Enhanced symbolic regression through local variable transformations

Conference paper (2017)

Authors

Jiří Kubalík Czech Technical University
E. Derner Czech Technical University
R. Babuska Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering , Czech Technical University
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2017

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

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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