Adaptive genetic programming applied to classification in data mining

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Al-Madi:2012:NaBIC,
  author =       "N. Al-Madi and S. A. Ludwig",
  booktitle =    "Nature and Biologically Inspired Computing (NaBIC),
                 2012 Fourth World Congress on",
  title =        "Adaptive genetic programming applied to classification
                 in data mining",
  year =         "2012",
  pages =        "79--85",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 pattern classification, adaptive GP, adaptive genetic
                 programming, classification accuracies, crossover
                 rates, data mining, mutation rates, Accuracy,
                 Evolutionary computation, Sociology, Standards,
                 Statistics, Adaptive Genetic Programming,
                 Classification, Evolutionary Computation",
  DOI =          "doi:10.1109/NaBIC.2012.6402243",
  abstract =     "Classification is a data mining method that assigns
                 items in a collection to target classes with the goal
                 to accurately predict the target class for each item in
                 the data. Genetic programming (GP) is one of the
                 effective evolutionary computation techniques to solve
                 classification problems, however, it suffers from a
                 long run time. In addition, there are many parameters
                 that need to be set before the GP is run. In this
                 paper, we propose an adaptive GP that automatically
                 determines the best parameters of a run, and executes
                 the classification faster than standard GP. This
                 adaptive GP has three variations. The first variant
                 consists of an adaptive selection process ensuring that
                 the produced solutions in the next generation are
                 better than the solutions in the previous generation.
                 The second variant adapts the crossover and mutation
                 rates by modifying the probabilities ensuring that a
                 solution with a high fitness is protected. And the
                 third variant is an adaptive function list that
                 automatically changes the functions used by deleting
                 the functions that do not favourably contribute to the
                 classification. These proposed variations were
                 implemented and compared to the standard GP. The
                 results show that a significant speedup can be achieved
                 by obtaining similar classification accuracies.",
  notes =        "Also known as \cite{6402243}",
}

Genetic Programming entries for Nailah Al-Madi Simone A Ludwig

Citations