Evolutionary dynamic optimization: Methodologies

Trung Thanh NGUYEN; Shengxiang YANG; Juergen BRANKE; Xin YAO

doi:10.1007/978-3-642-38416-5_2

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 Proceedings › Book Chapter › Research › peer-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 language	English
Title of host publication	Evolutionary Computation for Dynamic Optimization Problems
Editors	Shengxiang YANG, Xin YAO
Publisher	Springer
Chapter	2
Pages	39-64
Number of pages	26
ISBN (Electronic)	9783642384165
ISBN (Print)	9783642384158, 9783642448430
DOIs	https://doi.org/10.1007/978-3-642-38416-5_2
Publication status	Published - 2013
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer
Volume	490
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

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10.1007/978-3-642-38416-5_2

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title = "Evolutionary dynamic optimization: Methodologies",

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. {\textcopyright} 2013 Springer-Verlag Berlin Heidelberg.",

author = "NGUYEN, {Trung Thanh} and Shengxiang YANG and Juergen BRANKE and Xin YAO",

year = "2013",

doi = "10.1007/978-3-642-38416-5_2",

language = "English",

isbn = "9783642384158",

series = "Studies in Computational Intelligence",

publisher = "Springer",

pages = "39--64",

editor = "YANG, {Shengxiang } and YAO, {Xin }",

booktitle = "Evolutionary Computation for Dynamic Optimization Problems",

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Evolutionary dynamic optimization: Methodologies. / NGUYEN, Trung Thanh; YANG, Shengxiang; BRANKE, Juergen et al.
Evolutionary Computation for Dynamic Optimization Problems. ed. / Shengxiang YANG; Xin YAO. Springer, 2013. p. 39-64 (Studies in Computational Intelligence; Vol. 490).

Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review

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T1 - Evolutionary dynamic optimization: Methodologies

AU - NGUYEN, Trung Thanh

AU - YANG, Shengxiang

AU - BRANKE, Juergen

AU - YAO, Xin

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

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DO - 10.1007/978-3-642-38416-5_2

M3 - Book Chapter

SN - 9783642384158

SN - 9783642448430

T3 - Studies in Computational Intelligence

SP - 39

EP - 64

BT - Evolutionary Computation for Dynamic Optimization Problems

A2 - YANG, Shengxiang

A2 - YAO, Xin

PB - Springer

ER -

Evolutionary dynamic optimization: Methodologies

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