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The (1 + (?, ?)) global SEMO algorithm

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Abstract

The (1 + (?, ?)) genetic algorithm is a recently proposed single-objective evolutionary algorithm with several interesting properties. We show that its main working principle, mutation with a high rate and crossover as repair mechanism, can be transported also to multi-objective evolutionary computation. We define the (1 + (?, ?)) global SEMO algorithm, a variant of the classic global SEMO algorithm, and prove that it optimizes the OneMinMax benchmark asymptotically faster than the global SEMO. Following the single-objective example, we design a one-fifth rule inspired dynamic parameter setting (to the best of our knowledge for the first time in discrete multi-objective optimization) and prove that it further improves the runtime to O(n2), whereas the best runtime guarantee for the global SEMO is only O(n2 log n).

Original languageEnglish
Title of host publicationGECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages520-528
Number of pages9
ISBN (Electronic)9781450392372
DOIs
Publication statusPublished - 8 Jul 2022
Event2022 Genetic and Evolutionary Computation Conference, GECCO 2022 - Virtual, Online, United States
Duration: 9 Jul 202213 Jul 2022

Publication series

NameGECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference

Conference

Conference2022 Genetic and Evolutionary Computation Conference, GECCO 2022
Country/TerritoryUnited States
CityVirtual, Online
Period9/07/2213/07/22

Keywords

  • Runtime analysis
  • dynamic parameter setting
  • multi-objective optimization
  • mutation operator
  • one-fifth success rule

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