Advances in data-driven analyses and modelling using EPR-MOGA

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@Article{Giustolisi:2009:JH,
  author =       "O. Giustolisi and D. A. Savic",
  title =        "Advances in data-driven analyses and modelling using
                 EPR-MOGA",
  journal =      "Journal of Hydroinformatics",
  year =         "2009",
  volume =       "11",
  number =       "3",
  pages =        "225--236",
  keywords =     "genetic algorithms, genetic programming, data-driven
                 modelling, evolutionary computing, groundwater
                 resources, multiobjective optimization, symbolic
                 regression",
  ISSN =         "1464-7141",
  URL =          "http://www.iwaponline.com/jh/011/0225/0110225.pdf",
  DOI =          "doi:10.2166/hydro.2009.017",
  size =         "12 pages",
  abstract =     "Evolutionary Polynomial Regression (EPR) is a recently
                 developed hybrid regression method that combines the
                 best features of conventional numerical regression
                 techniques with the genetic programming/symbolic
                 regression technique. The original version of EPR works
                 with formulae based on true or pseudo-polynomial
                 expressions using a single-objective genetic algorithm.
                 Therefore, to obtain a set of formulae with a variable
                 number of pseudo-polynomial coefficients, the
                 sequential search is performed in the formulae space.
                 This article presents an improved EPR strategy that
                 uses a multi-objective genetic algorithm instead. We
                 demonstrate that multi-objective approach is a more
                 feasible instrument for data analysis and model
                 selection. Moreover, we show that EPR can also allow
                 for simple uncertainty analysis (since it returns
                 polynomial structures that are linear with respect to
                 the estimated coefficients). The methodology is tested
                 and the results are reported in a case study relating
                 groundwater level predictions to total monthly
                 rainfall.",
  notes =        "Brindisi, multi objective, ANN",
}

Genetic Programming entries for Orazio Giustolisi Dragan Savic

Citations