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



Publication type
Forschungsartikel in Sammelband (Konferenz)

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