2 citations · 9 across the 30 of their papers we have counts for
7 papers · 2 filters
GeM-EA: A Generative and Meta-learning Enhanced Evolutionary Algorithm for Streaming Data-Driven Optimization
Yue Wu, Yuan-Ting Zhong, Ze-Yuan Ma +1
Streaming Data-Driven Optimization (SDDO) problems arise in many applications where data arrive continuously and the optimization environment evolves over time. Concept drift produ…
A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization
Wenjie Qiu, Zixin Wang, Hongyu Fang +2
Cooperative Coevolution (CC) effectively addresses Large-Scale Global Optimization (LSGO) via decomposition but struggles with the emerging class of Heterogeneous LSGO (H-LSGO) pro…
COBRA++: Enhanced COBRA Optimizer with Augmented Surrogate Pool and Reinforced Surrogate Selection
Zipei Yu, Zhiyang Huang, Hongshu Guo +2
The optimization problems in realistic world present significant challenges onto optimization algorithms, such as the expensive evaluation issue and complex constraint conditions.…
Surrogate Ensemble in Expensive Multi-Objective Optimization via Deep Q-Learning
Yuxin Wu, Hongshu Guo, Ting Huang +2
Surrogate-assisted Evolutionary Algorithms~(SAEAs) have shown promising robustness in solving expensive optimization problems. A key aspect that impacts SAEAs' effectiveness is sur…
Meta-Learning-Assisted Constraint Relaxation for Constrained Black-Box Optimization
Qianhao Zhu, Sijie Ma, Zeyuan Ma +3
Constraint handling is central to constrained black-box optimization (BBO), where objective improvement and feasibility restoration often provide conflicting search signals. Existi…
Evolution of Benchmark: Black-Box Optimization Benchmark Design through Large Language Model
Chen Wang, Sijie Ma, Zeyuan Ma +1
Benchmark Design in Black-Box Optimization (BBO) is a fundamental yet open-ended topic. Early BBO benchmarks are predominantly human-crafted, introducing expert bias and constraini…
Detect and Act: Automated Dynamic Optimizer through Meta-Black-Box Optimization
Zijian Gao, Yuanting Zhong, Zeyuan Ma +2
Dynamic Optimization Problems (DOPs) are challenging to address due to their complex nature, i.e., dynamic environment variation. Evolutionary Computation methods are generally adv…