This paper proposes a new method for hyperparameter optimization (HPO) that balances exploration and exploitation. While evolutionary algorithms (EAs) show promise in HPO, they often struggle with effective exploitation. To address this, we integrate a linear surrogate model into a genetic algorithm (GA), allowing for smooth integration of multiple strategies. This combination improves exploitation performance, achieving an average improvement of 1.89 percent (max 6.55 percent, min -3.45 percent) over existing HPO methods.
@article{arxiv.2504.07359,
title = {A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization},
author = {Chul Kim and Inwhee Joe},
journal= {arXiv preprint arXiv:2504.07359},
year = {2025}
}
Comments
Published in IEEE Access, 12 pages, 10 figures. DOI: 10.1109/ACCESS.2024.3508269