Staff View: The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement

The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement

In the past few decades, there have been multiple algorithms proposed for the purpose of solving optimization problems including Machine Learning (ML) applications. Among these algorithms, metaheuristics are an appropriate tool to solve these real problems. Also, ML is one of the advanced tools in A...

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Main Authors:	Hosseini E., Al-Ghaili A.M., Kadir D.H., Daneshfar F., Gunasekaran S.S., Deveci M.
Other Authors:	57212521533
Format:	Article
Published:	Institute of Electrical and Electronics Engineers Inc. 2025
Subjects:	Adversarial machine learning Convergent algorithms Growth algorithms Machine-learning Meta-heuristic approach Metaheuristic Multiple algorithms Neural-networks Optimization problems Seed growth algorithm Seed growths Optimization algorithms
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id	my.uniten.dspace-37009
record_format	dspace
spelling	my.uniten.dspace-370092025-03-03T15:46:35Z The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement Hosseini E. Al-Ghaili A.M. Kadir D.H. Daneshfar F. Gunasekaran S.S. Deveci M. 57212521533 26664381500 57211243421 35078447100 55652730500 55734383000 Adversarial machine learning Convergent algorithms Growth algorithms Machine-learning Meta-heuristic approach Metaheuristic Multiple algorithms Neural-networks Optimization problems Seed growth algorithm Seed growths Optimization algorithms In the past few decades, there have been multiple algorithms proposed for the purpose of solving optimization problems including Machine Learning (ML) applications. Among these algorithms, metaheuristics are an appropriate tool to solve these real problems. Also, ML is one of the advanced tools in Artificial Intelligence (AI) including different learning strategies to teach new tasks according to data. Therefore, proposing an efficient meta-heuristic to improve the inputs of the trainer in ML would be significant. In this study, a new idea centered on seed growth, Seed Growth Algorithm (SGA), as a conditional convergent evolutionary algorithm is proposed for optimizing several discrete and continuous optimization problems. SGA is used in the process of solving optimization test problems by neural networks. The problems are solved by the same neural network with and without SGA, computational results prove the efficiency of SGA in neural networks. Finally, SGA is proposed to solve very extensive test problems including IoT optimization problems. Comparative results of applying the SGA on all of these problems with different sizes are included, and the proposed algorithm suggests effective solutions within a reasonable timeframe. ? 2013 IEEE. Final 2025-03-03T07:46:35Z 2025-03-03T07:46:35Z 2024 Article 10.1109/ACCESS.2024.3452511 2-s2.0-85203538553 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203538553&doi=10.1109%2fACCESS.2024.3452511&partnerID=40&md5=f16431ec98a29f60821e76fade764ec3 https://irepository.uniten.edu.my/handle/123456789/37009 12 127440 127459 All Open Access; Gold Open Access Institute of Electrical and Electronics Engineers Inc. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Adversarial machine learning Convergent algorithms Growth algorithms Machine-learning Meta-heuristic approach Metaheuristic Multiple algorithms Neural-networks Optimization problems Seed growth algorithm Seed growths Optimization algorithms
spellingShingle	Adversarial machine learning Convergent algorithms Growth algorithms Machine-learning Meta-heuristic approach Metaheuristic Multiple algorithms Neural-networks Optimization problems Seed growth algorithm Seed growths Optimization algorithms Hosseini E. Al-Ghaili A.M. Kadir D.H. Daneshfar F. Gunasekaran S.S. Deveci M. The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
description	In the past few decades, there have been multiple algorithms proposed for the purpose of solving optimization problems including Machine Learning (ML) applications. Among these algorithms, metaheuristics are an appropriate tool to solve these real problems. Also, ML is one of the advanced tools in Artificial Intelligence (AI) including different learning strategies to teach new tasks according to data. Therefore, proposing an efficient meta-heuristic to improve the inputs of the trainer in ML would be significant. In this study, a new idea centered on seed growth, Seed Growth Algorithm (SGA), as a conditional convergent evolutionary algorithm is proposed for optimizing several discrete and continuous optimization problems. SGA is used in the process of solving optimization test problems by neural networks. The problems are solved by the same neural network with and without SGA, computational results prove the efficiency of SGA in neural networks. Finally, SGA is proposed to solve very extensive test problems including IoT optimization problems. Comparative results of applying the SGA on all of these problems with different sizes are included, and the proposed algorithm suggests effective solutions within a reasonable timeframe. ? 2013 IEEE.
author2	57212521533
author_facet	57212521533 Hosseini E. Al-Ghaili A.M. Kadir D.H. Daneshfar F. Gunasekaran S.S. Deveci M.
format	Article
author	Hosseini E. Al-Ghaili A.M. Kadir D.H. Daneshfar F. Gunasekaran S.S. Deveci M.
author_sort	Hosseini E.
title	The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
title_short	The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
title_full	The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
title_fullStr	The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
title_full_unstemmed	The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
title_sort	evolutionary convergent algorithm: a guiding path of neural network advancement
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2025
_version_	1825816201447604224
score	13.244109

The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement

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