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

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., Z?VOIANU, 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: ACM 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