A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization

Abstract

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.

Published in IEEE Access, 12 pages, 10 figures. DOI: 10.1109/ACCESS.2024.3508269

References in corpus (1)

• Automated Machine Learning: State-of-The-Art and Open Challenges