9 citations · 34 across the 18 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…
Learning to Handle Complex Constraints for Vehicle Routing Problems
Jieyi Bi, Yining Ma, Jianan Zhou +4
Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feas…
Hierarchical Neural Constructive Solver for Real-world TSP Scenarios
Yong Liang Goh, Zhiguang Cao, Yining Ma +3
Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learnin…
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Jianan Zhou, Zhiguang Cao, Yaoxin Wu +4
Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, mak…
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…