Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods
Prager, Raphael Patrick; Seiler, Moritz Vinzent; Trautmann, Heike; Kerschke, Pascal
Abstract
In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.
Keywords
Automated Algorithm Selection; Exploratory Landscape Analysis; Deep Learning; Continuous Optimization
Cite as
Prager, R. P., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.),
Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Cham: Springer International Publishing.
Details
Publication type
Research article in proceedings (conference)
Peer reviewed
Yes
Publication status
Published
Year
2022
Conference
International Conference on Parallel Problem Solving from Nature
Venue
Dortmund
Book title
Parallel Problem Solving from Nature -- PPSN XVII
Editor
Rudolph, Günter; Kononova, Anna V.; Aguirre, Hernán; Kerschke, Pascal; Ochoa, Gabriela; Tušar, Tea
Start page
3
End page
17
Publisher
Springer International Publishing
Place
Cham
Language
English
ISBN
978-3-031-14714-2
DOI
Full text
