Nested Monte Carlo Search Expression Discovery for the Automated Design of Fuzzy ART Category Choice Functions
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.
M. Illetskova 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, pp. 171-172, Association for Computing Machinery (ACM), Jul 2019.
The definitive version is available at https://doi.org/10.1145/3319619.3322050
2019 Genetic and Evolutionary Computation Conference, GECCO 2019 (2019: Jul. 13-17, Prague, Czech Republic)
Keywords and Phrases
Algorithm engineering; Empirical study; Genetic programming; Metaheuristics; Neural networks
International Standard Book Number (ISBN)
Article - Conference proceedings
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