DOI: 10.7763/IJCTE.2026.V18.1384
An Efficient Convolutional Neural Network Architecture Search Using Multi-Layered Population Structure
2. School of Engineering, Graduate School of Informatics, Osaka Metropolitan University, Osaka, Japan
Email: yabuuchi.kazu0105@gmail.com (K.Y.); mnao@omu.ac.jp (N.M.)
*Corresponding author
Manuscript received December 23, 2024; revised April 29, 2025; accepted October 14, 2025; published January 9, 2026
Abstract—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.
Keywords—neural architecture search, convolutional neural network, evolutionary computation, multi-layered population structure
Cite: Kazuki Yabuuchi and Naoki Mori, "An Efficient Convolutional Neural Network Architecture Search Using Multi-Layered Population Structure," International Journal of Computer Theory and Engineering, vol. 18, no. 1, pp. 1-10, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).