GRADIENT: Grammar-driven genetic programming framework for building multi-component, hierarchical predictive systems

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@Article{Tsakonas2012,
  author =       "Athanasios Tsakonas and Bogdan Gabrys",
  title =        "GRADIENT: Grammar-driven genetic programming framework
                 for building multi-component, hierarchical predictive
                 systems",
  journal =      "Expert Systems with Applications",
  volume =       "39",
  number =       "18",
  pages =        "13253--13266",
  year =         "2012",
  month =        "15 " # dec,
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2012.05.076",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417412007920",
  keywords =     "genetic algorithms, genetic programming, Multi-level
                 prediction systems, Ensemble systems, Function
                 approximation, Non-linear regression",
  abstract =     "This work presents the GRADIENT (GRAmmar-DrIven
                 ENsemble sysTem) framework for the generation of hybrid
                 multi-level predictors for function approximation and
                 regression analysis tasks. The proposed model uses a
                 context-free grammar guided genetic programming for the
                 automatic building of multi-component prediction
                 systems with hierarchical structures. A
                 multi-population evolutionary algorithm together with
                 resampling and cross-validatory approaches are used to
                 increase component models' diversity and facilitate
                 more robust and efficient search for accurate
                 solutions. The system has been tested on a number of
                 synthetic and publicly available real-world regression
                 and time series problems for a range of configurations
                 in order to identify and subsequently illustrate and
                 discuss its characteristics and performance. GRADIENT
                 has been shown to be very competitive and versatile
                 when compared to a number of state-of-the-art
                 prediction methods.",
}

Genetic Programming entries for Athanasios D Tsakonas Bogdan Gabrys

Citations