Sijie MA's Homepage

Hi! I’m Sijie MA

I’m a senior undergraduate student majoring in Computer Science and Technology at South China University of Technology. With a strong interest in the intersection of machine learning and optimization, I’m currently focused on Meta Black-Box Optimization (MetaBBO).

I feel incredibly lucky to be recommended by Prof. Yuejiao Gong and to work under the guidance of Zeyuan Ma and Hongshu Guo. As a member of the MetaEvo group, I’m diving deep into the theory and practice of meta-learning, striving to build systems that adapt, generalize, and optimize across tasks.

News

Dec 25, 2025	I am grateful to have been awarded the National Scholarship for the second time!
Sep 19, 2025	🎉 MetaBox-v2 has been accepted at NeurIPS 2025 as a poster!
Mar 20, 2025	🎉 Our paper has been accepted to GECCO 2025! 🚀

Publications

Gecco2025

Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning

Hongshu Guo , Sijie Ma, Zechuan Huang, Yuzhi Hu, Zeyuan Ma, Xinglin Zhang, and Yue-Jiao Gong^*

arXiv preprint arXiv:2503.18061, Mar 2025

Abs arXiv Code

Recently, Meta-Black-Box-Optimization (MetaBBO) methods significantly enhance the performance of traditional black-box optimizers through meta-learning flexible and generalizable meta-level policies that excel in dynamic algorithm configuration (DAC) tasks within the low-level optimization, reducing the expertise required to adapt optimizers for novel optimization tasks. Though promising, existing MetaBBO methods heavily rely on human-crafted feature extraction approach to secure learning effectiveness. To address this issue, this paper introduces a novel MetaBBO method that supports automated feature learning during the meta-learning process, termed as RLDE-AFL, which integrates a learnable feature extraction module into a reinforcement learning-based DE method to learn both the feature encoding and meta-level policy. Specifically, we design an attention-based neural network with mantissa-exponent based embedding to transform the solution populations and corresponding objective values during the low-level optimization into expressive landscape features. We further incorporate a comprehensive algorithm configuration space including diverse DE operators into a reinforcement learning-aided DAC paradigm to unleash the behavior diversity and performance of the proposed RLDE-AFL. Extensive benchmark results show that co-training the proposed feature learning module and DAC policy contributes to the superior optimization performance of RLDE-AFL to several advanced DE methods and recent MetaBBO baselines over both synthetic and realistic BBO scenarios.
NeurIPS2025

MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization

Zeyuan Ma, Yue-Jiao Gong^*, Hongshu Guo, Wenjie Qiu , Sijie Ma, Hongqiao Lian, Jiajun Zhan, and 7 more authors

In Advances in Neural Information Processing Systems 38, Sep 2025

Abs arXiv Code

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 10−40x; 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.