A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization
This work addresses hyperparameter optimization for machine learning practitioners, but it is incremental as it builds on existing evolutionary algorithms.
The paper tackled the problem of hyperparameter optimization by balancing exploration and exploitation, achieving an average improvement of 1.89% over existing methods.
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.