On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems
Rook J, Trautmann H, Bossek J, Grimme C
Zusammenfassung
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.
Schlüsselwörter
multi-objective optimization; configuration; multi-modality
Publikationstyp
Forschungsartikel in Sammelband (Konferenz)
Begutachtet
Ja
Publikationsstatus
Veröffentlicht
Jahr
2022
Konferenz
Genetic and Evolutionary Computation Conference
Konferenzort
Boston
Buchtitel
Proceedings of the Genetic and Evolutionary Computation Conference Companion
Herausgeber
Fieldsend, J; Wagner, M.
Erste Seite
356–359
Letzte Seite
356–359
Reihe
GECCO '22
Verlag
Association for Computing Machinery
Ort
New York, NY, USA
Sprache
Englisch
ISBN
9781450392686
DOI
Gesamter Text