Promoting Semantics in Multi-objective Genetic Programming based on Decomposition

TL;DR

This paper investigates the impact of Semantic Similarity-based Crossover (SSC) on multi-objective genetic programming using decomposition, demonstrating that SSC enhances semantic diversity and improves results on the MNIST dataset.

Contribution

It extends the study of semantics in genetic programming to MOEA/D, showing that SSC benefits multi-objective evolutionary search with decomposition.

Findings

SSC promotes semantic diversity in MOEA/D

SSC leads to better performance on MNIST dataset

Semantic methods improve multi-objective GP results

Abstract

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.