Comparative Application of Artificial Neural Networks and Genetic Algorithms for Multivariate Time Series Modelling of Algal Blooms in Freshwater Lakes

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

@Article{recknagelGP1,
  author =       "Friedrich Recknagel and Jason Bobbin and 
                 Peter A. Whigham and Hugh Wilson",
  title =        "Comparative Application of Artificial Neural Networks
                 and Genetic Algorithms for Multivariate Time Series
                 Modelling of Algal Blooms in Freshwater Lakes",
  journal =      "Journal of Hydroinformatics",
  year =         "2002",
  volume =       "4",
  number =       "2",
  pages =        "125--133",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Ecology,
                 chlorophyll-a, Microcystis, short-term prediction,
                 artificial neural network model, genetic algorithm
                 model, rule sets, difference equations",
  ISSN =         "1464-7141",
  URL =          "http://jh.iwaponline.com/content/4/2/125",
  URL =          "http://www.iwaponline.com/jh/004/0125/0040125.pdf",
  size =         "9 pages",
  abstract =     "The paper compares potentials and achievements of
                 artificial neural networks and genetic algorithms in
                 terms of forecasting and understanding of algal blooms
                 in Lake Kasumigaura (Japan). Despite the complex and
                 nonlinear nature of ecological data, artificial neural
                 networks allow seven-days-ahead predictions of timing
                 and magnitudes of algal blooms with reasonable
                 accuracy. Genetic algorithms possess the capability to
                 evolve, refine and hybridize numerical and linguistic
                 models. Examples presented in the paper show that
                 models explicitly synthesized by genetic algorithms not
                 only perform better in seven-days-ahead predictions of
                 algal blooms than artificial neural network models, but
                 provide more transparency for explanation as well.",
  notes =        "More GA than GP? GAMA

                 Department of Soil and Water, University of Adelaide,
                 Glen Osmond, SA 5064, Australia

                 Department of Computer Science, University of Otago, PO
                 Box 56, Dunedin, New Zealand",
}

Genetic Programming entries for Friedrich Recknagel Jason Bobbin Peter Alexander Whigham Hugh Wilson

Citations