5 citations · 11 across the 30 of their papers we have counts for
8 papers · 1 filter
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…
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…
Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization
Zeyuan Ma, Jiacheng Chen, Hongshu Guo +1
Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reduci…
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…
LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation
Zeyuan Ma, Hongshu Guo, Jiacheng Chen +4
Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer. Ho…
Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
Jiacheng Chen, Zeyuan Ma, Hongshu Guo +3
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers. Despite their success, t…