  In this paper, we propose a surrogate-assisted evolutionary algorithm (EA)
for hyperparameter optimization of machine learning (ML) models. The proposed
STEADE model initially estimates the objective function landscape using
RadialBasis Function interpolation, and then transfers the knowledge to an EA
technique called Differential Evolution that is used to evolve new solutions
guided by a Bayesian optimization framework. We empirically evaluate our model
on the hyperparameter optimization problems as a part of the black box
optimization challenge at NeurIPS 2020 and demonstrate the improvement brought
about by STEADE over the vanilla EA.
