Parameter identification for symbolic regression using nonlinear least squares
Created by W.Langdon from
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- @Article{Kommenda:GPEM,
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author = "Michael Kommenda and Bogdan Burlacu and
Gabriel Kronberger and Michael Affenzeller",
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title = "Parameter identification for symbolic regression using
nonlinear least squares",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2020",
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volume = "21",
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number = "3",
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pages = "471--501",
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month = sep,
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note = "Special Issue on Integrating numerical optimization
methods with genetic programming",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Parameter identification, Nonlinear least
squares, Automatic differentiation",
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ISSN = "1389-2576",
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URL = "https://rdcu.be/cAIuP",
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DOI = "doi:10.1007/s10710-019-09371-3",
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size = "31 pages",
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abstract = "we analyse the effects of using non-linear least
squares for parameter identification of symbolic
regression models and integrate it as local search
mechanism in tree-based genetic programming. We employ
the Levenberg-Marquardt algorithm for parameter
optimization and calculate gradients via automatic
differentiation. We provide examples where the
parameter identification succeeds and fails and
highlight its computational overhead. Using an
extensive suite of symbolic regression benchmark
problems we demonstrate the increased performance when
incorporating nonlinear least squares within genetic
programming. Our results are compared with recently
published results obtained by several genetic
programming variants and state of the art machine
learning algorithms. Genetic programming with nonlinear
least squares performs among the best on the defined
benchmark suite and the local search can be easily
integrated in different genetic programming algorithms
as long as only differentiable functions are used
within the models.",
- }
Genetic Programming entries for
Michael Kommenda
Bogdan Burlacu
Gabriel Kronberger
Michael Affenzeller
Citations