14 papers
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…
Reinforcement Learning-assisted Constraint Relaxation for Constrained Expensive Optimization
Qianhao Zhu, Sijie Ma, Zeyuan Ma +2
Constraint handling plays a key role in solving realistic complex optimization problems. Though intensively discussed in the last few decades, existing constraint handling techniqu…
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…
READY: Reward Discovery for Meta-Black-Box Optimization
Zechuan Huang, Zhiguang Cao, Hongshu Guo +2
Meta-Black-Box Optimization (MetaBBO) is an emerging avenue within Optimization community, where algorithm design policy could be meta-learned by reinforcement learning to enhance…
Architectural Scaling Surpass Basis Complexity? Efficient KANs with Single-Parameter Design
Zhijie Chen, Xinglin Zhang, Hongshu Guo +1
The landscape of Kolmogorov-Arnold Networks (KANs) is rapidly expanding, yet lacks a unified theoretical framework and a clear principle for efficient architecture design. This pap…