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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Neural architecture search applying optimal stopping theory.

Matthew Sheehan¹, Oleg Yakimenko¹

¹Department of Systems Engineering, Naval Postgraduate School, Monterey, CA, United States.

Frontiers in Artificial Intelligence

|October 9, 2025

Summary

Exploring neural architecture search (NAS) is computationally expensive. This study applies the Secretary Problem (SP) to NAS, finding that exploring approximately 37% of the search space is optimal for discovering effective neural architectures efficiently.

Keywords:

Markov decision processes Markov time automated machine learning neural architecture search optimal stopping theory secretary problem

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Area of Science:

Artificial Intelligence
Machine Learning
Computer Science

Background:

Neural Architecture Search (NAS) demands significant computational resources, limiting accessibility for many researchers.
Early-stopping random search methods show competitive performance in NAS.
The impracticality of exploring large NAS search spaces necessitates efficient search strategies.

Purpose of the Study:

To determine the optimal exploration percentage of NAS search spaces for efficient architecture discovery.
To apply optimal stopping theory, specifically the Secretary Problem (SP), to NAS.
To investigate variants of the SP for further reducing NAS search space exploration.

Main Methods:

Applied the Secretary Problem (SP) and its extensions ('good enough', 'call back') to NAS.
Trained and evaluated 672 unique architectures across 20,000 runs on MNIST and CIFAR-10 datasets.
Statistically validated findings on NAS populations ranging from 100 to 3,500 architectures.

Main Results:

Empirically and theoretically confirmed that exploring ~37% of the NAS search space is sufficient for acceptable architecture discovery.
The 'good enough' and 'call back' SP variants reduced exploration to ~15% and ~4%, respectively.
Results were statistically robust across various population sizes and numerous runs.

Conclusions:

Researchers can balance computational costs and NAS effectiveness by exploring approximately 37% of the search space using SP principles.
SP variants offer further reductions in computational requirements for NAS.
Provides practical guidance for implementing efficient NAS strategies.