Neural Architecture Search (NAS) aims to automate the discovery of high-performing deep neural network architectures. Traditional objective-based NAS approaches typically optimize a certain performance metric (e.g., prediction accuracy), overlooking large parts of the architecture search space that potentially contain interesting network configurations. Furthermore, objective-driven population-based metaheuristics in complex search spaces often quickly exhaust population diversity and succumb to premature convergence to local optima. This issue becomes more complicated in NAS when performance objectives do not fully align with the actual performance of the candidate architectures, as is often the case with training-free metrics. While training-free metrics have gained popularity for their rapid performance estimation of candidate architectures without incurring computation-heavy network training, their effective incorporation into NAS remains a challenge. This paper presents the Pareto Dominance-based Novelty Search for multi-objective NAS with Multiple Training-Free metrics (MTF-PDNS). Unlike conventional NAS methods that optimize explicit objectives, MTF-PDNS promotes population diversity by utilizing a novelty score calculated based on multiple training-free performance and complexity metrics, thereby yielding a broader exploration of the search space. Experimental results on standard NAS benchmark suites demonstrate that MTF-PDNS outperforms conventional methods driven by explicit objectives in terms of convergence speed, diversity maintenance, architecture transferability, and computational costs.
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search
College of Software, Northeastern University（东北大学软件学院）；Institute of Intelligent Manufacturing, Nanjing Tech University（南京工业大学智能制造研究院）；School of Computer Science, Shaanxi Normal University（陕西师范大学计算机学院）；College of Computer Science, Northeastern University（东北大学计算机科学与工程学院）；Faculty of Technology, Bielefeld University（比勒费尔德大学技术学院）
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search
LAMIH/UMR CNRS；Université Polytechnique Hauts-de-France（上法兰西理工大学）；University of Lille（里尔大学）；Centre de Recherche en Informatique Signal et Automatique de Lille (CRIStAL)（里尔信息、信号与自动化研究中心（CRIStAL））
A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems
Birmingham City University（伯明翰城市大学）；Institute of Systems Engineering, Macau University of Science and Technology（澳门科学理工学院系统工程研究所）；Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology（新泽西理工学院亨利和约翰·C·哈特曼电气与计算机工程系）
