Shape Constraints in Symbolic Regression using Penalized Least Squares

arXiv:2405.20800v1 Announce Type: new
Abstract: We study the addition of shape constraints and their consideration during the parameter estimation step of symbolic regression (SR). Shape constraints serve as a means to introduce prior knowledge about the shape of the otherwise unknown model function into SR. Unlike previous works that have explored shape constraints in SR, we propose minimizing shape constraint violations during parameter estimation using gradient-based numerical optimization.
We test three algorithm variants to evaluate their performance in identifying three …

abstract arxiv constraints cs.lg cs.sc function knowledge least prior regression serve shape squares study type