pardalos, 11353:215–219. Lecture Notes in Computer Science. Cham: Springer, 2019.
J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization, 2019, vol. 11353, pp. 215–219.
Bossek, J., and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” Learning and Intelligent Optimization, edited by R Battiti et al., vol. 11353, Springer, 2019, pp. 215–219.
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