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Authors: Jirí Kubalík 1 ; Erik Derner 2 and Robert Babuška 3

Affiliations: 1 Czech Technical University in Prague, Czech Republic ; 2 Czech Technical University in Prague, Faculty of Electrical Engineering and Czech Technical University in Prague, Czech Republic ; 3 Czech Technical University in Prague, Czech Republic and Faculty of 3mE, Delft University of Technology, Netherlands

ISBN: 978-989-758-274-5

Keyword(s): Symbolic Regression, Single Node Genetic Programming, Nonlinear Regression, Data-driven Modeling

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Memetic Algorithms ; Soft Computing

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.

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Paper citation in several formats:
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

@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},
}

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

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