Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming University of Antwerp
This paper presents a novel approach to generate data-driven regression models that not only give reliable prediction of the observed data but also have smoother response surfaces and extra generalization capabilities with respect to extrapolation. These models are obtained as solutions of a genetic programming (GP) process, where selection is guided by a tradeoff between two competing objectives - numerical accuracy and the order of ...