1 citations · 1 across the 5 of their papers we have counts for
6 papers
PINNForge: Execution-Grounded Evolutionary Design of Physics-Informed Neural Networks
Mingyang Yu, Xu Yang, Jun Zhang +3
Physics-informed neural networks (PINNs) require coordinated choices across representation, architecture, sampling, constraints, loss construction, and optimization, yet effective…
Evolutionary Algorithm-Guided LLMs for Physics-Informed Neural Network Design
Xu Yang, Mingyang Yu, Jing Xu +1
Physics-informed neural networks (PINNs) are unusually sensitive to interacting choices of architecture, activation, loss weighting, collocation, optimization, and constraint enfor…
Large Language Model Assisted Automated Algorithm Generation and Evolution via Meta-black-box optimization
Xu Yang, Rui Wang, Kaiwen Li +2
Meta-black-box optimization has been significantly advanced through the use of large language models (LLMs), yet in fancy on constrained evolutionary optimization. In this work, Aw…
Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model
Xu Yang, Rui Wang, Kaiwen Li +2
Despite significant efforts to manually design high-performance evolutionary algorithms, their adaptability remains limited due to the dynamic and ever-evolving nature of real-worl…
PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization
Xu Yang, Rui Wang, Kaiwen Li +3
The landscape of optimization problems has become increasingly complex, necessitating the development of advanced optimization techniques. Meta-Black-Box Optimization (MetaBBO), wh…
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization
Xu Yang, Rui Wang, Kaiwen Li +1
Differential evolution (DE) algorithm is recognized as one of the most effective evolutionary algorithms, demonstrating remarkable efficacy in black-box optimization due to its der…