Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results

Maxim Buzdalov, Vladimir Parfenov

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

Abstract

Steady-state evolutionary algorithms are often favoured over generational ones due to better scalability in parallel and distributed environments. However, in certain conditions they are able to produce results of better quality as well. We consider several ways to introduce various ``degrees of steadiness'' in the NSGA-II algorithm, some of which have not been known in literature, and show experimentally (on a corpus of 21 test problems) the presence of a general trend: algorithms with more steadiness yield better results.
Original languageEnglish
Title of host publicationGECCO Companion '15
Subtitle of host publicationProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
EditorsSara Silva
PublisherAssociation for Computing Machinery
Pages749-750
Number of pages2
ISBN (Print)978-1-4503-3488-4
DOIs
Publication statusPublished - 11 Jul 2015
Externally publishedYes
Event16th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015

Conference

Conference16th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/TerritorySpain
CityMadrid
Period11 Jul 201515 Jul 2015

Keywords

  • multi-objective
  • nsga-ii
  • steady-state

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