Increasing crossover operator efficiency in multiobjective nonlinear systems identification

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Patelli:2010:IS,
  author =       "Alina Patelli and Lavinia Ferariu",
  title =        "Increasing crossover operator efficiency in
                 multiobjective nonlinear systems identification",
  booktitle =    "5th IEEE International Conference Intelligent Systems
                 (IS)",
  year =         "2010",
  month =        jul,
  pages =        "426--431",
  abstract =     "An elitist multiobjective optimisation methodology,
                 based on genetic programming, is suggested in the
                 following, as means of identifying complex nonlinear
                 systems. The structure and parameters of the nonlinear
                 models are selected simultaneously as result of the
                 conjoint usage of customised genetic operators and of a
                 deterministic parameter computation procedure. This
                 symbiosis is configured to efficiently exploit the
                 nonlinear, linear in parameters formalism, a proven
                 universal approximator, according to which the models
                 are generated. In order to protect useful model terms
                 from fragmentation via crossover, the authors have
                 introduced a novel encapsulation mechanism supervised
                 by a fuzzy controller. To meet the specific
                 requirements of systems identification in engineering
                 applications, the optimisation procedure considers two
                 evaluation criteria, namely accuracy and parsimony,
                 exploited from an elitist standpoint. The approach also
                 features an original similarity analysis technique,
                 meant to encourage population diversity. The practical
                 efficiency of the proposed identification algorithm was
                 tested in the framework of a real life industrial
                 system.",
  keywords =     "genetic algorithms, genetic programming, accuracy
                 evaluation criteria, complex nonlinear system
                 identification, crossover operator efficiency,
                 customized genetic operators, deterministic parameter
                 computation procedure, elitist multiobjective
                 optimization methodology, encapsulation mechanism,
                 fuzzy controller, multiobjective nonlinear systems
                 identification, parsimony evaluation criteria,
                 similarity analysis technique, universal approximator,
                 fuzzy control, identification, large-scale systems,
                 nonlinear control systems",
  DOI =          "doi:10.1109/IS.2010.5548346",
  notes =        "Also known as \cite{5548346}",
}

Genetic Programming entries for Alina Patelli Lavinia Ferariu

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