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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



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. To be presented at the 2021 Genetic and evolutionary computation conference (GECCO 2021), 10-14 July 2021, [virtual conference]. New York: ACM [online], (accepted). To be made 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
Deposit Date Apr 29, 2021
Publisher Association for Computing Machinery
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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