  Semantic diversity in Genetic Programming has proved to be highly beneficial
in evolutionary search. We have witnessed a surge in the number of scientific
works in the area, starting first in discrete spaces and moving then to
continuous spaces. The vast majority of these works, however, have focused
their attention on single-objective genetic programming paradigms, with a few
exceptions focusing on Evolutionary Multi-objective Optimization (EMO). The
latter works have used well-known robust algorithms, including the
Non-dominated Sorting Genetic Algorithm II and the Strength Pareto Evolutionary
Algorithm, both heavily influenced by the notion of Pareto dominance. These
inspiring works led us to make a step forward in EMO by considering
Multi-objective Evolutionary Algorithms Based on Decomposition (MOEA/D). We
show, for the first time, how we can promote semantic diversity in MOEA/D in
Genetic Programming.
