Enhanced Symbolic Regression Through Local Variable Transformations

Jirí Kubalík, Erik Derner, Robert Babuška

2017

Abstract

Genetic programming (GP) is a technique widely used in a range of symbolic regression problems, in particular when there is no prior knowledge about the symbolic function sought. In this paper, we present a GP extension introducing a new concept of local transformed variables, based on a locally applied affine transformation of the original variables. This approach facilitates finding accurate parsimonious models. We have evaluated the proposed extension in the context of the Single Node Genetic Programming (SNGP) algorithm on synthetic as well as real-problem datasets. The results confirm our hypothesis that the transformed variables significantly improve the performance of the standard SNGP algorithm.

Paper Citation

in Harvard Style

Kubalík J., Derner E. and Babuška R. (2017). Enhanced Symbolic Regression Through Local Variable Transformations.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 91-100. DOI: 10.5220/0006505200910100

in Bibtex Style

@conference{ijcci17,
author={Jirí Kubalík and Erik Derner and Robert Babuška},
title={Enhanced Symbolic Regression Through Local Variable Transformations},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={91-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006505200910100},
isbn={978-989-758-274-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Enhanced Symbolic Regression Through Local Variable Transformations
SN - 978-989-758-274-5
AU - Kubalík J.
AU - Derner E.
AU - Babuška R.
PY - 2017
SP - 91
EP - 100
DO - 10.5220/0006505200910100