Comparison of a Genetic Algorithm to Grammatical Evolution for Automated Design of Genetic Programming Classification Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@Article{Nyathi2018EWSA,
  author =       "Thambo Nyathi and Nelishia Pillay",
  title =        "Comparison of a Genetic Algorithm to Grammatical
                 Evolution for Automated Design of Genetic Programming
                 Classification Algorithms",
  journal =      "Expert System with Applications",
  year =         "2018",
  volume =       "104",
  pages =        "213--234",
  month =        "15 " # aug,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  DOI =          "doi:10.1016/j.eswa.2018.03.030",
  abstract =     "Genetic Programming (GP) is gaining increased
                 attention as an effective method for inducing
                 classifiers for data classification. However, the
                 manual design of a genetic programming classification
                 algorithm is a non-trivial time consuming process. This
                 research investigates the hypothesis that automating
                 the design of a GP classification algorithm for data
                 classification can still lead to the induction of
                 effective classifiers and also reduce the design time.
                 Two evolutionary algorithms, namely, a genetic
                 algorithm (GA) and grammatical evolution (GE) are used
                 to automate the design of GP classification algorithms.
                 The classification performance of the automated
                 designed GP classifiers i.e. GA designed GP classifiers
                 and GE designed GP classifiers are compared to each
                 other and to manually designed GP classifiers on
                 real-world problems. Furthermore, a comparison of the
                 design times of automated design and manual design is
                 also carried out for the same set of problems. The
                 automated designed classifiers were found to outperform
                 manually designed classifiers across problem domains.
                 Automated design time is also found to be less than
                 manual design time. This study revealed that for the
                 considered datasets GE performs better for binary
                 classification while the GA does better for multiclass
                 classification. Overall the results of the study are in
                 support of the hypothesis.",
}

Genetic Programming entries for Thambo Nyathi Nelishia Pillay

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