Multiobjective genetic programming with adaptive clustering

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Lavinia Ferariu and Bogdan Burlacu",
  title =        "Multiobjective genetic programming with adaptive
  booktitle =    "IEEE International Conference on Intelligent Computer
                 Communication and Processing (ICCP 2011)",
  year =         "2011",
  month =        "25-27 " # aug,
  pages =        "27--32",
  address =      "Cluj-Napoca, Romania",
  size =         "6 pages",
  abstract =     "This paper presents a new approach meant to provide an
                 automatic design of feed forward neural models by means
                 of multiobjective graph genetic programming. The
                 suggested algorithm can deal with partially
                 interconnected neural architectures and various types
                 of global and local neurons within each hidden neural
                 layer. It concomitantly ensures the reduction of
                 variables and the selection of convenient model
                 structures and parameters, by working on a set of
                 graph-based encrypted individuals built via genetic
                 programming with the guarantee of phenotypic and
                 genotypic validity. In order to provide a realistic
                 assessment of the neural models, the optimisation is
                 carried out subject to multiple objectives of different
                 priorities. In relation to this idea, the authors
                 propose a new Pareto-ranking strategy, which
                 progressively guides the search towards the preferred
                 zones of the exploration space. The fitness assignment
                 procedure monitors the phenotypic diversity of the best
                 individuals, as well as the convergence speed of the
                 algorithm, and exploits the resulted heuristics for
                 performing a preliminary clustering of individuals. The
                 experimental trials targeting the identification of an
                 industrial system show the capacity of the suggested
                 approach to automatically build simple and precise
                 models, whilst dealing with noisy data and scarce a
                 priori information.",
  keywords =     "genetic algorithms, genetic programming,
                 Pareto-ranking strategy, adaptive clustering, automatic
                 design, convergence speed, feedforward neural model,
                 genotypic validity, graph based encrypted individual,
                 hidden neural layer, industrial system, interconnected
                 neural architecture, model structure, multiobjective
                 graph genetic programming, noisy data, phenotypic
                 validity, cryptography, feedforward neural nets, graph
                 theory, pattern clustering",
  DOI =          "doi:10.1109/ICCP.2011.6047840",
  notes =        "Also known as \cite{6047840}",

Genetic Programming entries for Lavinia Ferariu Bogdan Burlacu