Evolutionary Computation for the Automated Design of Category Functions for Fuzzy ART: An Initial Exploration

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Islam Elnabarawy and Daniel R. Tauritz and 
                 Donald C. Wunsch",
  title =        "Evolutionary Computation for the Automated Design of
                 Category Functions for Fuzzy {ART}: An Initial
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1133--1140",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3082056",
  DOI =          "doi:10.1145/3067695.3082056",
  acmid =        "3082056",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, adaptive
                 resonance theory, adjusted rand index, clustering,
                 evolutionary computing, hyper-heuristics, unsupervised
  month =        "15-19 " # jul,
  abstract =     "Fuzzy Adaptive Resonance Theory (ART) is a classic
                 unsupervised learning algorithm. Its performance on a
                 particular clustering problem is sensitive to the
                 suitability of the category function for said problem.
                 However, classic Fuzzy ART employs a fixed category
                 function and thus is unable to benefit from the
                 potential to adjust its category function. This paper
                 presents an exploration into employing evolutionary
                 computation for the automated design of category
                 functions to obtain significantly enhanced Fuzzy ART
                 performance through tailoring to specific problem
                 classes. We employ a genetic programming powered
                 hyper-heuristic approach where the category functions
                 are constructed from a set of primitives constituting
                 those of the original Fuzzy ART category function as
                 well as additional hand-selected primitives. Results
                 are presented for a set of experiments on benchmark
                 classification tasks from the UCI Machine Learning
                 Repository demonstrating that tailoring Fuzzy ART's
                 category function can achieve statistically significant
                 superior performance on the testing datasets in
                 stratified 10-fold cross-validation procedures. We
                 conclude with discussing the results and placing them
                 in the context of being a first step towards automating
                 the design of entirely new forms of ART.",
  notes =        "Also known as
                 \cite{Elnabarawy:2017:ECA:3067695.3082056} GECCO-2017 A
                 Recombination of the 26th International Conference on
                 Genetic Algorithms (ICGA-2017) and the 22nd Annual
                 Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Islam Elnabarawy Daniel R Tauritz Donald C Wunsch II