Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication

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@InCollection{Cao:2006:2lakes,
  author =       "Hongqing Cao and Friedrich Recknagel and 
                 Bomchul Kim and Noriko Takamura",
  title =        "Hybrid Evolutionary Algorithm for Rule Set Discovery
                 in Time-Series Data to Forecast and Explain Algal
                 Population Dynamics in Two Lakes Different in
                 Morphometry and Eutrophication",
  booktitle =    "Ecological Informatics: Scope, Techniques and
                 Applications",
  publisher =    "Springer-Verlag",
  year =         "2006",
  editor =       "Friedrich Recknagel",
  chapter =      "17",
  pages =        "347--367",
  address =      "Berlin, Heidelberg, New York",
  edition =      "2nd",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-28383-8",
  DOI =          "doi:10.1007/3-540-28426-5_17",
  abstract =     "A hybrid evolutionary algorithm (HEA) has been
                 developed to discover predictive rule sets in complex
                 ecological data. It has been designed to evolve the
                 structure of rule sets by using genetic programming and
                 to optimise the random parameters in the rule sets by
                 means of a genetic algorithm.

                 HEA was successfully applied to long-term monitoring
                 data of the shallow, eutrophic Lake Kasumigaura (Japan)
                 and the deep, mesotrophic Lake Soyang (Korea). The
                 results have demonstrated that HEA is able to discover
                 rule sets, which can forecast for 7-days-ahead seasonal
                 abundances of blue-green algae and diatom populations
                 in the two lakes with relatively high accuracy but are
                 also explanatory for relationships between physical,
                 chemical variables and the abundances of algal
                 populations. The explanations and the sensitivity
                 analysis for the best rule sets correspond well with
                 theoretical hypotheses and experimental findings in
                 previous studies.",
  notes =        "http://www.springer.com/sgw/cda/frontpage/0,11855,5-10031-22-68637391-0,00.html",
}

Genetic Programming entries for Hong-Qing Cao Friedrich Recknagel Bomchul Kim Noriko Takamura

Citations