On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems
Rook J, Trautmann H, Bossek J, Grimme C
Abstract
Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.
Keywords
multi-objective optimization; configuration; multi-modality
Publication type
Research article in proceedings (conference)
Peer reviewed
Yes
Publication status
Published
Year
2022
Conference
Genetic and Evolutionary Computation Conference
Venue
Boston
Book title
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Editor
Fieldsend, J; Wagner, M.
Start page
356–359
End page
356–359
Title of series
GECCO '22
Publisher
Association for Computing Machinery
Place
New York, NY, USA
Language
English
ISBN
9781450392686
DOI
Full text