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MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
Zeyuan Ma · Hongshu Guo · Jiacheng Chen · Zhenrui Li · Guojun Peng · Yue-Jiao Gong · Yining Ma · Zhiguang Cao

Thu Dec 14 02:05 PM -- 02:20 PM (PST) @
Event URL: https://github.com/GMC-DRL/MetaBox »

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.

Author Information

Zeyuan Ma (South China University of Technology)
Hongshu Guo (South China University of Technology)
Jiacheng Chen (South China University of Technology)
Zhenrui Li (South China University of Technology)
Guojun Peng (South China University of Technology)
Yue-Jiao Gong
Yining Ma (National University of Singapore)
Zhiguang Cao (Singapore Management University)

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