Nested monte carlo search expression discovery for the automated design of fuzzy ART category choice functions
| Autor: | Leonardo Enzo Brito da Silva, Donald C. Wunsch, Daniel R. Tauritz, Marketa Illetskova, Islam Elnabarawy |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Monte Carlo method Algorithm engineering Genetic programming 0102 computer and information sciences 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Fuzzy logic Expression (mathematics) Adaptive resonance theory 010201 computation theory & mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Metaheuristic computer |
| Zdroj: | GECCO (Companion) |
| DOI: | 10.1145/3319619.3322050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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