Effects of constant optimization by nonlinear least squares minimization in symbolic regression
Michael Kommenda, Gabriel Kronberger, Stephan Winkler, Michael Affenzeller, Ştefan Wagner · 2013
In this publication a constant optimization approach for symbolic regression is introduced to separate the task of finding the correct model structure from the necessity to evolve the correct numerical constants. A gradient-based nonlinear least squares optimization algorithm, the Levenberg-Marquardt (LM) algorithm, is used for adjusting constant values in symbolic expression trees during their evolution. The LM algorithm depends on gradient information consisting of partial derivations of the trees, which are obtained by automatic differentiation.