  The study of semantics in Genetic Program (GP) deals with the behaviour of a
program given a set of inputs and has been widely reported in helping to
promote diversity in GP for a range of complex problems ultimately improving
evolutionary search. The vast majority of these studies have focused their
attention in single-objective GP, with just a few exceptions where Pareto-based
dominance algorithms such as NSGA-II and SPEA2 have been used as frameworks to
test whether highly popular semantics-based methods, such as Semantic
Similarity-based Crossover (SSC), helps or hinders evolutionary search.
Surprisingly it has been reported that the benefits exhibited by SSC in SOGP
are not seen in Pareto-based dominance Multi-objective GP. In this work, we are
interested in studying if the same carries out in Multi-objective Evolutionary
Algorithms based on Decomposition (MOEA/D). By using the MNIST dataset, a
well-known dataset used in the machine learning community, we show how SSC in
MOEA/D promotes semantic diversity yielding better results compared to when
this is not present in canonical MOEA/D.
