IEEE

Multiform Genetic Programming Framework for Symbolic Regression Problems

Abstract: genetic programming (GP) is a widely recognized and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on a single form to solve the problem, which ...
生物通

Hosemann-Schramek模型在描述和预测实验单峰分子量分布中的评估

模拟分子量分布（Molecular Weight Distribution，MWD）是计算建模中预测聚合物体系热力学和流变性质的关键步骤。本研究利用从文献中汇编的30组实验分子量分布数据，评估了两个参数模型——对数正态（log-Normal）和Schulz-F 模拟分子量分布（Molecular Weight Distribution，MWD ...
IEEE

Multiexpression Symbolic Regression and Its Circuit Design Case

Abstract: Symbolic regression is commonly considered in wide-ranging applications due to its inherent capability for learning both structure and weighting parameters of an interpretable model. However ...
GitHub

FunctionEvolve: Structure-Guided Symbolic Regression with LLMs

FunctionEvolve is a neuro-symbolic evolutionary framework for scientific equation discovery. It combines LLM reasoning with an explicit abstract-syntax-tree (AST) search: domain-informed seeds and ...
GitHub

Improving Monte Carlo Tree Search for Symbolic Regression

MCTS-4-SR is a C++20 implementation of Monte Carlo Tree Search for symbolic regression, with Python bindings exposed through pybind11. The repository includes the C++ search core, the imcts Python ...