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

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{kim2025_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 }
}