• Corpus ID: 236635250

Contemporary Symbolic Regression Methods and their Relative Performance

@article{Cava2021ContemporarySR,
  title={Contemporary Symbolic Regression Methods and their Relative Performance},
  author={W. L. Cava and Patryk Orzechowski and Bogdan Burlacu and Fabr'icio Olivetti de Francca and M. Virgolin and Ying Jin and Michael Kommenda and Jason H. Moore},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.14351}
}
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. We address this shortcoming by introducing an open-source, reproducible benchmarking platform for symbolic regression. We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems. Our assessment includes both real-world datasets with no… 

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    Proceedings of the Genetic and Evolutionary Computation Conference
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