Multi Resolution Genetic Programming Approach for Stream Flow Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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

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