A Review on Automation Artificial Neural Networks based on Evolutionary Algorithms

The biological human brain model was used to inspire the idea of Artificial Neural Networks (ANNs). The notion is then converted into a mathematical formulation and then into machine learning, which is utilized to address various issues throughout the world. Moreover, ANNs has achieved advances in solving numerous intractable problems in several fields in recent times. However, its success depends on the hyper-parameters it selects, and manually fine-tuning them is a time-consuming task. Therefore, automation of the design or topology of artificial neural networks has become a hot issue in both academic and industrial studies. Among the numerous optimization approaches, evolutionary algorithms (EAs) are commonly used to optimize the architecture and parameters of ANNs. We review several successful, well-designed strategies to using EAs to develop artificial neural network architecture that has been published in the last four years in this paper. In addition, we conducted a thorough study and analysis of each publication. Furthermore, details such as methods used, datasets, computer resources, training duration, and performance are summarized for each study. Despite this, the automated neural network techniques performed admirably. However, the long training period and huge computer resources remain issues for these sorts of ANNs techniques.