An Efficient Convolutional Neural Network Architecture Search Using Multi-Layered Population Structure

The design of optimal Convolutional Neural Network (CNN) architectures has become increasingly complex as networks have grown deeper and more diverse. Neural Architecture Search (NAS) has emerged as a powerful technique to automate the discovery of high-performance architectures, thereby reducing reliance on manual expertise. In this work, we propose a novel approach that integrates NAS with a Multi-Layered Population Structure (MLPS), an effective evolutionary scheme originally developed for Genetic Programming (GP). We term this method Multi-Layered Population Structure Neural Architecture Search (MLPS-NAS). Our approach leverages the hierarchical, pyramid-like population management of MLPS to maintain a diverse set of partial solutions, while systematically exploring the vast search space of CNN architectures. A key feature of MLPS-NAS is its ability to construct complex architectures by repeatedly incorporating effective building blocks. To significantly accelerate the search process, we introduce a weight inheritance mechanism between generations, which drastically reduces the computational cost of training new candidate architectures. Experimental results on a real-world image classification task demonstrate that MLPS-NAS achieves accuracy comparable to an established method, CNN Architecture Design Using Cartesian Genetic Programming (CGP-CNN), while substantially reducing the required search time. This synergy between NAS and MLPS offers a promising direction for the efficient, automated design of high-performance CNNs.