  Semantics is a growing area of research in Genetic programming (GP) and
refers to the behavioural output of a Genetic Programming individual when
executed. This research expands upon the current understanding of semantics by
proposing a new approach: Semantic-based Distance as an additional criteriOn
(SDO), in the thus far, somewhat limited researched area of semantics in
Multi-objective GP (MOGP). Our work included an expansive analysis of the GP in
terms of performance and diversity metrics, using two additional semantic-based
approaches, namely Semantic Similarity-based Crossover (SCC) and Semantic-based
Crowding Distance (SCD). Each approach is integrated into two evolutionary
multi-objective (EMO) frameworks: Non-dominated Sorting Genetic Algorithm II
(NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and along
with the three semantic approaches, the canonical form of NSGA-II and SPEA2 are
rigorously compared. Using highly-unbalanced binary classification datasets, we
demonstrated that the newly proposed approach of SDO consistently generated
more non-dominated solutions, with better diversity and improved hypervolume
results.

Neural and Evolutionary Computing