Multiobjective Graph Genetic Programming with Encapsulation Applied to Neural System Identification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Lavinia Ferariu and Bogdan Burlacu",
  title =        "Multiobjective Graph Genetic Programming with
                 Encapsulation Applied to Neural System Identification",
  booktitle =    "15th International Conference on System Theory,
                 Control, and Computing (ICSTCC 2011)",
  year =         "2011",
  month =        "14-16 " # oct,
  address =      "Sinaia",
  keywords =     "genetic algorithms, genetic programming, Pareto
                 ranking, encapsulation operator, evolutionary
                 algorithm, feedforward hybrid neural network,
                 industrial plant, multiobjective graph genetic
                 programming, multiobjective optimisation, neural system
                 identification, nonlinear system identification, Pareto
                 optimisation, feedforward neural nets, graph theory",
  isbn13 =       "978-1-4577-1173-2",
  URL =          "",
  size =         "6 pages",
  abstract =     "This paper presents two new encapsulation operators
                 compatible with graph genetic programming. The approach
                 is used for the evolvement of partially interconnected,
                 feed-forward hybrid neural networks, within the
                 framework of nonlinear system identification. The
                 suggested encapsulations are targeted to protect
                 valuable terminals and useful sub-graphs directly
                 connected with the root node. To preserve a better
                 balance between exploitation and exploration, the
                 quality of the inner substructures is assessed in
                 relation with the phenotypic properties of the
                 individuals to whom they belong. The multiobjective
                 optimisation of accuracy and parsimony is adopted; for
                 each generation, the requirements expressed by the
                 decision block are progressively translated to the
                 evolutionary algorithm, via a preliminary clustering of
                 the individuals, performed before Pareto-ranking. The
                 experimental results achieved on the identification of
                 an industrial plant indicate that the proposed
                 encapsulations are able to enforce the selection of
                 accurate and simple models.",
  notes =        "Also known as \cite{6085706}",

Genetic Programming entries for Lavinia Ferariu Bogdan Burlacu