Semantic-based Distance Approaches in Multi-objective Genetic Programming

TL;DR

This paper compares three semantic-based methods in multi-objective genetic programming, demonstrating that semantic distance approaches improve performance and diversity over traditional methods, with specific emphasis on the use of a pivot for semantic diversity.

Contribution

It introduces and empirically evaluates two novel semantic distance methods in MOGP, highlighting their advantages over existing semantic and canonical approaches.

Findings

Semantic distance methods outperform canonical approaches in MOGP.

Using a pivot enhances diversity by balancing semantic similarity and dissimilarity.

Single-objective semantic success does not directly translate to multi-objective scenarios.

Abstract

Semantics in the context of Genetic Program (GP) can be understood as the behaviour of a program given a set of inputs and has been well documented in improving performance of GP for a range of diverse problems. There have been a wide variety of different methods which have incorporated semantics into single-objective GP. The study of semantics in Multi-objective (MO) GP, however, has been limited and this paper aims at tackling this issue. More specifically, we conduct a comparison of three different forms of semantics in MOGP. One semantic-based method, (i) Semantic Similarity-based Crossover (SSC), is borrowed from single-objective GP, where the method has consistently being reported beneficial in evolutionary search. We also study two other methods, dubbed (ii) Semantic-based Distance as an additional criteriOn (SDO) and (iii) Pivot Similarity SDO. We empirically and consistently show how by naturally handling semantic distance as an additional criterion to be optimised in MOGP leads to better performance when compared to canonical methods and SSC. Both semantic distance based approaches made use of a pivot, which is a reference point from the sparsest region of the search space and it was found that individuals which were both semantically similar and dissimilar to this pivot were beneficial in promoting diversity. Moreover, we also show how the semantics successfully promoted in single-objective optimisation does not necessary lead to a better performance when adopted in MOGP.