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