Computational study demonstrates improved time-series forecasting accuracy in power systems, indicating supervising agents enhance neural architecture search efficiency.
This paper investigates the merits and limitations of various neural architecture search (NAS) methods and proposes a novel method of engaging two or more NAS methods and improving their individual performances by introducing a supervising agent. Empirical results show that the proposed method achieves a lower root mean square error (RMSE) in time-series forecasting tasks in power systems and a lower time of convergence compared to baseline NAS methods.
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Dolatabadi et al. (2023) studied this question.
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