Neural networks optimization through genetic algorithm searches: A review

Neural networks and genetic algorithms are the two sophisticated machine learning techniques presently attracting attention from scientists, engineers, and statisticians, among others. They have gained popularity in recent years. This paper presents a state of the art review of the research conduc...

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Main Authors:	Chiroma, Haruna, Mohd Nor, Ahmad Shukri, Abdul Kareem, Sameem, Abubakar, Adamu, Hermawan, Arief, Qin, Hongwu, Hamza, Mukhtar Fatihu, Herawan, Tutut
Format:	Article
Language:	English English
Published:	Natural Sciences Publishing Corporation 2017
Subjects:	QA Mathematics QA76 Computer software
Online Access:	http://irep.iium.edu.my/63043/ http://irep.iium.edu.my/63043/ http://irep.iium.edu.my/63043/ http://irep.iium.edu.my/63043/1/63043_Neural%20networks%20optimization%20through%20genetic_article.pdf http://irep.iium.edu.my/63043/2/63043_Neural%20networks%20optimization%20through%20genetic_scopus.pdf

id	iium-63043
recordtype	eprints
spelling	iium-630432018-05-05T05:47:00Z http://irep.iium.edu.my/63043/ Neural networks optimization through genetic algorithm searches: A review Chiroma, Haruna Mohd Nor, Ahmad Shukri Abdul Kareem, Sameem Abubakar, Adamu Hermawan, Arief Qin, Hongwu Hamza, Mukhtar Fatihu Herawan, Tutut QA Mathematics QA76 Computer software Neural networks and genetic algorithms are the two sophisticated machine learning techniques presently attracting attention from scientists, engineers, and statisticians, among others. They have gained popularity in recent years. This paper presents a state of the art review of the research conducted on the optimization of neural networks through genetic algorithm searches. Optimization is aimed toward deviating from the limitations attributed to neural networks in order to solve complex and challenging problems. We provide an analysis and synthesis of the research published in this area according to the application domain, neural network design issues using genetic algorithms, types of neural networks and optimal values of genetic algorithm operators (population size, crossover rate and mutation rate). This study may provide a proper guide for novice as well as expert researchers in the design of evolutionary neural networks helping them choose suitable values of genetic algorithm operators for applications in a specific problem domain. Further research direction, which has not received much attention from scholars, is unveiled. Natural Sciences Publishing Corporation 2017 Article PeerReviewed application/pdf en http://irep.iium.edu.my/63043/1/63043_Neural%20networks%20optimization%20through%20genetic_article.pdf application/pdf en http://irep.iium.edu.my/63043/2/63043_Neural%20networks%20optimization%20through%20genetic_scopus.pdf Chiroma, Haruna and Mohd Nor, Ahmad Shukri and Abdul Kareem, Sameem and Abubakar, Adamu and Hermawan, Arief and Qin, Hongwu and Hamza, Mukhtar Fatihu and Herawan, Tutut (2017) Neural networks optimization through genetic algorithm searches: A review. Applied Mathematics and Information Sciences, 11 (6). pp. 1543-1564. ISSN 1935-0090 E-ISSN 2325-0399 http://www.naturalspublishing.com/files/published/2n52l3nl6737r3.pdf 10.18576/amis/110602
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA Mathematics QA76 Computer software
spellingShingle	QA Mathematics QA76 Computer software Chiroma, Haruna Mohd Nor, Ahmad Shukri Abdul Kareem, Sameem Abubakar, Adamu Hermawan, Arief Qin, Hongwu Hamza, Mukhtar Fatihu Herawan, Tutut Neural networks optimization through genetic algorithm searches: A review
description	Neural networks and genetic algorithms are the two sophisticated machine learning techniques presently attracting attention from scientists, engineers, and statisticians, among others. They have gained popularity in recent years. This paper presents a state of the art review of the research conducted on the optimization of neural networks through genetic algorithm searches. Optimization is aimed toward deviating from the limitations attributed to neural networks in order to solve complex and challenging problems. We provide an analysis and synthesis of the research published in this area according to the application domain, neural network design issues using genetic algorithms, types of neural networks and optimal values of genetic algorithm operators (population size, crossover rate and mutation rate). This study may provide a proper guide for novice as well as expert researchers in the design of evolutionary neural networks helping them choose suitable values of genetic algorithm operators for applications in a specific problem domain. Further research direction, which has not received much attention from scholars, is unveiled.
format	Article
author	Chiroma, Haruna Mohd Nor, Ahmad Shukri Abdul Kareem, Sameem Abubakar, Adamu Hermawan, Arief Qin, Hongwu Hamza, Mukhtar Fatihu Herawan, Tutut
author_facet	Chiroma, Haruna Mohd Nor, Ahmad Shukri Abdul Kareem, Sameem Abubakar, Adamu Hermawan, Arief Qin, Hongwu Hamza, Mukhtar Fatihu Herawan, Tutut
author_sort	Chiroma, Haruna
title	Neural networks optimization through genetic algorithm searches: A review
title_short	Neural networks optimization through genetic algorithm searches: A review
title_full	Neural networks optimization through genetic algorithm searches: A review
title_fullStr	Neural networks optimization through genetic algorithm searches: A review
title_full_unstemmed	Neural networks optimization through genetic algorithm searches: A review
title_sort	neural networks optimization through genetic algorithm searches: a review
publisher	Natural Sciences Publishing Corporation
publishDate	2017
url	http://irep.iium.edu.my/63043/ http://irep.iium.edu.my/63043/ http://irep.iium.edu.my/63043/ http://irep.iium.edu.my/63043/1/63043_Neural%20networks%20optimization%20through%20genetic_article.pdf http://irep.iium.edu.my/63043/2/63043_Neural%20networks%20optimization%20through%20genetic_scopus.pdf
first_indexed	2023-09-18T21:29:23Z
last_indexed	2023-09-18T21:29:23Z
_version_	1777412408434753536

Neural networks optimization through genetic algorithm searches: A review

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