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Article
Nested Monte Carlo Search Expression Discovery for the Automated Design of Fuzzy ART Category Choice Functions
GECCO 2019 Companion -- Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
  • Marketa Illetskova
  • Islam Elnabarawy
  • Leonardo Enzo Brito Da Silva
  • Daniel R. Tauritz, Missouri University of Science and Technology
  • Donald C. Wunsch, Missouri University of Science and Technology
Abstract

While the performance of many neural network and machine learning schemes has been improved through the automated design of various components of their architectures, the automated improvement of Adaptive Resonance Theory (ART) neural networks remains relatively unexplored. Recent work introduced a genetic programming (GP) approach to improve the performance of the Fuzzy ART neural network employing a hyper-heuristic approach to tailor Fuzzy ART's category choice function to specific problems. The GP method showed promising results. However, GP is not the only tool that can be used for automatic improvement. Among other methods, Nested Monte Carlo Search (NMCS) was recently applied to expression discovery and outperformed traditional evolutionary approaches by finding better solutions in fewer evaluations. This work applies NMCS to the discovery of new Fuzzy ART category choice functions targeted to specific problems with results demonstrating its ability to find better performing Fuzzy ART networks than the GP approach.

Meeting Name
2019 Genetic and Evolutionary Computation Conference, GECCO 2019 (2019: Jul. 13-17, Prague, Czech Republic)
Department(s)
Computer Science
Keywords and Phrases
  • Algorithm engineering,
  • Empirical study,
  • Genetic programming,
  • Metaheuristics,
  • Neural networks
International Standard Book Number (ISBN)
978-145036748-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
7-1-2019
Disciplines
Citation Information
Marketa Illetskova, Islam Elnabarawy, Leonardo Enzo Brito Da Silva, Daniel R. Tauritz, et al.. "Nested Monte Carlo Search Expression Discovery for the Automated Design of Fuzzy ART Category Choice Functions" GECCO 2019 Companion -- Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (2019) p. 171 - 172
Available at: http://works.bepress.com/daniel-tauritz/77/