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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.16206 |
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| _version_ | 1866912343695294464 |
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| author | Zhan, Dawei Zeng, Zhaoxi Wei, Shuoxiao Wu, Ping |
| author_facet | Zhan, Dawei Zeng, Zhaoxi Wei, Shuoxiao Wu, Ping |
| contents | Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology. However, most of current batch approaches do not scale well with the batch size. That is, their performances deteriorate dramatically as the batch size increases. To address this issue, we propose a simple and efficient approach to extend Bayesian optimization to large-scale batch evaluation in this work. Different from existing batch approaches, the idea of the new approach is to draw a batch of axis-aligned subspaces of the original problem and select one acquisition point from each subspace. To achieve this, we propose the expected subspace improvement criterion to measure the amount of the improvement that a candidate point can achieve within a certain axis-aligned subspace. By optimizing these expected subspace improvement functions simultaneously, we can get a batch of query points for parallel evaluation. Numerical experiments show that our proposed approach can speedup the convergence significantly when compared with the sequential Bayesian optimization algorithm, and performs very competitively when compared with seven batch Bayesian optimization algorithms. A Matlab implementation of the proposed approach is available at https://github.com/zhandawei/Expected_Subspace_Improvement_Batch_Bayesian_Optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16206 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Simple and Efficient Approach to Batch Bayesian Optimization Zhan, Dawei Zeng, Zhaoxi Wei, Shuoxiao Wu, Ping Machine Learning Artificial Intelligence Neural and Evolutionary Computing Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology. However, most of current batch approaches do not scale well with the batch size. That is, their performances deteriorate dramatically as the batch size increases. To address this issue, we propose a simple and efficient approach to extend Bayesian optimization to large-scale batch evaluation in this work. Different from existing batch approaches, the idea of the new approach is to draw a batch of axis-aligned subspaces of the original problem and select one acquisition point from each subspace. To achieve this, we propose the expected subspace improvement criterion to measure the amount of the improvement that a candidate point can achieve within a certain axis-aligned subspace. By optimizing these expected subspace improvement functions simultaneously, we can get a batch of query points for parallel evaluation. Numerical experiments show that our proposed approach can speedup the convergence significantly when compared with the sequential Bayesian optimization algorithm, and performs very competitively when compared with seven batch Bayesian optimization algorithms. A Matlab implementation of the proposed approach is available at https://github.com/zhandawei/Expected_Subspace_Improvement_Batch_Bayesian_Optimization. |
| title | A Simple and Efficient Approach to Batch Bayesian Optimization |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2411.16206 |