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Sequential Symbolic Regression with Genetic Programming

Oliveira, Luiz O.V.B. and Otero, Fernando E.B. and Pappa, Gisele L. and Albinati, Julio (2015) Sequential Symbolic Regression with Genetic Programming. In: Worzel, Bill and Kotanchek, Mark and Riolo, Rick, eds. Genetic Programming Theory and Practice XII. Genetic and Evolutionary Computation . Springer, pp. 73-90. ISBN 978-3-319-16029-0. E-ISBN 978-3-319-16030-6. (doi:10.1007/978-3-319-16030-6_5)

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http://dx.doi.org/10.1007/978-3-319-16030-6_5

Abstract

This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the

problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances between the desired and obtained outputs and a geometric semantic operator. The rationale behind SSR is that, after generating a suboptimal function f via symbolic regression, the output errors can be approximated by another function in a subsequent iteration. The method was tested in eight polynomial functions, and compared with canonical genetic programming (GP) and geometric semantic genetic programming (SGP). Results showed that SSR significantly outperforms SGP and presents no statistical difference to GP. More importantly, they show the potential of the proposed strategy: an effective way of applying geometric semantic operators to combine different (partial) solutions, avoiding the exponential growth problem arising from the use of these operators.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-16030-6_5
Uncontrolled keywords: Symbolic Regression; Semantic Genetic Programming; Geometric Semantic Crossover; Problem Transformation
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 07 Aug 2014 20:14 UTC
Last Modified: 01 Aug 2019 10:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/42149 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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