GP-Based Modeling Method for Time Series Prediction with Parameter Optimization and Node Alternation

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

@InProceedings{yoshihara:2000:gmmtsppona,
  author =       "I. Yoshihara and T. Aoyama and M. Yasunaga",
  title =        "GP-Based Modeling Method for Time Series Prediction
                 with Parameter Optimization and Node Alternation",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1475--1481",
  volume =       "2",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series,
                 GP based model building, GP based modelling method,
                 backpropagation-like algorithm, complicated functions,
                 fast method, functional forms, model parameters,
                 mutation, node alternation, parameter optimisation,
                 seismic ground motion, time series prediction,
                 backpropagation, parameter estimation, statistical
                 analysis",
  ISBN =         "0-7803-6375-2",
  DOI =          "doi:10.1109/CEC.2000.870828",
  abstract =     "A fast method of GP based model building for time
                 series prediction is proposed. The method involves two
                 newly-devised techniques. One is regarding
                 determination of model parameters: only functional
                 forms are inherited from their parents with genetic
                 programming, but model parameters are not inherited.
                 They are optimised by a backpropagation-like algorithm
                 when a child (model) is newborn. The other is regarding
                 mutation: nodes which require a different number of
                 edges, can be transformed into different types of nodes
                 through mutation. This operation is effective at
                 accelerating complicated functions e.g. seismic ground
                 motion. The method has been applied to a typical
                 benchmark of time series and many real world problems",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

Genetic Programming entries for Ikuo Yoshihara T Aoyama M Yasunaga

Citations