Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition
This work addresses a gap in multi-objective optimization for genetic programming, though it is incremental as it extends existing decomposition-based methods.
The paper tackles the lack of semantic diversity in multi-objective genetic programming by introducing a method to promote it within MOEA/D, showing improved performance in evolutionary search.
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.