  Semantics has become a key topic of research in Genetic Programming (GP).
Semantics refers to the outputs (behaviour) of a GP individual when this is run
on a data set. The majority of works that focus on semantic diversity in
single-objective GP indicates that it is highly beneficial in evolutionary
search. Surprisingly, there is minuscule research conducted in semantics in
Multi-objective GP (MOGP). In this work we make a leap beyond our understanding
of semantics in MOGP and propose SDO: Semantic-based Distance as an additional
criteriOn. This naturally encourages semantic diversity in MOGP. To do so, we
find a pivot in the less dense region of the first Pareto front (most promising
front). This is then used to compute a distance between the pivot and every
individual in the population. The resulting distance is then used as an
additional criterion to be optimised to favour semantic diversity. We also use
two other semantic-based methods as baselines, called Semantic Similarity-based
Crossover and Semantic-based Crowding Distance. Furthermore, we also use the
NSGA-II and the SPEA2 for comparison too. We use highly unbalanced binary
classification problems and consistently show how our proposed SDO approach
produces more non-dominated solutions and better diversity, leading to better
statistically significant results, using the hypervolume results as evaluation
measure, compared to the rest of the other four methods.
