Tight bounds on the expected runtime of a standard steady state genetic algorithm

Oliveto, P.S., Sudholt, D. and Witt, C. (2022) Tight bounds on the expected runtime of a standard steady state genetic algorithm. Algorithmica, 84 (6). pp. 1603-1658. ISSN 0178-4617

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Oliveto, P.S.
  • Sudholt, D.
  • Witt, C.
Copyright, Publisher and Additional Information:

© The Author(s) 2021. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords: Evolutionary algorithms; Runtime analysis; Crossover; Lower bounds
Dates:
  • Published: June 2022
  • Published (online): 23 December 2021
  • Accepted: 8 November 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
Funder
Grant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/M004252/1
European Cooperation in Science and Technology
CA15140
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
UNSPECIFIED
Depositing User: Symplectic Sheffield
Date Deposited: 13 Jan 2022 16:27
Last Modified: 31 May 2022 13:46
Status: Published
Publisher: Springer
Refereed: Yes
Identification Number: 10.1007/s00453-021-00893-w
Open Archives Initiative ID (OAI ID):

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