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


Zusammenfassung

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.

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



Publikationstyp
Forschungsartikel in Sammelband (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2022

Konferenz
Genetic and Evolutionary Computation Conference '22

Konferenzort
Boston, Massachusetts

Buchtitel
Proceedings of the Genetic and Evolutionary Computation Conference

Herausgeber
-

Erste Seite
657

Letzte Seite
665

Verlag
ACM Press

Ort
New York, NY, USA

Sprache
Englisch

ISBN
9781450392372

DOI