Evolutionary dynamic optimization: Methodologies

Trung Thanh NGUYEN*, Shengxiang YANG, Juergen BRANKE, Xin YAO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

11 Citations (Scopus)

Abstract

In recent years, Evolutionary Dynamic Optimization (EDO) has attracted a lot of research effort and has become one of the most active research areas in evolutionary computation (EC) in terms of the number of activities and publications. This chapter provides a summary of main EDO approaches in solving DOPs. The strength and weakness of each approach and their suitability for different types of DOPs are discussed. Current gaps, challenging issues and future directions regarding EDO methodolgies are also presented. © 2013 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationEvolutionary Computation for Dynamic Optimization Problems
EditorsShengxiang YANG, Xin YAO
PublisherSpringer
Chapter2
Pages39-64
Number of pages26
ISBN (Electronic)9783642384165
ISBN (Print)9783642384158, 9783642448430
DOIs
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
PublisherSpringer
Volume490
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

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  • Evolutionary Computation for Dynamic Optimization Problems

    YANG, S. (ed.) & YAO, X. (ed.), 2013, Heidelberg: Springer. 470 p. (Studies in Computational Intelligence; vol. 490)

    Research output: Scholarly Books | Reports | Literary WorksBook (Editor)Researchpeer-review

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