Generalized offline orthant search: one code for many problems in multiobjective optimization.

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

We introduce generalized offline orthant search, an algorithmic framework that can be used to solve many problems coming from evolutionary multiobjective optimization using a common well-optimized algorithmic core and relatively cheap reduction procedures. The complexity of the core procedure is O(n · (log n)k-1) for n points of dimension k, and it has a good performance in practice.

We show that the presented approach can perform various flavors of non-dominated sorting, dominance counting, evaluate the ε-indicator and perform initial fitness assignment for IBEA. It is either competitive with the state-of-the-art algorithms or completely outperforms them for higher problem sizes. We hope that this approach will simplify future development of efficient algorithms for evolutionary multiobjective optimization.
Original languageEnglish
Title of host publicationGECCO '18
Subtitle of host publicationProceedings of the Genetic and Evolutionary Computation Conference
EditorsHernan Aguirre, Keiki Takadama
PublisherAssociation for Computing Machinery, Inc
Pages593-600
Number of pages8
ISBN (Print)978-1-4503-5618-3
DOIs
Publication statusPublished - 02 Jul 2018
EventGECCO 2018: The Genetic and Evolutionary Computation Conference - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018
http://gecco-2018.sigevo.org

Conference

ConferenceGECCO 2018: The Genetic and Evolutionary Computation Conference
Country/TerritoryJapan
CityKyoto
Period15 Jul 201819 Jul 2018
Internet address

Keywords

  • IBEA
  • dominance couting
  • epsilon-indicator
  • multiobjective optimization
  • non-dominated sorting
  • performance

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