Learning Dynamical Systems Using Standard Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{gaucel:2014:EuroGP,

• author = "Sebastien Gaucel and Maarten Keijzer and Evelyne Lutton and Alberto Tonda",
• title = "Learning Dynamical Systems Using Standard Symbolic Regression",
• booktitle = "17th European Conference on Genetic Programming",
• year = "2014",
• editor = "Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. {Rivas Santos} and Kevin Sim",
• series = "LNCS",
• volume = "8599",
• publisher = "Springer",
• pages = "25--36",
• address = "Granada, Spain",
• month = "23-25 " # apr,
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-662-44302-6",
• DOI = "doi:10.1007/978-3-662-44303-3_3",
• abstract = "Symbolic regression has many successful applications in learning free-form regular equations from data. Trying to apply the same approach to differential equations is the logical next step: so far, however, results have not matched the quality obtained with regular equations, mainly due to additional constraints and dependencies between variables that make the problem extremely hard to tackle. In this paper we propose a new approach to dynamic systems learning. Symbolic regression is used to obtain a set of first-order Eulerian approximations of differential equations, and mathematical properties of the approximation are then exploited to reconstruct the original differential equations. Advantages of this technique include the de-coupling of systems of differential equations, that can now be learnt independently; the possibility of exploiting established techniques for standard symbolic regression, after trivial operations on the original dataset; and the substantial reduction of computational effort, when compared to existing ad-hoc solutions for the same purpose. Experimental results show the efficacy of the proposed approach on an instance of the Lotka-Volterra model.",
• notes = "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014 and EvoApplications2014",

}

Genetic Programming entries for Sebastien Gaucel Maarten Keijzer Evelyne Lutton Alberto Tonda

Citations