Genetic Programming of Polynomial Harmonic Models using the Discrete Fourier Transform

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

  author =       "Nikolay Nikolaev and Hitoshi Iba",
  title =        "Genetic Programming of Polynomial Harmonic Models
                 using the Discrete Fourier Transform",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "267--274",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 regularization, time series prediction, STROGANOFF,
                 GMDH network, discrete Fourier transform, hybrid
                 tree-structured network representation, irregular
                 frequencies, overfitting avoidance, polynomial function
                 nodes, polynomial harmonic, polynomial harmonic models,
                 regularized statistical fitness function, search
                 control, terminal harmonics, discrete Fourier
                 transforms, polynomials",
  ISBN =         "0-7803-6658-1",
  DOI =          "doi:10.1109/CEC.2001.934400",
  size =         "8 pages",
  abstract =     "This paper presents a Genetic Programming (GP) system
                 that evolves polynomial harmonic networks. The hybrid
                 tree-structured network representation suggests that
                 terminal harmonics with non-multiple frequencies may
                 enter polynomial function nodes as variables. The
                 harmonics with non-multiple, irregular frequencies are
                 derived analytically using the discrete Fourier
                 transform. The development of polynomial harmonic GP
                 includes also design of a regularised statistical
                 fitness function for improved search control and
                 overfitting avoidance. Empirical results show that this
                 hybrid version outperforms the previous GP system
                 manipulating polynomials STROGANOFF, the traditional
                 Koza-style GP, and the harmonic GMDH network algorithm
                 on processing time series",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 overfitting avoidance. polynomial harmonic GP. Tested
                 on Mackey-Glass, Sunspots, Yen-Dollar exchange rate
                 prediction, time lags Complexity penalty, Akaike
                 fitness multipled by (N+M)/(N-M)


Genetic Programming entries for Nikolay Nikolaev Hitoshi Iba