Interpretable scientific discovery with symbolic regression: a review

By Nour Makke, Sanjay Chawla. Artificial Intelligence Review, 2022

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Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery tool, achieving significant advances in various application domains ranging from fundamental to applied sciences. In this survey, we present a structured and comprehensive overview of symbolic regression methods, review the adoption of these methods for model discovery in various areas, and assess their effectiveness. We have also grouped state-of-the-art symbolic regression applications in a categorized manner in a living review.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/020f363fb9419903c5a4c9618b6c25718a7c6b97