Multiobjective Genetic Programming for Nonlinear System Identification

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@InProceedings{Ferariu:2009:ICANNGA,
  author =       "Lavinia Ferariu and Alina Patelli",
  title =        "Multiobjective Genetic Programming for Nonlinear
                 System Identification",
  year =         "2009",
  booktitle =    "9th International Conference on Adaptive and Natural
                 Computing Algorithms, ICANNGA 2009",
  editor =       "Mikko Kolehmainen and Pekka Toivanen and 
                 Bartlomiej Beliczynski",
  series =       "Lecture Notes in Computer Science",
  volume =       "5495",
  pages =        "233--242",
  address =      "Kuopio, Finland",
  month =        "23-25 " # apr,
  publisher =    "Springer",
  note =         "Revised selected papers",
  keywords =     "genetic algorithms, genetic programming,
                 multiobjective optimisation, nonlinear system
                 identification",
  isbn13 =       "978-3-642-04920-0",
  DOI =          "doi:10.1007/978-3-642-04921-7_24",
  abstract =     "The paper presents a novel identification method,
                 which makes use of genetic programming for concomitant
                 flexible selection of models structure and parameters.
                 The case of nonlinear models, linear in parameters is
                 addressed. To increase the convergence speed, the
                 proposed algorithm considers customized genetic
                 operators and a local optimisation procedure, based on
                 QR decomposition, able to efficiently exploit the
                 linearity of the model subject to its parameters. Both
                 the model accuracy and parsimony are improved via a
                 multiobjective optimization, considering different
                 priority levels for the involved objectives. An
                 enhanced Pareto loop is implemented, by means of a
                 special fitness assignment technique and a migration
                 mechanism, in order to evolve accurate and compact
                 representations of dynamic nonlinear systems. The
                 experimental results reveal the benefits of the
                 proposed methodology within the framework of an
                 industrial system identification.",
  notes =        "ICANNGA 2009",
}

Genetic Programming entries for Lavinia Ferariu Alina Patelli

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