ParFam - (Neural Guided) Symbolic Regression Based on Continuous Global Optimization

Scholl, Philipp; Bieker, Katharina; Hauger, Hillary und Kutyniok, Gitta (2025): ParFam - (Neural Guided) Symbolic Regression Based on Continuous Global Optimization. ICLR 2025: The Thirteenth International Conference on Learning Representations, Singapur, 24. - 28. April 2025.

Abstract

The problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of financial markets from given data. Various methods exist to address the problem of SR, often based on genetic programming. However, these methods are usually complicated and involve various hyperparameters. In this paper, we present our new approach ParFam that utilizes parametric families of suitable symbolic functions to translate the discrete symbolic regression problem into a continuous one, resulting in a more straightforward setup compared to current state-of-the-art methods. In combination with a global optimizer, this approach results in a highly effective method to tackle the problem of SR. We theoretically analyze the expressivity of ParFam and demonstrate its performance with extensive numerical experiments based on the common SR benchmark suit SRBench, showing that we achieve state-of-the-art results. Moreover, we present an extension incorporating a pre-trained transformer network (DL-ParFam) to guide ParFam, accelerating the optimization process by up to two magnitudes. Our code and results can be found at https://github.com/Philipp238/parfam.

• Dokumententyp: Konferenzbeitrag (Paper)
• Keywords: Symbolic Regression; Machine Learning; Pre-Training; Deep Learning
• Fakultät: Mathematik, Informatik und Statistik ; Mathematik, Informatik und Statistik > Mathematik ; Mathematik, Informatik und Statistik > Mathematik > Professur für Mathematische Grundlagen des Verständnisses der künstlichen Intelligenz
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Sprache: Englisch