1 citations · 1 across the 6 of their papers we have counts for
9 papers
Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification
Hengzhe Zhang, Qi Chen, Bing Xue +3
Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple b…
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…
GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework
Nathan Haut, Ilya Basin, Marzieh Kianinejad +4
Beagle is a new software framework that enables execution of Genetic Programming tasks on the GPU. Currently available for symbolic regression, it processes individuals of the popu…
Enhancing Generalization in Evolutionary Feature Construction for Symbolic Regression through Vicinal Jensen Gap Minimization
Hengzhe Zhang, Qi Chen, Bing Xue +2
Genetic programming-based feature construction has achieved significant success in recent years as an automated machine learning technique to enhance learning performance. However,…
Bridging Fitness With Search Spaces By Fitness Supremums: A Theoretical Study on LGP
Zhixing Huang, Yi Mei, Fangfang Zhang +2
Genetic programming has undergone rapid development in recent years. However, theoretical studies of genetic programming are far behind. One of the major obstacles to theoretical s…
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…
Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations
Zhixing Huang, Yi Mei, Fangfang Zhang +2
Existing genetic programming (GP) methods are typically designed based on a certain representation, such as tree-based or linear representations. These representations show various…
Sharpness-Aware Minimization in Genetic Programming
Illya Bakurov, Nathan Haut, Wolfgang Banzhaf
Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) functi…
Sharpness-Aware Minimization for Evolutionary Feature Construction in Regression
Hengzhe Zhang, Qi Chen, Bing Xue +2
In recent years, genetic programming (GP)-based evolutionary feature construction has achieved significant success. However, a primary challenge with evolutionary feature construct…