International Journal of Soft Computing
Year:
2009
Volume:
4
Issue:
2
Page No.
95 - 102
Evolving Connection Weights of Artificial Neural Networks Using Genetic Algorithm with Application to the Prediction of Stroke Disease
Authors :
D. Shanthi ,
G. Sahoo
and
N. Saravanan
References
Adeli, H. and S. Hung, 1995. Machine Learning: Neural Networks, Genetic Algorithms and Fuzzy Systems. John Wiley and Sons Inc., New York, ISBN: 0-471-01633-0, pp: 211.
Baxt, W.G., 1995. Application of artificial neural networks to clinical medicine. Lancet, 346: 1135-1138.
CrossRef | Davis, L., 1991. Handbook of Genetic Algorithms. 1st Edn., Van Nostrand Reinhold, New York, USA., ISBN-13: 9780442001735, Pages: 385.
Goldberg, D.E., 1989. Genetic Algorithms in Search optimization and Machine Learning. Addison Wesley and Longman Publishing Co., Inc., Boston, MA., USA., ISBN: 0201157675, pp: 372.
Janson, D.J. and J.F. Frenzel, 1993. Training product unit neural networks with genetic algorithms. IEEE Expert, 8: 26-33.
CrossRef | Kim, K. and I. Han, 1999. Simultaneous optimization method of feature transformation and weighting for artificial neural networks using genetic algorithm: Kpplication to Korean stock market. Proc. Korea Intel. Inform. Syst. Soc. Conf., 11: 323-335.
Direct Link | Kumanan, V., S.K. Nanne Saheb and C.P. Jesuthanam, 2006. Prediction of machining forces using neural networks trained by a genetic algorithm. J. Inst. Eng. India Part Pe Prod. Eng. Division, 87: 11-15.
Direct Link | Mohr, J.P., 2001. Stroke Analysis. 4th Edn., Oxford Press, New York, USA.
Montana, D. and L. Davis, 1989. Training feedforward neural networks using genetic algorithms. Proceeding of the 11th International Conference Artificial Intelligence, (ICAI'89), Morgan Kaufmann, pp: 762-767.
Punitha, A., C.P. Sumathi and T. Santhanam, 2007. A combination of genetic algorithm and ART neural network for breast cancer diagnosis. Asian J. Inform. Technol., 6: 112-117.
Rumelhart, D.E., G.E. Hinton and R.J. Williams, 1986. Learning representations by back-propagating errors. Nature, 323: 533-536.
CrossRef | Sexton, R.S., B. Alidaee, R.E. Dorsey and J.D. Johnson, 1998. Global optimization for artificial neural networks: A tabu search application. Eur. J. Operat. Res., 106: 570-584.
Direct Link | Sexton, R.S., R.E. Dorsey and J.D. Johnson, 1998. Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation. Decision Support Syst., 22: 171-185.
CrossRef | Sexton, R.S., R.E. Dorsey and J.D. Johnson, 1999. Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. Eur. J. Operat. Res., 114: 589-601.
CrossRef | Shanthi, D., G. Sahoo and N. Saravanan, 2008. Input feature selection using hybrid neuro-genetic approach in the diagnosis of stroke disease. Int. J. Comput. Sci. Network Security, 8: 99-107.
Sit, C.W., 2005. Application of artificial neural network-genetic algorithm in inferential estimation and control of a distillation column. M.Sc. Thesis, Universiti Teknologi Malaysia.
Vijayan, S., 2008. Several Approaches to Variable Selection by Means of Genetic Algorithms. Intelligent Information Technologies: Concepts, Methodologies, Tools and Applications. IGI Publishing, USA.
Wang, J., 2006. Encyclopedia of Data Warehousing and Mining. Vol. 2, Miscrosoft Research Asia, China.
Wieland, 1991. Evolving neural network controllers for unstable systems. Proceedings of the International Joint Conference, (IJC'91), Seattle, WA., pp: 667-673.
Yao, X. and Y. Liu, 1997. A new evolutionary system for evolving artificial neural networks. IEEE. Trans. Neural Networks, 8: 694-713.