SRBench: Symbolic Regression Benchmarks

The methods for symbolic regression (SR) have come a long way since the days of Koza-style genetic programming (GP). Our goal with this project is create a living benchmark of modern symbolic regression methods, in the context of state-of-the-art ML methods, and with a view towards high-impact applications in the health sciences.

Symbolic regression algorithms (marked with asterisk) benchmarked against black-box ML on hundreds of regression problems. La Cava et al. NeurIPS 2021
Symbolic regression algorithms (marked with asterisk) benchmarked against black-box ML on hundreds of regression problems. La Cava et al. NeurIPS 2021

Code

  • SRBench: A living Benchmark for Symbolic Regression

    • Selected Papers

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    Where are we now? A large benchmark study of recent symbolic regression methods
    Patryk Orzechowski, William La Cava, Jason H. Moore (2018)
    GECCO '18
    dl.acm.org  |  arXiv  |  pdf
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