A Comparative Study on the Numerical Performance of Kaizen Programming and Genetic Programming for Symbolic Regression Problems

Symbolic Regression (SR) is a problem that arises in the context of surrogate modeling and involves the fitting of a mathematical model to an input-output data set. Kaizen Programming (KP) is a novel algorithm for solving SR problems. This work presents a comparative analysis on the performance of KP and Genetic Programming (GP) for SR on 15 optimization benchmark functions and an industrial process application case. The experimental analysis shows that KP has a better performance than GP in almost all benchmark cases and in the application case. Also, the results of KP are competitive with state of the art algorithms reported in previous works. This work provides additional evidence on the benefits of KP and corroborates that KP represents a promising solver for SR problems.