Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Awuley:2016:CEC,
  author =       "Anthony Awuley and Brian J. Ross",
  title =        "Feature Selection and Classification Using Age Layered
                 Population Structure Genetic Programming",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "2417--2426",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744088",
  abstract =     "This paper presents a new algorithm called Feature
                 Selection Age Layered Population Structure (FSALPS) for
                 feature subset selection and classification of varied
                 supervised learning tasks. FSALPS is a modification of
                 Hornby's ALPS algorithm - an evolutionary algorithm
                 renown for avoiding pre-mature convergence on difficult
                 problems. FSALPS uses a novel frequency count system to
                 rank features in the GP population based on evolved
                 feature frequencies. The ranked features are translated
                 into probabilities, which are used to control
                 evolutionary processes such as terminal-symbol
                 selection for the construction of GP trees/sub-trees.
                 The FSALPS meta-heuristic continuously refines the
                 feature subset selection process whiles simultaneously
                 evolving efficient classifiers through a non-converging
                 evolutionary process that favours selection of features
                 with high discrimination of class labels. We compared
                 the performance of canonical GP, ALPS and FSALPS on
                 some high-dimensional benchmark classification
                 datasets, including a hyperspectral vision problem.
                 Although all algorithms had similar classification
                 accuracy, ALPS and FSALPS usually dominated canonical
                 GP in terms of smaller and efficient trees.
                 Furthermore, FSALPS significantly outperformed
                 canonical GP, ALPS, and other feature selection
                 strategies in the literature in its ability to perform
                 dimensionality reduction",
  notes =        "WCCI2016",
}

Genetic Programming entries for Anthony Awuley Brian J Ross

Citations