5 citations · 11 across the 30 of their papers we have counts for
17 papers · 1 filter
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…
Offline Multi-Task Multi-Objective Data-Driven Evolutionary Algorithm with Language Surrogate Model and Implicit Q-Learning
Xian-Rong Zhang, Yue-Jiao Gong, Zeyuan Ma +1
Data-driven evolutionary algorithms has shown surprising results in addressing expensive optimization problems through robust surrogate modeling. Though promising, existing surroga…
Learning Where, What and How to Transfer: A Multi-Role Reinforcement Learning Approach for Evolutionary Multitasking
Jiajun Zhan, Zeyuan Ma, Yue-Jiao Gong +1
Evolutionary multitasking (EMT) algorithms typically require tailored designs for knowledge transfer, in order to assure convergence and optimality in multitask optimization. In th…
Meta-Black-Box-Optimization through Offline Q-function Learning
Zeyuan Ma, Zhiguang Cao, Zhou Jiang +2
Recent progress in Meta-Black-Box-Optimization (MetaBBO) has demonstrated that using RL to learn a meta-level policy for dynamic algorithm configuration (DAC) over an optimization…
A Novel Two-Phase Cooperative Co-evolution Framework for Large-Scale Global Optimization with Complex Overlapping
Wenjie Qiu, Hongshu Guo, Zeyuan Ma +1
Cooperative Co-evolution, through the decomposition of the problem space, is a primary approach for solving large-scale global optimization problems. Typically, when the subspaces…
Accurate Peak Detection in Multimodal Optimization via Approximated Landscape Learning
Zeyuan Ma, Hongqiao Lian, Wenjie Qiu +1
Detecting potential optimal peak areas and locating the accurate peaks in these areas are two major challenges in Multimodal Optimization problems (MMOPs). To address them, much ef…
Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning
Hongshu Guo, Sijie Ma, Zechuan Huang +4
Recently, Meta-Black-Box-Optimization (MetaBBO) methods significantly enhance the performance of traditional black-box optimizers through meta-learning flexible and generalizable m…
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization
Zeyuan Ma, Hongshu Guo, Yue-Jiao Gong +2
In this survey, we introduce Meta-Black-Box-Optimization~(MetaBBO) as an emerging avenue within the Evolutionary Computation~(EC) community, which incorporates Meta-learning approa…
RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning
Hongqiao Lian, Zeyuan Ma, Hongshu Guo +2
Solving multimodal optimization problems (MMOP) requires finding all optimal solutions, which is challenging in limited function evaluations. Although existing works strike the bal…
Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning
Zeyuan Ma, Jiacheng Chen, Hongshu Guo +2
Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum. The exploration-exploi…
Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
Hongshu Guo, Yining Ma, Zeyuan Ma +5
Evolutionary algorithms, such as Differential Evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across dif…