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Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems.

Liefooghe, Arnaud; Verel, Sébastien; Lacroix, Benjamin; Zăvoianu, Alexandru-Ciprian; McCall, John

Authors

Arnaud Liefooghe

Sébastien Verel

John McCall



Contributors

Francisco Chicano
Editor

Abstract

In this paper, we demonstrate the application of features from landscape analysis, initially proposed for multi-objective combinatorial optimisation, to a benchmark set of 1 200 randomly-generated multiobjective interpolated continuous optimisation problems (MO-ICOPs). We also explore the benefits of evaluating the considered landscape features on the basis of a fixed-size sampling of the search space. This allows fine control over cost when aiming for an efficient application of feature-based automated performance prediction and algorithm selection. While previous work shows that the parameters used to generate MO-ICOPs are able to discriminate the convergence behaviour of four state-of-the-art multi-objective evolutionary algorithms, our experiments reveal that the proposed (black-box) landscape features used as predictors deliver a similar accuracy when combined with a classification model. In addition, we analyse the relative importance of each feature for performance prediction and algorithm selection.

Citation

LIEFOOGHE, A., VEREL, S., LACROIX, B., ZĂVOIANU, A.-C. and MCCALL, J. 2021. Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems. In Chicano, F. (ed) Proceedings of 2021 Genetic and evolutionary computation conference (GECCO 2021), 10-14 July 2021, [virtual conference]. New York: ACM [online], pages 421-429. Available from: https://doi.org/10.1145/3449639.3459353

Conference Name 2021 Genetic and evolutionary computation conference (GECCO 2021)
Conference Location [virtual conference]
Start Date Jul 10, 2021
End Date Jul 14, 2021
Acceptance Date Mar 26, 2021
Online Publication Date Jun 26, 2021
Publication Date Jun 30, 2021
Deposit Date Apr 29, 2021
Publicly Available Date Jul 27, 2021
Publisher Association for Computing Machinery
Pages 421-429
ISBN 9781450383509
DOI https://doi.org/10.1145/3449639.3459353
Keywords Computing methodologies; Continuous space search; Theory of computation; Evolutionary algorithms; Applied computing; Multi-criterion optimization and decision-making
Public URL https://rgu-repository.worktribe.com/output/1324031

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