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Article
Evolutionary Computation for the Automated Design of Category Functions for Fuzzy Art: An Initial Exploration
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2017, Berlin, Germany)
  • Islam Elnabarawy
  • Daniel R. Tauritz, Missouri University of Science and Technology
  • Donald C. Wunsch, Missouri University of Science and Technology
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.

Meeting Name
Genetic and Evolutionary Computation Conference, GECCO 2017 (2017: Jul. 15-19, Berlin, Germany)
Department(s)
Computer Science
Second Department
Electrical and Computer Engineering
Keywords and Phrases
  • Adaptive resonance theory,
  • Adjusted rand index,
  • Clustering,
  • Evolutionary computing,
  • Genetic programming,
  • Hyper-heuristics,
  • Unsupervised learning
International Standard Book Number (ISBN)
978-145034939-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
7-1-2017
Citation Information
Islam Elnabarawy, Daniel R. Tauritz and Donald C. Wunsch. "Evolutionary Computation for the Automated Design of Category Functions for Fuzzy Art: An Initial Exploration" Proceedings of the Genetic and Evolutionary Computation Conference Companion (2017, Berlin, Germany) (2017) p. 1133 - 1140
Available at: http://works.bepress.com/daniel-tauritz/70/