Modeling global temperature changes with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Stanislawska2012,
  author =       "Karolina Stanislawska and Krzysztof Krawiec and 
                 Zbigniew W. Kundzewicz",
  title =        "Modeling global temperature changes with genetic
                 programming",
  journal =      "Computer \& Mathematics with Applications",
  year =         "2012",
  volume =       "64",
  number =       "12",
  pages =        "3717--3728",
  ISSN =         "0898-1221",
  DOI =          "doi:10.1016/j.camwa.2012.02.049",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0898122112001745",
  keywords =     "genetic algorithms, genetic programming, Data-driven
                 modelling, Unconstrained optimisation, Evolutionary
                 computation, Global temperature modelling",
  abstract =     "We use genetic programming (GP), a variant of
                 evolutionary computation, to build interpretable models
                 of global mean temperature as a function of natural and
                 anthropogenic forcings. In contrast to the conventional
                 approach, which engages models that are
                 physically-based but very data-demanding and
                 computation-intense, the proposed method is a
                 data-driven randomised search algorithm capable of
                 inducing a model from moderate amount of training data
                 at reasonable computational cost. GP maintains a
                 population of models and recombines them iteratively to
                 improve their performance meant as an ability to
                 explain the training data. Each model is a multiple
                 input-single output arithmetic expression built of a
                 predefined set of elementary components. Inputs include
                 external climate forcings, such as solar activity,
                 volcanic eruptions, composition of the atmosphere
                 (greenhouse gas concentration and aerosols), and
                 indices of internal variability (oscillations in the
                 Ocean-Atmosphere system), while the output is the
                 large-scale temperature. We used the data from the
                 period 1900-1999 for training and the period 2000-2009
                 for testing, and employed two quality measures: mean
                 absolute error and correlation coefficient. The
                 experiment showed that the models evolved by GP are
                 capable to predict, based exclusively on
                 non-temperature data, the global temperature more
                 accurately than a reference approach known in the
                 literature.",
}

Genetic Programming entries for Karolina Stanislawska Krzysztof Krawiec Zbigniew W Kundzewicz

Citations