Ordinal Pareto Genetic Programming

Abstract

This paper introduces the first attempt to combine the theory of ordinal optimization and symbolic regression via genetic programming. A new approach called ordinal ParetoGP allows obtaining considerably fitter solutions with more consistency between independent runs while spending less computational effort. The conclusions are supported by a number of experiments using three symbolic regression benchmark problems of various size.