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
Forschungsartikel in Sammelband (Konferenz)

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

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