A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization
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Abstract (arXiv)
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.
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References · 9
- 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
- 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 466
- 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 111
- 2019A Survey of Optimization Methods from a Machine Learning Perspectivecited by 90
- 2022Recent Advances in Bayesian Optimizationcited by 74
- 2019Automated Machine Learning: State-of-The-Art and Open Challengescited by 29
- 2023Advancing Bayesian Optimization via Learning Correlated Latent Spacecited by 18
- 2022A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functionscited by 12
- 2018Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimizationcited by 5
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