Discovery of Predictive Rule Sets for Chlorophyll-a Dynamics in the Nakdong River (Korea) by Means of the Hybrid Evolutionary Algorithm HEA

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@Article{Cao:2006:EI,
  author =       "Hongqing Cao and Friedrich Recknagel and 
                 Gea-Jae Joo and Dong-Kyun Kim",
  title =        "Discovery of Predictive Rule Sets for Chlorophyll-a
                 Dynamics in the Nakdong River (Korea) by Means of the
                 Hybrid Evolutionary Algorithm HEA",
  journal =      "Ecological Informatics",
  year =         "2006",
  volume =       "1",
  number =       "1",
  pages =        "43--53",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Hybrid
                 evolutionary algorithm, Rule sets, Chl.a, Sensitivity
                 analysis, Nakdong River",
  ISSN =         "1574-9541",
  DOI =          "doi:10.1016/j.ecoinf.2005.08.001",
  size =         "11 pages",
  abstract =     "We present a hybrid evolutionary algorithm (HEA) to
                 discover complex rule sets predicting the concentration
                 of chlorophyll-a (Chl.a) based on the measured
                 meteorological, hydrological and limnological variables
                 in the hypertrophic Nakdong River. The HEA is designed:
                 (1) to evolve the structure of rule sets by using
                 genetic programming and (2) to optimise the random
                 parameters in the rule sets by means of a genetic
                 algorithm. Time-series of input-output data from 1995
                 to 1998 without and with time lags up to 7 days were
                 used for training HEA. Independent input output data
                 for 1994 were used for testing HEA. HEA successfully
                 discovered rule sets for multiple nonlinear
                 relationships between physical, chemical variables and
                 Chl.a, which proved to be predictive for unseen data as
                 well as explanatory. The comparison of results by HEA
                 and previously applied recurrent artificial neural
                 networks to the same data with input--output time lags
                 of 3 days revealed similar good performances of both
                 methods. The sensitivity analysis for the best
                 performing predictive rule set revealed relationships
                 between seasons, specific input variables and Chl.a
                 which to some degree correspond with known properties
                 of the Nakdong River. The statistics of numerous random
                 runs of the HEA also allowed determining most relevant
                 input variables without a priori knowledge.",
  notes =        "http://www.elsevier.com/wps/find/journaldescription.cws_home/705192/description#description",
}

Genetic Programming entries for Hong-Qing Cao Friedrich Recknagel Gea-Jae Joo Dong-Kyun Kim

Citations