Neural networks optimization through genetic algorithm searches: A review

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

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Abstract

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.

Item Type:	Article (Review)
Additional Information:	7132/63043
Uncontrolled Keywords:	Genetic Algorithm; Neural networks; Topology optimization; Weights optimization; Review
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Dr Adamu Abubakar
Date Deposited:	25 Mar 2018 13:21
Last Modified:	05 May 2018 13:47
URI:	http://irep.iium.edu.my/id/eprint/63043

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