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Eager Random Search for Differential Evolution in Continuous Optimization
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3425-3837
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
2015 (English)In: PROGRESS IN ARTIFICIAL INTELLIGENCE, 2015, 286-291 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a memetic computing algorithm by incorporating Eager Random Search (ERS) into differential evolution (DE) to enhance its search ability. ERS is a local search method that is eager to move to a position that is identified as better than the current one without considering other opportunities. Forsaking optimality of moves in ERS is advantageous to increase the randomness and diversity of search for avoiding premature convergence. Three concrete local search strategies within ERS are introduced and discussed, leading to variants of the proposed memetic DE algorithm. The results of evaluations on a set of benchmark problems have demonstrated that the integration of DE with Eager Random Search can improve the performance of pure DE algorithms while not incurring extra computing expenses.

Place, publisher, year, edition, pages
2015. 286-291 p.
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 9273
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:mdh:diva-29587DOI: 10.1007/978-3-319-23485-4_29ISI: 000363570000029Scopus ID: 2-s2.0-84945973642ISBN: 978-3-319-23485-4; 978-3-319-23484-7 (print)OAI: oai:DiVA.org:mdh-29587DiVA: diva2:872607
Conference
17th Portuguese Conference on Artificial Intelligence (EPIA), SEP 08-11, 2015, Univ Coimbra, Coimbra, PORTUGAL
Available from: 2015-11-19 Created: 2015-11-19 Last updated: 2016-10-26Bibliographically approved

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Xiong, Ning

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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