Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Created by W.Langdon from gp-bibliography.bib Revision:1.7941

@Misc{cranmer2023interpretablemachinelearningscience,

• title = "Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl",
• author = "Miles Cranmer",
• howpublished = "arXiv",
• year = "2023",
• edition = "v3",
• month = "5 " # may,
• keywords = "genetic algorithms, genetic programming, GPU, Julia",
• eprint = "2305.01582",
• archiveprefix = "arXiv",
• primaryclass = "astro-ph.IM",
• URL = "https://arxiv.org/abs/2305.01582",
• code_url = "https://github.com/MilesCranmer/PySR",
• size = "24 pages",
• abstract = "PySR is an open-source library for practical symbolic regression, a type of machine learning which aims to discover human-interpretable symbolic models. PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep learning packages. PySR internal search algorithm is a multi-population evolutionary algorithm, which consists of a unique evolve-simplify-optimize loop, designed for optimization of unknown scalar constants in newly-discovered empirical expressions. PySR backend is the extremely optimized Julia library SymbolicRegression.jl, which can be used directly from Julia. It is capable of fusing user-defined operators into SIMD kernels at runtime, performing automatic differentiation, and distributing populations of expressions to thousands of cores across a cluster. In describing this software, we also introduce a new benchmark, EmpiricalBench, to quantify the applicability of symbolic regression algorithms in science. This benchmark measures recovery of historical empirical equations from original and synthetic datasets.",

}

Genetic Programming entries for Miles Cranmer

Citations