Learning Dynamical Systems Using Standard Symbolic Regression
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- @InProceedings{gaucel:2014:EuroGP,
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author = "Sebastien Gaucel and Maarten Keijzer and
Evelyne Lutton and Alberto Tonda",
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title = "Learning Dynamical Systems Using Standard Symbolic
Regression",
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booktitle = "17th European Conference on Genetic Programming",
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year = "2014",
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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",
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series = "LNCS",
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volume = "8599",
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publisher = "Springer",
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pages = "25--36",
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address = "Granada, Spain",
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month = "23-25 " # apr,
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-662-44302-6",
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DOI = "doi:10.1007/978-3-662-44303-3_3",
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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.",
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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