GFAM: A genetic algorithm optimization of Fuzzy ARTMAP

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2006, Pages 315-322

  Al Daraiseh, A ^a   Georgiopoulos, M ^a   Anagnostopoulos, G ^b   Wu, A S ^a   Mollaghasemi, M ^a  

^a University of Central Florida   (United States)

^b Florida Institute of Technology ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; GENETIC ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING;

CATEGORY PROLIFERATION PROBLEM; FUZZY ARTMAP (FAM); GENETIC ALGORITHM OPTIMIZATION;

FUZZY SETS;

EID: 34250753758     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2006.1681731     Document Type: Conference Paper

References (9)

1. Anagnostopoulos, G.C., Bharadwaj, M., Georgiopoulos, M., Verzi, S.J., & Heileman, G. L. (2003). Exemplar-based pattern recognition via semi-supervised learning, in Proc. of the International Joint Conference on Neural Networks, Vol. 4, pp. 2782-2787, Portland, Oregon, July 20-24.
2. Carpenter, G.A., Grossberg, S., Markuzon, N., & Reynolds, J.H. (1992). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multi-dimensional maps, IEEE Trans. Neural Networks, 3 (5), 698-713.
3. Goldberg, D. E. (1989). Genetic Algorithms in search, optimization, and Machine Learning. Addison-Wesley, Reading, MA.
4. Gomez-Sanchez, E., Dimitriadis, Y.A., Cano-Izquierdo, J.M., & Lopez-Coronado, J. (2001). Safe-μARTMAP: a new solution for reducing category proliferation in Fuzzy ARTMAP, in Proc. of the IEEE International Joint Conference on Neural Networks, Vol. 2, pp. 1197-1202, July 15-19.
5. Grossberg, S. (1976). Adaptive pattern recognition and universal receding II: Feedback, expectation, olfaction, and illusions, Biological Cybernetics, 23, 187-202.
6. Neuman, D.J., Hettich, S., Blake, C.L., & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/ MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
7. Parrado-Hemandez E., Gomez-Sanchez, E., & Dimitriadis, Y.A. (2003). Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP-based neural systems, Neural Networks, (16), 1039-1057.
8. Verzi, S.J., Georgiopoulos, M., Heileman, G.L., & Healy, M. (2001). Rademacher penalization applied to Fuzzy ARTMAP and Boosted ARTMAP, in Proc. of the IEEE-INNS International Joint Conference on Neural Network, pp. 1191-1196, Washington, DC, July 14-19.
9. Yao, X. (1999). Evolving artificial neural networks. Proceedings of the IEEE, Volume 87, Issue 9, Page(s):1423-1447