  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.
