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Article: Convergence in Probability on a Big Class of Time-Variant Evolutionary Algorithms

TitleConvergence in Probability on a Big Class of Time-Variant Evolutionary Algorithms
Authors
Keywordsasymptotical elitism
convergence
phase transitions
spectral analysis
Time-variant evolutionary algorithms
Issue Date2019
Citation
International Journal of Pattern Recognition and Artificial Intelligence, 2019, v. 33, n. 6, article no. 1959018 How to Cite?
AbstractMotivated by the growing popularity of time-variant evolutionary algorithms (EAs) in solving practical problems, this paper uses spectral analyses to study convergence in probability for a general class of time-variant EAs which can be asymptotically described by reducible Markov chains with multiple aperiodic recurrent classes, covering many existing concrete case studies as specific instantiations. We provide a universal yet easily checkable characteristic for time-variant EAs satisfying global convergence, by introducing the asymptotical elitism and asymptotical monotonicity. To illustrate the effectiveness of our result, we consider four specific EAs with distinct asymptotical behavior, and recover, under even mild conditions, the state-of-the-art result as simple applications of our general theorem. Besides, simulation experiments further verify these results.
Persistent Identifierhttp://hdl.handle.net/10722/329538
ISSN
2023 Impact Factor: 0.9
2023 SCImago Journal Rankings: 0.302
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Ming-
dc.contributor.authorLei, Yunwen-
dc.contributor.authorDing, Lixin-
dc.contributor.authorTong, Zhao-
dc.date.accessioned2023-08-09T03:33:31Z-
dc.date.available2023-08-09T03:33:31Z-
dc.date.issued2019-
dc.identifier.citationInternational Journal of Pattern Recognition and Artificial Intelligence, 2019, v. 33, n. 6, article no. 1959018-
dc.identifier.issn0218-0014-
dc.identifier.urihttp://hdl.handle.net/10722/329538-
dc.description.abstractMotivated by the growing popularity of time-variant evolutionary algorithms (EAs) in solving practical problems, this paper uses spectral analyses to study convergence in probability for a general class of time-variant EAs which can be asymptotically described by reducible Markov chains with multiple aperiodic recurrent classes, covering many existing concrete case studies as specific instantiations. We provide a universal yet easily checkable characteristic for time-variant EAs satisfying global convergence, by introducing the asymptotical elitism and asymptotical monotonicity. To illustrate the effectiveness of our result, we consider four specific EAs with distinct asymptotical behavior, and recover, under even mild conditions, the state-of-the-art result as simple applications of our general theorem. Besides, simulation experiments further verify these results.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Pattern Recognition and Artificial Intelligence-
dc.subjectasymptotical elitism-
dc.subjectconvergence-
dc.subjectphase transitions-
dc.subjectspectral analysis-
dc.subjectTime-variant evolutionary algorithms-
dc.titleConvergence in Probability on a Big Class of Time-Variant Evolutionary Algorithms-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1142/S0218001419590183-
dc.identifier.scopuseid_2-s2.0-85057062944-
dc.identifier.volume33-
dc.identifier.issue6-
dc.identifier.spagearticle no. 1959018-
dc.identifier.epagearticle no. 1959018-
dc.identifier.isiWOS:000465495600011-

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