2 citations · 9 across the 30 of their papers we have counts for
6 papers · 2 filters
Instance Generation for Meta-Black-Box Optimization through Latent Space Reverse Engineering
Chen Wang, Yue-Jiao Gong, Zhiguang Cao +1
To relieve intensive human-expertise required to design optimization algorithms, recent Meta-Black-Box Optimization (MetaBBO) researches leverage generalization strength of meta-le…
DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization
Hongshu Guo, Zeyuan Ma, Yining Ma +3
Designing effective black-box optimizers is hampered by limited problem-specific knowledge and manual control that spans months for almost every detail. In this paper, we present \…
MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization
Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo +11
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 p…
Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization
Hongshu Guo, Wenjie Qiu, Zeyuan Ma +3
Recent research in Cooperative Coevolution~(CC) have achieved promising progress in solving large-scale global optimization problems. However, existing CC paradigms have a primary…
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study
Zeyuan Ma, Zhiyang Huang, Jiacheng Chen +2
Recent Meta-Black-Box Optimization (MetaBBO) approaches have shown possibility of enhancing the optimization performance through learning meta-level policies to dynamically configu…
Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
Qi Li, Zhiguang Cao, Yining Ma +2
Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution…