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To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features

Vermetten, Diederick; Wang, Hao; Sim, Kevin; Hart, Emma

Authors

Diederick Vermetten

Hao Wang



Contributors

João Correia
Editor

Stephen Smith
Editor

Raneem Qaddoura
Editor

Abstract

Dynamic algorithm selection aims to exploit the complementarity of multiple optimization algorithms by switching between them during the search. While these kinds of dynamic algorithms have been shown to have potential to outperform their component algorithms, it is still unclear how this potential can best be realized. One promising approach is to make use of landscape features to enable a per-run trajectory-based switch. Here, the samples seen by the first algorithm are used to create a set of features which describe the landscape from the perspective of the algorithm. These features are then used to predict what algorithm to switch to.

In this work, we extend this per-run trajectory-based approach to consider a wide variety of potential points at which to perform the switch. We show that using a sliding window to capture the local landscape features contains information which can be used to predict whether a switch at that point would be beneficial to future performance. By analyzing the resulting models, we identify what features are most important to these predictions. Finally, by evaluating the importance of features and comparing these values between multiple algorithms, we show clear differences in the way the second algorithm interacts with the local landscape features found before the switch.

Presentation Conference Type Conference Paper (Published)
Conference Name Evo Applications 2023
Start Date Apr 12, 2023
End Date Apr 14, 2023
Acceptance Date Jan 18, 2023
Online Publication Date Apr 9, 2023
Publication Date Apr 10, 2023
Deposit Date Feb 23, 2023
Publicly Available Date Apr 10, 2024
Publisher Springer
Pages 335-350
Series Title Lecture Notes in Computer Science
Series Number 13989
Book Title Applications of Evolutionary Computation: 26th International Conference, EvoApplications 2023
ISBN 9783031302282
DOI https://doi.org/10.1007/978-3-031-30229-9_22
Keywords Dynamic algorithm selection, benchmarking, exploratory landscape analysis

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To Switch or Not to Switch: Predicting the Benefit of Switching Between Algorithms Based on Trajectory Features (accepted version) (2 Mb)
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