Proposing an Extension Method for tdgaCNN Based on the Introduction of Skip Connections

Abstract

Machine learning-based image recognition has gained significant attention, mainly using Convolutional Neural Networks (CNNs). As the complexity of problems increases, so does the complexity of CNN architectures. This makes finding the optimal CNN structure a challenging combinatorial optimization problem. Manual settings are time-consuming and labor-intensive. To address this, the field of AutoML has introduced gaCNN, which uses a genetic algorithm for CNN structure search, and tdgaCNN, which applies thermodynamic selection rules. These methods have shown superiority over traditional ones. In this study, we propose a tdgaCNN extension that incorporates skip connections to enhance performance. Its effectiveness is demonstrated on an image benchmark dataset.