An Improved Single Node Genetic Programming for Symbolic Regression

Jiří Kubalík, Robert Babuška

2015

Abstract

This paper presents a first step of our research on designing an effective and efficient GP-based method for solving the symbolic regression. We have proposed three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on the depth of the nodes, (2) operators for placing a compact version of the best tree to the beginning and to the end of the population, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on three symbolic regression problems and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to significantly improve the performance of the SNGP algorithm.

Paper Citation

in Harvard Style

Kubalík J. and Babuška R. (2015). An Improved Single Node Genetic Programming for Symbolic Regression . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 244-251. DOI: 10.5220/0005598902440251

in Bibtex Style

@conference{ecta15,
author={Jiří Kubalík and Robert Babuška},
title={An Improved Single Node Genetic Programming for Symbolic Regression},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={244-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005598902440251},
isbn={978-989-758-157-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - An Improved Single Node Genetic Programming for Symbolic Regression
SN - 978-989-758-157-1
AU - Kubalík J.
AU - Babuška R.
PY - 2015
SP - 244
EP - 251
DO - 10.5220/0005598902440251