A Functional Analysis Approach to Symbolic Regression

Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki Stein, Anna Kononova

Research output: Contribution to conferencePaperpeer-review

Abstract

Symbolic regression (SR) poses a significant challenge for randomized search heuristics due to its reliance on the synthesis of expressions for input-output mappings. Although traditional genetic programming (GP) algorithms have achieved success in various domains, they exhibit limited performance when tree-based representations are used for SR. To address these limitations, we introduce a novel SR approach called Fourier Tree Growing (FTG) that draws insights from functional analysis. This new perspective enables us to perform optimization directly in a different space, thus avoiding intricate symbolic expressions. Our proposed algorithm exhibits significant performance improvements over traditional GP methods on a range of classical one-dimensional benchmarking problems. To identify and explain the limiting factors of GP and FTG, we perform experiments on a large-scale polynomials benchmark with high-order polynomials up to degree 100. To the best of the authors' knowledge, this work represents the pioneering application of functional analysis in addressing SR problems. The superior performance of the proposed algorithm and insights into the limitations of GP open the way for further advancing GP for SR and related areas of explainable machine learning.

Original languageEnglish
Pages859-867
Number of pages9
DOIs
Publication statusPublished - 14 Jul 2024

Keywords

  • functional analysis
  • genetic programming
  • hilbert space optimization
  • symbolic regression

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