GMC-DRL Team

GMC-DRL Team

Research team on (Meta-)Black-Box Optimization, Deep Reinforcement Learning and Large Language Model

Publications

Benchmarking MetaBBO Approaches

YearPaper
2023Zeyuan Ma, et al. “MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning.” Advances in Neural Information Processing Systems 36 (NeurIPS 2023).

Developing MetaBBO Approaches

YearPaper
2024Zeyuan Ma*, Jiacheng Chen*, Hongshu Guo, et. al. “Neural Exploratory Landscape Analysis” arXiv preprint arXiv:2408.10672 (2024).
2024Zeyuan Ma*, Jiacheng Chen*, Hongshu Guo, et. al. “Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning” The Genetic and Evolutionary Computation Conference (2024).
2024Hongqiao Lian, Zeyuan Ma, Hongshu Guo, et. al. “RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning” The Genetic and Evolutionary Computation Conference (2024).
2024Hongshu Guo, Zeyuan Ma, Jiacheng Chen, et. al. “Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution” IEEE Transactions on Systems, Man, and Cybernetics: Systems (2024).
2024Jiacheng Chen*, Zeyuan Ma*, et. al. “Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning” The Twelfth International Conference on Learning Representations (ICLR 2024)

Exploring AGI (AIGC) in Optimization

YearPaper
2024Zeyuan Ma, Hongshu Guo, Jiacheng Chen, et. al. “LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation”

Other Topics in Optimization

YearPaper
2023SC Lei, Hongshu Guo, et. al. “A High-Performance Tensorial Evolutionary Computation for Solving Spatial Optimization Problems” International Conference on Neural Information Processing (ICONIP 2023)
2021Zeyuan Ma, et. al. “An efficient computational approach for automatic itinerary planning on web servers” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021)