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Lots of effort in neural architecture search (NAS) research has beendedicated to algorithmic development, aiming at designing more efficient andless costly methods. Nonetheless, the investigation of the initialization ofthese techniques remain scare, and currently most NAS methodologies rely onstochastic initialization procedures, because acquiring information prior tosearch is costly. However, the recent availability of NAS benchmarks haveenabled low computational resources prototyping. In this study, we propose toaccelerate a NAS algorithm using a data-driven initialization technique,leveraging the availability of NAS benchmarks. Particularly, we proposed atwo-step methodology. First, a calibrated clustering analysis of the searchspace is performed. Second, the centroids are extracted and used to initializea NAS algorithm. We tested our proposal using Aging Evolution, an evolutionaryalgorithm, on NAS-bench-101. The results show that, compared to a randominitialization, a faster convergence and a better performance of the finalsolution is achieved.

Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS