Expansion: A Novel Mutation Operator for Genetic Programming

Mohiul Islam, Nawwaf Kharma, Peter Grogono

2018

Abstract

Expansion is a novel mutation operator for Genetic Programming (GP). It uses Monte Carlo simulation to repeatedly expand and evaluate programs using unit instructions, taking advantage of the granular search space of evolutionary program synthesis. Monte Carlo simulation and its heuristic search method, Monte Carlo Tree Search has been applied to Koza-style tree-based representation to compare results with different variation operations such as sub-tree crossover and point mutation. Using a set of benchmark symbolic regression problems, we prove that expansion have better fitness performance than point mutation, when included with crossover. It also provides significant boost in fitness when compared with GP using only crossover on a diverse problem set. We conclude that the best fitness can be achieved by including all three operators in GP, crossover, point mutation and expansion.

Paper Citation

in Harvard Style

Islam M., Kharma N. and Grogono P. (2018). Expansion: A Novel Mutation Operator for Genetic Programming. In Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI; ISBN 978-989-758-327-8, SciTePress, pages 55-66. DOI: 10.5220/0006927800550066

in Bibtex Style

@conference{ijcci18,
author={Mohiul Islam and Nawwaf Kharma and Peter Grogono},
title={Expansion: A Novel Mutation Operator for Genetic Programming},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI},
year={2018},
pages={55-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006927800550066},
isbn={978-989-758-327-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - Volume 1: IJCCI
TI - Expansion: A Novel Mutation Operator for Genetic Programming
SN - 978-989-758-327-8
AU - Islam M.
AU - Kharma N.
AU - Grogono P.
PY - 2018
SP - 55
EP - 66
DO - 10.5220/0006927800550066
PB - SciTePress