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Workability review of genetic algorithm approach in networks

In this paper, we surveyed the implementations of genetic algorithm within networks, whether it is computer network, transportation network, and other fields that have networking context. Their feasibilities are discussed along with our suggestions for potential improvements. Genetic algorithm can a...

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Main Authors: Nurika, O., Zakaria, N., Hassan, F., Jung, L.T.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2014
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938808512&doi=10.1109%2fICCOINS.2014.6868385&partnerID=40&md5=e41d78c645cbd9d061308c8612c6c921
http://eprints.utp.edu.my/31180/
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spelling my.utp.eprints.311802022-03-25T09:02:12Z Workability review of genetic algorithm approach in networks Nurika, O. Zakaria, N. Hassan, F. Jung, L.T. In this paper, we surveyed the implementations of genetic algorithm within networks, whether it is computer network, transportation network, and other fields that have networking context. Their feasibilities are discussed along with our suggestions for potential improvements. Genetic algorithm can also be an alternative to other optimization methods/algorithms. In some cases, it even outperforms other methods. However, the choice of genetic algorithm might be influenced by some concerns, such as execution time and problem size. Generally, genetic algorithm process will accomplish according to its parameters sizes. Finally, the success stories prove the applicability, adaptability, and scalability of genetic algorithm, specifically for almost-any network optimization. © 2014 IEEE. Institute of Electrical and Electronics Engineers Inc. 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938808512&doi=10.1109%2fICCOINS.2014.6868385&partnerID=40&md5=e41d78c645cbd9d061308c8612c6c921 Nurika, O. and Zakaria, N. and Hassan, F. and Jung, L.T. (2014) Workability review of genetic algorithm approach in networks. In: UNSPECIFIED. http://eprints.utp.edu.my/31180/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description In this paper, we surveyed the implementations of genetic algorithm within networks, whether it is computer network, transportation network, and other fields that have networking context. Their feasibilities are discussed along with our suggestions for potential improvements. Genetic algorithm can also be an alternative to other optimization methods/algorithms. In some cases, it even outperforms other methods. However, the choice of genetic algorithm might be influenced by some concerns, such as execution time and problem size. Generally, genetic algorithm process will accomplish according to its parameters sizes. Finally, the success stories prove the applicability, adaptability, and scalability of genetic algorithm, specifically for almost-any network optimization. © 2014 IEEE.
format Conference or Workshop Item
author Nurika, O.
Zakaria, N.
Hassan, F.
Jung, L.T.
spellingShingle Nurika, O.
Zakaria, N.
Hassan, F.
Jung, L.T.
Workability review of genetic algorithm approach in networks
author_facet Nurika, O.
Zakaria, N.
Hassan, F.
Jung, L.T.
author_sort Nurika, O.
title Workability review of genetic algorithm approach in networks
title_short Workability review of genetic algorithm approach in networks
title_full Workability review of genetic algorithm approach in networks
title_fullStr Workability review of genetic algorithm approach in networks
title_full_unstemmed Workability review of genetic algorithm approach in networks
title_sort workability review of genetic algorithm approach in networks
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938808512&doi=10.1109%2fICCOINS.2014.6868385&partnerID=40&md5=e41d78c645cbd9d061308c8612c6c921
http://eprints.utp.edu.my/31180/
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score 13.211869

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