A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes

Seiler, Moritz Vinzent; Prager, Raphael Patrick; Kerschke, Pascal; Trautmann, Heike


Abstract

Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.

Keywords
Deep Learning; Fitness Landscape; Exploratory Landscape Analysis; Continuous Black-Box Optimization



Publication type
Forschungsartikel in Sammelband (Konferenz)

Peer reviewed
Yes

Publication status
Published

Year
2022

Conference
Genetic and Evolutionary Computation Conference '22

Venue
Boston, Massachusetts

Book title
Proceedings of the Genetic and Evolutionary Computation Conference

Editor
-

Start page
657

End page
665

Publisher
Association for Computing Machinery

Place
New York, NY, USA

Language
English

ISBN
9781450392372

DOI