System identification using structured genetic algorithms

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

@InCollection{iba:1997:HECb,
  author =       "Hitoshi Iba",
  title =        "System identification using structured genetic
                 algorithms",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and David B. Fogel and 
                 Zbigniew Michalewicz",
  chapter =      "section G1.4",
  keywords =     "genetic algorithms, genetic programming, stroganoff,
                 gmdh, sgpc version 1.1",
  ISBN =         "0-7503-0392-1",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
  DOI =          "doi:10.1201/9781420050387.ptg",
  size =         "11 pages",
  abstract =     "This case study describes a new approach to system
                 identification problems based on genetic programming
                 (GP), and presents an adaptive system called STROGANOFF
                 (structured representation on genetic algorithms for
                 nonlinear function fitting). STROGANOFF integrates an
                 adaptive search and a statistical method called group
                 method of data handling (GMDH). More precisely,
                 STROGANOFF consists of two processes: (i) the evolution
                 of structured representations using a traditional
                 genetic algorithm and (ii) the fitting of parameters of
                 the nodes with a multiple-regression analysis. The
                 fitness evaluation is based on a
                 minimum-description-length (MDL) criterion. Our
                 approach builds a bridge from traditional GP to a more
                 powerful search strategy. In other words, we introduce
                 a new approach to GP, by supplementing it with a local
                 hill climbing. The approach is successfully applied to
                 a time-series prediction.",
}

Genetic Programming entries for Hitoshi Iba

Citations