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Python code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (CEC-2019). Paper Link: https://ieeexplore.ieee.org/document/8790341

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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):

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}
}

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Python code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (CEC-2019). Paper Link: https://ieeexplore.ieee.org/document/8790341

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