Effects of Selection Schemes in Genetic Programming for Time Series Prediction

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

@InProceedings{kim:1999:ESSGPTSP,
  author =       "Jung-Jib Kim and Byoung-Tak Zhang",
  title =        "Effects of Selection Schemes in Genetic Programming
                 for Time Series Prediction",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "252--258",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series,
                 breeder genetic algorithm, dynamics control,
                 evolutionary algorithms, evolutionary computation, hard
                 selection, laser time-series data, performance testing,
                 selection differential, selection operators, selection
                 schemes, soft selection, time series prediction,
                 variable-size representations, variable-size tree
                 representation, evolutionary computation",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://bi.snu.ac.kr/Publications/Conferences/International/CEC99_Kim.pdf",
  DOI =          "doi:10.1109/CEC.1999.781933",
  size =         "7 pages",
  abstract =     "The problem of time series prediction provides a
                 practical benchmark for testing the performance of
                 evolutionary algorithms. In this paper, we compare
                 various selection methods for genetic programming, an
                 evolutionary computation with variable-size tree
                 representations, with application to time series data.
                 Selection is an important operator that controls the
                 dynamics of evolutionary computation. A number of
                 selection operators have been so far proposed and
                 tested in evolutionary algorithms with fixed-size
                 chromosomes. However, the effect of selection schemes
                 remains relatively unexplored in evolutionary
                 algorithms with variable-size representations. We
                 analyse the evolutionary dynamics of genetic
                 programming by means of the selection to response and
                 the selection differential proposed in the breeder
                 genetic algorithm (BGA). The empirical analysis using
                 the laser time-series data suggests that hard selection
                 is more preferable than soft selection. This seems due
                 to the lack of heritability in genetic programming",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143

                 A Study on Effects of Selection Schemes in Genetic
                 Programming for Time Series Prediction, Jung-Jib Kim,
                 Master Thesis, Dept. of Computer Engineering, Seoul
                 National University, February 2000",
}

Genetic Programming entries for Jung-Jib Kim Byoung-Tak Zhang

Citations