On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems

Jeroen Rook, Heike Trautmann, Jakob Bossek, Christian Grimme

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
108 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationGECCO 2022 Companion
Subtitle of host publicationProceedings of the 2022 Genetic and Evolutionary Computation Conference
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages356-359
Number of pages4
ISBN (Electronic)978-1-4503-9268-6
DOIs
Publication statusPublished - 9 Jul 2022
EventGenetic and Evolutionary Computation Conference, GECCO 2022 - Boston, United States
Duration: 9 Jul 202213 Jul 2022
https://gecco-2022.sigevo.org/HomePage

Conference

ConferenceGenetic and Evolutionary Computation Conference, GECCO 2022
Abbreviated titleGECCO 2022
Country/TerritoryUnited States
CityBoston
Period9/07/2213/07/22
Internet address

Keywords

  • Configuration
  • Multi-modality
  • Multi-objective optimization
  • 2024 OA procedure

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