Symbolic-regression boosting

Moshe Sipper, Jason H. Moore

Research output: Contribution to journalLetterpeer-review

Abstract

Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: symbolic-regression boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages—between 2 and 5—to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.

Original languageEnglish
Pages (from-to)357-381
Number of pages25
JournalGenetic Programming and Evolvable Machines
Volume22
Issue number3
DOIs
StatePublished - 1 Sep 2021

Keywords

  • Genetic programming
  • Gradient boosting
  • Symbolic regression

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Science Applications

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