Genetic algorithms in wireless networking: techniques, applications, and issues

Mehboob, U; Qadir, J; Ali, S; Vasilakos, A

Genetic algorithms in wireless networking: techniques, applications, and issues

Mehboob, U Qadir, J Ali, S Vasilakos, A

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Soft Computing, 2016, 20, (6), pp. 2467-2501
Issue Date:: 2016-06-01

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Field	Value	Language
dc.contributor.author	Mehboob, U
dc.contributor.author	Qadir, J
dc.contributor.author	Ali, S
dc.contributor.author	Vasilakos, A https://orcid.org/0000-0003-1902-9877
dc.date.accessioned	2022-08-08T10:04:13Z
dc.date.available	2022-08-08T10:04:13Z
dc.date.issued	2016-06-01
dc.identifier.citation	Soft Computing, 2016, 20, (6), pp. 2467-2501
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/159771
dc.description.abstract	In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization. GAs are well known for their remarkable generality and versatility and have been applied in a wide variety of settings in wireless networks. In this paper, we provide a comprehensive survey of the applications of GAs in wireless networks. We provide both an exposition of common GA models and configuration and provide a broad-ranging survey of GA techniques in wireless networks. We also point out open research issues and define potential future work. While various surveys on GAs exist in the literature, our paper is the first paper, to the best of our knowledge, which focuses on their application in wireless networks.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-016-2070-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Genetic algorithms in wireless networking: techniques, applications, and issues
dc.type	Journal Article
utslib.citation.volume	20
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-08T10:04:11Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	6

Abstract:

In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization. GAs are well known for their remarkable generality and versatility and have been applied in a wide variety of settings in wireless networks. In this paper, we provide a comprehensive survey of the applications of GAs in wireless networks. We provide both an exposition of common GA models and configuration and provide a broad-ranging survey of GA techniques in wireless networks. We also point out open research issues and define potential future work. While various surveys on GAs exist in the literature, our paper is the first paper, to the best of our knowledge, which focuses on their application in wireless networks.

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http://hdl.handle.net/10453/159771