Genetic Programming with Rademacher Complexity

This repository contains code for reproducing the experiments in the paper "Genetic Programming with Rademacher Complexity for Symbolic Regression" by Christian Raymond, Qi Chen, Bing Xue, and Mengjie Zhang.

Contents

Implementation of Genetic Programming for Symbolic Regression (GP-SR) and the newly proposed Genetic Programming with Rademacher Complexity (GPRC):

• Genetic Programming for Symbolic Regression (GP-SR) A prototypical implementation of Genetic Programming for Symbolic Regression. This program aims to map the input data to the output data through the use of a symbolic representation (expression trees) and evolutionary techniques.

• Genetic Programming with Rademacher Complexity (GP-RC) An implementation of Genetic Programming for Symbolic Regression that uses the Rademacher Complexity to estimate the complexity of the hypotheses generated in the evolutionary process.

Code Reproducibility:

The code has not been comprehensively checked and re-run since refactoring. If you're having any issues, find a problem/bug or cannot reproduce similar results as the paper please open an issue or email me.

Reference

If you use our library or find our research of value please consider citing our papers with the following Bibtex entry:

@inproceedings{raymond2019genetic,
  title={Genetic Programming with Rademacher Complexity for Symbolic Regression},
  author={Raymond, Christian and Chen, Qi and Xue, Bing and Zhang, Mengjie},
  booktitle={2019 IEEE Congress on Evolutionary Computation (CEC)},
  pages={2657--2664},
  year={2019},
  organization={IEEE}
}