A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Saini, Bhupinder Singh; Lopez-Ibanez, Manuel; Miettinen, Kaisa

Emojulkaisu: GECCO '19 : Proceedings of the Genetic and Evolutionary Computation Conference : Companion Volume

Konferenssin paikka ja aika: Prague, Czech Republic, 13.-17.7.2019

ISBN: 978-1-4503-6748-6

Julkaisuvuosi: 2019

Artikkelin sivunumerot: 1765-1772

Kirjan kokonaissivumäärä: 2075

Kustantaja: ACM

Kustannuspaikka: New York

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1145/3319619.3326890

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/65389


Tiivistelmä

A typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the literature, there is no standard procedure that will select the best technique for a given problem.

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.


YSO-asiasanat: optimointi; monitavoiteoptimointi; algoritmit

Vapaat asiasanat: surrogate modelling; automatic algorithm selection; exploratory landscape analysis


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointi: Kyllä

Raportointivuosi: 2019

JUFO-taso: 1


Viimeisin päivitys 2024-08-01 klo 16:55