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

Hosseini, Eghbal and Al-Ghaili, Abbas M. and Hussein Kadir, Dler and Daneshfar, Fatemeh and Shamini Gunasekaran, Saraswathy and Deveci, Muhammet (2024) The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement. IEEE Access, 12. pp. 127440-127459. ISSN 2169-3536

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Meta-heuristic Approaches, Seed Growth Algorithm, Machine Learning, Neural Networks.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Department of Business Administration > Research papers
Depositing User: ePrints Depositor
Date Deposited: 23 Nov 2024 19:41
Last Modified: 23 Nov 2024 19:41
URI: https://eprints.cihanuniversity.edu.iq/id/eprint/2948

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