Multi Resolution Genetic Programming Approach for Stream Flow Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/semcco/MaheswaranK11,
  author =       "Rathinasamy Maheswaran and Rakesh Khosa",
  title =        "Multi Resolution Genetic Programming Approach for
                 Stream Flow Forecasting",
  booktitle =    "Proceedings of the Second International Conference
                 Swarm, Evolutionary, and Memetic Computing (SEMCCO
                 2011) Part {I}",
  year =         "2011",
  editor =       "Bijaya K. Panigrahi and 
                 Ponnuthurai Nagaratnam Suganthan and Swagatam Das and 
                 Suresh Chandra Satapathy",
  volume =       "7076",
  series =       "Lecture Notes in Computer Science",
  pages =        "714--722",
  address =      "Visakhapatnam, Andhra Pradesh, India",
  month =        dec # " 19-21",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, wavelet
                 analysis, multiscale forecasting, water stream flow",
  isbn13 =       "978-3-642-27171-7",
  DOI =          "doi:10.1007/978-3-642-27172-4_84",
  size =         "9 pages",
  abstract =     "Genetic Programming (GP) is increasingly used as an
                 alternative for Artificial Neural Networks (ANN) in
                 many applications viz. forecasting, classification etc.
                 However, GP models are limited in scope as their
                 application is restricted to stationary systems. This
                 study proposes use of Multi Resolution Genetic
                 Programming (MRGP) based approach as an alternative
                 modelling strategy to treat non-stationaries. The
                 proposed approach is a synthesis of Wavelets based
                 Multi-Resolution Decomposition and Genetic Programming.
                 Wavelet transform is used to decompose the time series
                 at different scales of resolution so that the
                 underlying temporal structures of the original time
                 series become more tractable. Further, Genetic
                 Programming is then applied to capture the underlying
                 process through evolutionary algorithms. In the case
                 study investigated, the MRGP is applied for forecasting
                 one month ahead stream flow in Fraser River, Canada,
                 and its performance compared with the conventional, but
                 scale insensitive, GP model. The results show the MRGP
                 as a promising approach for flow forecasting.",
  notes =        "Fraser river",
  affiliation =  "Department of Civil Engineering, Indian Institute of
                 Technology Delhi, Hauz Khas, New Delhi, 110016 India",
  bibdate =      "2011-12-14",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/semcco/semcco2011-1.html#MaheswaranK11",
}

Genetic Programming entries for Rathinasamy Maheswaran Rakesh Khosa

Citations