2 citations · 9 across the 30 of their papers we have counts for
32 papers
Learning to Optimize UAV Path Planning for Data Sensing in Wireless Sensor Networks
Sijie Ma, Zeyuan Ma, Weijia Cao +4
UAVs have emerged as highly flexible platforms for data sensing in Wireless Sensor Networks (WSNs). Path planning for UAVs in such tasks plays a key role to assure remote sensing e…
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…
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…
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…
Evolutionary System 2 Reasoning: An Empirical Proof
Zeyuan Ma, Wenqi Huang, Guo-Huan Song +4
Machine intelligence marks the ultimate dream of making machines' intelligence comparable to human beings. While recent progress in Large Language Models (LLMs) show substantial sp…
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…
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…
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…
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…
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…
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…
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
Hongshu Guo, Zeyuan Ma, Jiacheng Chen +4
Recent advances in Meta-learning for Black-Box Optimization (MetaBBO) have shown the potential of using neural networks to dynamically configure evolutionary algorithms (EAs), enha…