Elitist multiobjective nonlinear systems identification with insular evolution and diversity preservation

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@InProceedings{Patelli:2010:cec,
  author =       "Alina Patelli and Lavinia Ferariu",
  title =        "Elitist multiobjective nonlinear systems
                 identification with insular evolution and diversity
                 preservation",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "The paper suggests a customised elite based genetic
                 programming technique for the identification of complex
                 nonlinear systems. The models generated by the proposed
                 method are nonlinear, linear in parameters, as the
                 universal approximation capacities of such a
                 mathematical formalism have been rigorously proven. To
                 better exploit the models' parameter wise linearity,
                 the authors propose a memetic approach that combines
                 the stochastic structural transitions caused by
                 enhanced genetic operators, with a deterministic
                 parameter computation routine based on QR
                 decomposition. This symbiosis assures a quasi
                 simultaneous model structure and parameters selection
                 and heightened search space exploration capabilities.
                 To better fit the requirements of systems
                 identification, the problem is formulated as a
                 multiobjective optimization one, employing accuracy and
                 parsimony assessment criteria. Two elitist evolutionary
                 procedures have been implemented to obtain a solution,
                 each featuring original contributions: the first one
                 employs a dynamic clustering mechanism aimed at
                 encouraging specific solutions of interest for the
                 problem at hand, whilst the second is oriented towards
                 maintaining population diversity by means of similarity
                 analysis. The practical efficiency of the described
                 methods is demonstrated relative to a multivariable
                 test system with delayed inputs and a complex
                 industrial plant.",
  DOI =          "doi:10.1109/CEC.2010.5586212",
  notes =        "WCCI 2010. Also known as \cite{5586212}",
}

Genetic Programming entries for Alina Patelli Lavinia Ferariu

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