2004 | OriginalPaper | Buchkapitel
Symbolic Regression Problems by Genetic Programming with Multi-branches
verfasst von : Carlos Oliver Morales, Katya Rodríguez Vázquez
Erschienen in: MICAI 2004: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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This work has the aim of exploring the area of symbolic regression problems by means of Genetic Programming. It is known that symbolic regression is a widely used method for mathematical function approximation. Previous works based on Genetic Programming have already dealt with this problem, but considering Koza’s GP approach. This paper introduces a novel GP encoding based on multi-branches. In order to show the use of the proposed multi-branches representation, a set of testing equations has been selected. Results presented in this paper show the advantages of using this novel multi-branches version of GP.