A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916682938712064 |
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| author | Kim, Chul Joe, Inwhee |
| author_facet | Kim, Chul Joe, Inwhee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization Kim, Chul Joe, Inwhee Neural and Evolutionary Computing Artificial Intelligence 68T20 I.2.8; G.1.6 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. |
| title | A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization |
| topic | Neural and Evolutionary Computing Artificial Intelligence 68T20 I.2.8; G.1.6 |
| url | https://arxiv.org/abs/2504.07359 |