Designing CNNs with Effective Weights Using Genetic Algorithm for Image Classification

Subject Areas : Computer Engineering and IT

Mojtaba Sajadi ¹ (Department of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, Iran )
محمد باقر توکلی ² (Department of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, Iran )
فربد ستوده ³ (Department of Electrical Engineering, Arak University of Technology, Arak, Iran )
Amir Hossein Salemi ⁴ (Department of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, Iran )

Received: 2023-12-27 Accepted : 2024-05-10 Published : 2024-07-31

Keywords: Convolutional neural network, Genetic algorithm, Effective weights, Image Classification.,

Abstract :

Convolutional neural networks (CNNs) are the most important branch of deep learning (DL) and have experienced rapid development in recent years. A major challenge in using these networks is their large number of parameters, which result in high computational and time costs in real-world applications. In many cases, this increase in costs is due to the design of deeper networks with more parameters for achieving higher accuracy. The present paper employed evolutionary algorithms (EAs) to introduce a method that can identify the best weights and use them to construct more accurate CNNs, hence eliminating the need for deeper networks. At the end of the article, the CNN obtained from the proposed algorithm is compared with the best existing CNNs; which shows that the proposed CNN has increased the classification accuracy, while the number of its parameters is much less, and as a result, it saves computing resources and time.

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