2 citations · 9 across the 30 of their papers we have counts for
18 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…
Evolutionary Neural Architecture Search with Dual Contrastive Learning
Xian-Rong Zhang, Yue-Jiao Gong, Wei-Neng Chen +1
Evolutionary Neural Architecture Search (ENAS) has gained attention for automatically designing neural network architectures. Recent studies use a neural predictor to guide the pro…
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…