A4 Article in conference proceedings
Automatic surrogate modelling technique selection based on features of optimization problems (2019)


Saini, Bhupinder Singh; Lopez-Ibanez, Manuel; Miettinen, Kaisa (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. New York: ACM, 1765-1772. DOI: 10.1145/3319619.3326890


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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

Open Access: Publication channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65389


Abstract

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.


Keywords: optimisation; multi-objective optimisation; algorithms

Free keywords: surrogate modelling; automatic algorithm selection; exploratory landscape analysis


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Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1


Last updated on 2020-18-08 at 13:47