101 citations · 274 across the 40 of their papers we have counts for
24 papers · 1 filter
Benchmarking Zero-Shot LLM-Generated Parent Selection in Genetic Programming for Symbolic Regression
Hengzhe Zhang, Qi Chen, Bing Xue +2
Parent selection significantly affects exploration, exploitation, and complexity control in genetic programming (GP) for symbolic regression. It is unclear whether large language m…
Search Hardness-Aware LLM-Based Problem Formulation for Expensive Simulation-Driven Design
Yuchen Li, Handing Wang, Bing Xue +1
Expensive simulation-driven design is widely used in engineering to identify requirement-satisfying designs with as few high-fidelity simulations as possible. Most existing efforts…
Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Zhixing Huang, Bing Xue, Mengjie Zhang +3
Machine learning techniques play an important role in analyzing spectral data. The spectral data of fish biomass is useful in fish production, as it carries many important chemistr…
LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression
Hengzhe Zhang, Qi Chen, Bing Xue +2
Large language models (LLMs) have revolutionized algorithm development, yet their application in symbolic regression, where algorithms automatically discover symbolic expressions f…
A Genetic Algorithm-Based Approach for Automated Optimization of Kolmogorov-Arnold Networks in Classification Tasks
Quan Long, Bin Wang, Bing Xue +1
To address the issue of interpretability in multilayer perceptrons (MLPs), Kolmogorov-Arnold Networks (KANs) are introduced in 2024. However, optimizing KAN structures is labor-int…
Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning
Christian Raymond, Qi Chen, Bing Xue +1
In this paper, we develop upon the topic of loss function learning, an emergent meta-learning paradigm that aims to learn loss functions that significantly improve the performance…