Multiobjective design of evolutionary hybrid neural networks

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

@InProceedings{Ferariu:2011:ICAC,
  author =       "Lavinia Ferariu and Bogdan Burlacu",
  title =        "Multiobjective design of evolutionary hybrid neural
                 networks",
  booktitle =    "17th International Conference on Automation and
                 Computing (ICAC 2011)",
  year =         "2011",
  month =        "10 " # sep,
  pages =        "195--200",
  address =      "Huddersfield, UK",
  keywords =     "genetic algorithms, genetic programming,
                 Pareto-ranking strategy, data-driven modelling,
                 evolutionary hybrid neural networks, industrial system,
                 interconnected structures, multiobjective design,
                 multiobjective graph genetic programming, Pareto
                 optimisation, data models, design, neural nets",
  isbn13 =       "978-1-4673-0000-1",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6084926",
  size =         "6 pages",
  abstract =     "The paper presents a new approach to data-driven
                 modelling. The models are flexibly configured in
                 compliance with the neural network formalism, by
                 accepting partially interconnected structures and
                 various types of global and local neurons within each
                 hidden neural layer. A simultaneous selection of
                 convenient model structure and parameters is performed,
                 making use of multiobjective graph genetic programming.
                 For an efficient assessment of individuals, the authors
                 suggest a new Pareto-ranking strategy, which permits a
                 progressive combination between search and decision,
                 tailored to handle objectives of different priorities.
                 The experiments carried out for the identification of
                 an industrial system show the capacity of the proposed
                 approach to automatically build simple and precise
                 models, whilst dealing with noisy data and poor
                 aprioric information.",
  notes =        "Also known as \cite{6084926}",
}

Genetic Programming entries for Lavinia Ferariu Bogdan Burlacu

Citations