Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression.

Abstract

Geometric Semantic Genetic Programming (GSGP) is a recently introduced form of Genetic Programming (GP) that searches the semantic space of functions/programs. The fitness landscape seen by GSGP is always -- for any domain and for any problem -- unimodal with a linear slope by construction. This makes the search for the optimum much easier than for traditional GP, and it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. Very recent work proposed a runtime analysis of mutation-based GSGP on the class of all Boolean functions. We present a runtime analysis of mutation-based GSGP on the class of all regression problems with generic basis functions (encompassing e.g., polynomial regression and trigonometric regression).