Skip to main navigation Skip to search Skip to main content

A tight runtime analysis for the (+) EA

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

Abstract

Despite significant progress in the theory of evolutionary algorithms, the theoretical understanding of true population-based evolutionary algorithms remains challenging and only few rigorous results exist. Already for the most basic problem, the determination of the asymptotic runtime of the (+) evolutionary algorithm on the simple OneMax benchmark function, only the special cases = 1 and = 1 have been solved. In this work, we analyze this long-standing problem and show the asymptotically tight result that the runtime T, the number of iterations until the optimum is found, satisfies n n log+ log+ /, E[T ] = Θn logn + + / log+ / where log+ x := max{1, log x } for all x > 0.

Original languageEnglish
Title of host publicationGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages1459-1466
Number of pages8
ISBN (Electronic)9781450356183
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018

Publication series

NameGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference

Conference

Conference2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Country/TerritoryJapan
CityKyoto
Period15/07/1819/07/18

Keywords

  • Populations
  • Runtime Analysis
  • Theory

Fingerprint

Dive into the research topics of 'A tight runtime analysis for the (+) EA'. Together they form a unique fingerprint.

Cite this