Behavioral Locality in Genetic Programming

Adam Kotaro Pindur, Hitoshi Iba

2020

Abstract

Locality is a key concept affecting exploration and exploitation in evolutionary computation systems. Genotype to phenotype mapping has locality if the neighborhood is preserved under that mapping. Unfortunately, assessment of the locality in Genetic Programming is dependent on the distance metric used to compare program trees. Furthermore, there is no distinction between genotype and phenotype in GP. As such the definition of locality in GP was studied only in the context of genotype-fitness mapping. In this work we propose a different family of similarity measures, graph kernels, as alternatives to the traditional family of distance metrics in use, that is edit distance. Traditional tree edit distance is compared to the Weisfeiler-Lehman Subtree Kernel, which is considered to be the state-of-the-art method in graph classification. Additionally, we extend the definition of the locality of GP, by studying the relationship between genotypes and behaviors. In this paper, we consider a mutation-based GP system applied to two basic benchmark problems: artificial ant (multimodal deceptive landscape) and even parity (highly neutral landscape).

Paper Citation

in Harvard Style

Pindur A. and Iba H. (2020). Behavioral Locality in Genetic Programming. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA; ISBN 978-989-758-475-6, SciTePress, pages 81-91. DOI: 10.5220/0010113400810091

in Bibtex Style

@conference{ecta20,
author={Adam Kotaro Pindur and Hitoshi Iba},
title={Behavioral Locality in Genetic Programming},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA},
year={2020},
pages={81-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010113400810091},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA
TI - Behavioral Locality in Genetic Programming
SN - 978-989-758-475-6
AU - Pindur A.
AU - Iba H.
PY - 2020
SP - 81
EP - 91
DO - 10.5220/0010113400810091
PB - SciTePress