A4 Article in conference proceedings
Automatic surrogate modelling technique selection based on features of optimization problems (2019)
Saini, B. S., Lopez-Ibanez, M., & Miettinen, K. (2019). Automatic surrogate modelling technique selection based on features of optimization problems. In GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume (pp. 1765-1772). ACM. https://doi.org/10.1145/3319619.3326890
JYU authors or editors
Publication details
All authors or editors: Saini, Bhupinder Singh; Lopez-Ibanez, Manuel; Miettinen, Kaisa
Parent publication: GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume
Place and date of conference: Prague, Czech Republic, 13.-17.7.2019
ISBN: 978-1-4503-6748-6
Publication year: 2019
Pages range: 1765-1772
Number of pages in the book: 2075
Publisher: ACM
Place of Publication: New York
Publication country: United States
Publication language: English
DOI: https://doi.org/10.1145/3319619.3326890
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65389
Abstract
In this work, we propose the automatic selection of a surrogate modelling technique based on exploratory landscape features of the optimization problem that underlies the given dataset. The overall idea is to learn offline from a large pool of benchmark problems, on which we can evaluate a large number of surrogate modelling techniques. When given a new dataset, features are used to select the most appropriate surrogate modelling technique. The preliminary experiments reported here suggest that the proposed automatic selector is able to identify high-accuracy surrogate models as long as an appropriate classifier is used for selection.
Keywords: optimisation; multi-objective optimisation; algorithms
Free keywords: surrogate modelling; automatic algorithm selection; exploratory landscape analysis
Contributing organizations
Related projects
- Decision Support for Computationally Demanding Optimization Problems
- Miettinen, Kaisa
- Research Council of Finland
Ministry reporting: Yes
VIRTA submission year: 2019
JUFO rating: 1