Symbolic Regression Algorithms with Built-in Linear Regression

Understand

Recently, several algorithms for symbolic regression (SR) emerged which employ a form of multiple linear regression (LR) to produce generalized linear models.

• The use of LR allows the algorithms to create models with relatively small error right from the beginning of the search; such algorithms are thus claimed to be (sometimes by orders of magnitude) faster than SR algorithms based on vanilla genetic programming.
• However, a systematic comparison of these algorithms on a common set of problems is still missing.
• In this paper we conceptually and experimentally compare several representatives of such algorithms (GPTIPS, FFX, and EFS).