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

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.

Keywords

• Meta-heuristic approaches
• seed growth algorithm
• machine learning
• neural networks