An improved multi-expression programming algorithm applied in function discovery and data prediction

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@Article{Zhang:2013:IJICT,
  title =        "An improved multi-expression programming algorithm
                 applied in function discovery and data prediction",
  author =       "Qingke Zhang and Bo Yang and Lin Wang and 
                 Jianzhang Jiang",
  journal =      "International Journal of Information and Communication
                 Technology",
  year =         "2013",
  month =        dec # "~19",
  volume =       "5",
  number =       "3/4",
  pages =        "218--233",
  keywords =     "genetic algorithms, genetic programming,
                 multi-expression programming, MEP, double-layer
                 chromosome, prediction modelling, function discovery,
                 data prediction, soft computing, cement strength
                 prediction.",
  publisher =    "Inderscience Publishers",
  language =     "eng",
  ISSN =         "1741-8070",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  URL =          "http://www.inderscience.com/link.php?id=54952",
  DOI =          "DOI:10.1504/IJICT.2013.054952",
  abstract =     "This paper presents an improved multi-expression
                 programming (MEP). In the algorithm, each individual is
                 encoded as a double-layer structure, and two-dimension
                 space operators are introduced through two-dimension
                 crossover and mutation. The problems of symbolic
                 expression are defined and used as benchmarks to
                 compare the effectiveness of proposal method against
                 the baseline single-layer MEP. Experiments showed that
                 our method using two-dimensional super chromosome can
                 find the optimal solution in a short time with small
                 population. Then the improved algorithm is applied to
                 the prediction of 28-day cement compressive strength.
                 Comparison with other three soft computing models,
                 namely MEP model, neural networks (NN) model and fuzzy
                 logic (FL) model on cement strength prediction revealed
                 that the improved MEP model has a lower rate in RMSE
                 and MAE. Test results demonstrate the proposed method
                 is efficient and performed better in function discovery
                 and data prediction.",
}

Genetic Programming entries for Qingke Zhang Bo Yang Lin Wang Jianzhang Jiang

Citations