Migration-based multiobjective genetic programming for nonlinear system identification

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "L. Ferariu and A. Patelli",
  title =        "Migration-based multiobjective genetic programming for
                 nonlinear system identification",
  booktitle =    "5th International Symposium on Applied Computational
                 Intelligence and Informatics, SACI '09",
  year =         "2009",
  month =        may,
  pages =        "475--480",
  keywords =     "genetic algorithms, genetic programming, QR
                 decomposition, adaptive threshold, convergence speed,
                 dominance analysis, flexible model structure selection,
                 migration-based multiobjective genetic programming,
                 nonlinear system identification, optimization
                 algorithm, quasi independent subpopulation, tree
                 encoding, identification, nonlinear control systems,
                 trees (mathematics)",
  DOI =          "doi:10.1109/SACI.2009.5136295",
  abstract =     "Nonlinear system identification is addressed by means
                 of genetic programming. For a flexible selection of
                 model structure and parameters, a multiobjective
                 optimization of the tree encoded individuals is carried
                 out, in terms of accuracy and parsimony. The paper
                 suggests a new optimization algorithm based on the
                 evolvement of two quasi-independent subpopulations,
                 which makes use of a flexible migration scheme with
                 adaptive thresholds and multiple rates. By efficiently
                 exploiting the concept of dominance analysis, the
                 algorithm is able to select compact and accurate
                 models, with good generalization capabilities. The
                 approach is compliant with nonlinear models, linear in
                 parameters. That permits the hybridization with QR
                 decomposition and the use of enhanced genetic
                 operators, aimed to increase the algorithm convergence
                 speed. The performances of the suggested design
                 procedure are illustrated by the identification of two
                 nonlinear industrial subsystems.",
  notes =        "Also known as \cite{5136295}",

Genetic Programming entries for Lavinia Ferariu Alina Patelli