On the use of semantics in multi-objective genetic programming

Abstract

Research on semantics in Genetic Programming (GP) has increased dramatically over the last number of years. Results in this area clearly indicate that its use in GP can considerably increase GP performance. Motivated by these results, this paper investigates for the first time the use of Semantics in Muti-objective GP within the well-known NSGA-II algorithm. To this end, we propose two forms of incorporating semantics into a MOGP system. Results on challenging (highly) unbalanced binary classification tasks indicate that the adoption of semantics in MOGP is beneficial, in particular when a semantic distance is incorporated into the core of NSGA-II.