MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911222335537152 |
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| author | Ma, Zeyuan Gong, Yue-Jiao Guo, Hongshu Qiu, Wenjie Ma, Sijie Lian, Hongqiao Zhan, Jiajun Chen, Kaixu Wang, Chen Huang, Zhiyang Huang, Zechuan Peng, Guojun Cheng, Ran Ma, Yining |
| author_facet | Ma, Zeyuan Gong, Yue-Jiao Guo, Hongshu Qiu, Wenjie Ma, Sijie Lian, Hongqiao Zhan, Jiajun Chen, Kaixu Wang, Chen Huang, Zhiyang Huang, Zechuan Peng, Guojun Cheng, Ran Ma, Yining |
| contents | Meta-Black-Box Optimization (MetaBBO) streamlines the automation of optimization algorithm design through meta-learning. It typically employs a bi-level structure: the meta-level policy undergoes meta-training to reduce the manual effort required in developing algorithms for low-level optimization tasks. The original MetaBox (2023) provided the first open-source framework for reinforcement learning-based single-objective MetaBBO. However, its relatively narrow scope no longer keep pace with the swift advancement in this field. In this paper, we introduce MetaBox-v2 (https://github.com/MetaEvo/MetaBox) as a milestone upgrade with four novel features: 1) a unified architecture supporting RL, evolutionary, and gradient-based approaches, by which we reproduce $23$ up-to-date baselines; 2) efficient parallelization schemes, which reduce the training/testing time by $10-40$x; 3) a comprehensive benchmark suite of $18$ synthetic/realistic tasks ($1900$+ instances) spanning single-objective, multi-objective, multi-model, and multi-task optimization scenarios; 4) plentiful and extensible interfaces for custom analysis/visualization and integrating to external optimization tools/benchmarks. To show the utility of MetaBox-v2, we carry out a systematic case study that evaluates the built-in baselines in terms of the optimization performance, generalization ability and learning efficiency. Valuable insights are concluded from thorough and detailed analysis for practitioners and those new to the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17745 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization Ma, Zeyuan Gong, Yue-Jiao Guo, Hongshu Qiu, Wenjie Ma, Sijie Lian, Hongqiao Zhan, Jiajun Chen, Kaixu Wang, Chen Huang, Zhiyang Huang, Zechuan Peng, Guojun Cheng, Ran Ma, Yining Machine Learning Artificial Intelligence Neural and Evolutionary Computing Meta-Black-Box Optimization (MetaBBO) streamlines the automation of optimization algorithm design through meta-learning. It typically employs a bi-level structure: the meta-level policy undergoes meta-training to reduce the manual effort required in developing algorithms for low-level optimization tasks. The original MetaBox (2023) provided the first open-source framework for reinforcement learning-based single-objective MetaBBO. However, its relatively narrow scope no longer keep pace with the swift advancement in this field. In this paper, we introduce MetaBox-v2 (https://github.com/MetaEvo/MetaBox) as a milestone upgrade with four novel features: 1) a unified architecture supporting RL, evolutionary, and gradient-based approaches, by which we reproduce $23$ up-to-date baselines; 2) efficient parallelization schemes, which reduce the training/testing time by $10-40$x; 3) a comprehensive benchmark suite of $18$ synthetic/realistic tasks ($1900$+ instances) spanning single-objective, multi-objective, multi-model, and multi-task optimization scenarios; 4) plentiful and extensible interfaces for custom analysis/visualization and integrating to external optimization tools/benchmarks. To show the utility of MetaBox-v2, we carry out a systematic case study that evaluates the built-in baselines in terms of the optimization performance, generalization ability and learning efficiency. Valuable insights are concluded from thorough and detailed analysis for practitioners and those new to the field. |
| title | MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2505.17745 |