Modeling Genetic Network by Hybrid GP

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

  author =       "Shin Ando and Hitoshi Iba and Erina Sakamoto",
  title =        "Modeling Genetic Network by Hybrid GP",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and 
                 Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and 
                 Mark Shackleton",
  pages =        "291--296",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  month =        "12-17 " # may,
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, artificial
                 data, differential equations, evolutionary modelling
                 method, genetic regulatory network modeling, hybrid
                 algorithm, hybrid genetic programming, least mean
                 square method, multiple runs, real world data,
                 regulation, statistical analysis, time series,
                 differential equations, least mean squares methods,
                 statistical analysis",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2002.1006249",
  abstract =     "We present an Evolutionary Modelling method for
                 modeling genetic regulatory networks. The method
                 features hybrid algorithm of Genetic Programming with
                 statistical analysis to derive systems of differential
                 equations. Genetic Programming and Least Mean Square
                 method were combined to identify a concise form of
                 regulation between the variables from a given set of
                 time series. Also, results of multiple runs were
                 statistically analysed to indicate the term with robust
                 and significant influence. Our approach was evaluated
                 in artificial data and real world data.",
  notes =        "oai:CiteSeerPSU:520794",
  size =         "6 pages",

Genetic Programming entries for Shin Ando Hitoshi Iba Erina Sakamoto