Co-evolutionary multi-population genetic programming for classification in software defect prediction: An empirical case study

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@Article{Mausa:2017:SC,
  author =       "Goran Mausa and Tihana Galinac Grbac",
  title =        "Co-evolutionary multi-population genetic programming
                 for classification in software defect prediction: An
                 empirical case study",
  journal =      "Applied Soft Computing",
  year =         "2017",
  volume =       "55",
  pages =        "331--351",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Classification, Coevolution, Software defect
                 prediction",
  ISSN =         "1568-4946",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494617300650",
  DOI =          "doi:10.1016/j.asoc.2017.01.050",
  size =         "21 pages",
  abstract =     "Evolving diverse ensembles using genetic programming
                 has recently been proposed for classification problems
                 with unbalanced data. Population diversity is crucial
                 for evolving effective algorithms. Multilevel selection
                 strategies that involve additional colonization and
                 migration operations have shown better performance in
                 some applications. Therefore, in this paper, we are
                 interested in analysing the performance of evolving
                 diverse ensembles using genetic programming for
                 software defect prediction with unbalanced data by
                 using different selection strategies. We use
                 colonization and migration operators along with three
                 ensemble selection strategies for the multi-objective
                 evolutionary algorithm. We compare the performance of
                 the operators for software defect prediction datasets
                 with varying levels of data imbalance. Moreover, to
                 generalize the results, gain a broader view and
                 understand the underlying effects, we replicated the
                 same experiments on UCI datasets, which are often used
                 in the evolutionary computing community. The use of
                 multilevel selection strategies provides reliable
                 results with relatively fast convergence speeds and
                 outperforms the other evolutionary algorithms that are
                 often used in this research area and investigated in
                 this paper. This paper also presented a promising
                 ensemble strategy based on a simple convex hull
                 approach and at the same time it raised the question
                 whether ensemble strategy based on the whole population
                 should also be investigated.",
}

Genetic Programming entries for Goran Mausa Tihana Galinac Grbac

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