# Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization

arXiv: https://arxiv.org/abs/2012.06453 · HTML: https://mlsanity.com/paper/2012.06453/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information](https://mlsanity.com/paper/2111.05199/) (2021)
- [ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization](https://mlsanity.com/paper/2301.03317/) (2023)

## Abstract (arXiv)

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.