Random Articles
Reseach Article
Performance Analysis of Neural Networks Training using Real Coded Genetic Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 51 - Number 18 |
| Year of Publication: 2012 |
| Authors: Partha Pratim Sarangi, Banshidhar Majhi, Madhumita Panda |
10.5120/8143-1891
|
Partha Pratim Sarangi, Banshidhar Majhi, Madhumita Panda . Performance Analysis of Neural Networks Training using Real Coded Genetic Algorithm. International Journal of Computer Applications. 51, 18 ( August 2012), 30-36. DOI=10.5120/8143-1891
Abstract
Multilayer perceptrons (MLPs) are widely used for pattern classification and regression problems. Backpropagation (BP) algorithm is known technique in the training of multilayer perceptrons. However for its optimum training convergence, the learning and momentum parameters need to be tuned on trial and error method. Further, sometimes the backpropagation algorithm fails to achieve global convergence. To alleviate these problems we suggest a genetic algorithm based training for MLP network. Both binary coded and real coded genetic algorithm are used and a comparative training performance analysis has been studied. It is observed from simulation results that both the schemes outperform backpropagation algorithm and achieve global convergence. Further the real coded GA based training shows a faster convergence than binary coded GA based training. For simulation datasets are taken from UCI based machine learning repository. Hence real-coded genetic algorithm finds an alternative for back propagation based training algorithm.
References
- Pal S. K. and Bhandari D. 1994"Selection Of optimal set of weights in a layered network using genetic algorithm", Information Sciences, Vol: 80, Pages: 213-234.
- Yao X. 1991 "Evolving Artificial Neural Networks," in Preprints Int. Symp. AI, Reasoning & Creativity, Queensland, Australia,Griffith Univ.
- Bornholdt S. and D. Graudenz. 1992. "General asymptotic and neural networks and structure design by genetic algorithms," Neural Networks, vol. 5, no. , pp. 327-334.
- Muehlenbein H. 1990. "Limitations of Multi-layer Perceptron Networks - Steps Towards Genetic Neural Networks", Parallel Computing, Vol. 14, pp. 249-260.
- Whiteley. D. T. Starkweather & C. Bogart, 1990. "Genetic algorithms and neural networks: optimizing connections and connectivity," Parallel Computing, Vol. 4, pp. 374-361.
- Gupta, J. N. D. , & Sexton, R. S. Comparing backpropagation with a genetic algorithm for neural network training. Omega, 27, 679–684.
- Denton, J. W. , Hung, M. S. , & Osyk, B. A. 1990. A neural network approach to the classification problem. Expert Systems with Applications, 1(4), 417–424.
- Holmstrom, L. , Koistinen, P. , Laaksonen, J. , & Oja, E. 1997. Neural and statistical classifiers taxonomy and two case studies. IEEE Transactions on Neural Networks,8, 5–17.
- Mangiameli, P. , & West, D. 1999. An improved neural classification network for the two-group problem. Computers and Operations Research, 26, 443–460.
- Patwo, E. , Hu, M. Y. , & Hung, M. S. 1993. "Two-group classification using neural networks" Decision Sciences, 24(4), 825–845.
- Pendharkar, P. C. 2005. A threshold varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers and Operations Research, 32, 2561–2582.
- Chien-Yu Huang, Long-Hui Chen, Yueh-Li Chen, Fengming M. Chang. 2009. Evaluating the process of a genetic algorithm to improve the back-propagation network: A Monte Carlo study, Expert Systems with Applications 36 1459–1465
- Rumelhart, D. E. , Hinton, G. , & Williams, R. 1986. "Learning representation by backpropagation errors". Nature, 32(9), 533–536.
- Gen, M. , & Cheng, R. (1996). Genetic algorithms and engineering design. New York:Wiley.
- Goldberg, D. E. 1989. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison Wesley.
- Deep, K. , & Thakur, M. (2007). A new crossover operator for real coded genetic algorithms. Applied Mathematics and Computation, 188, 895–911.
- Montana D. J. 1989. and L. Davis, "Training Feedforward Neural Networks Using Genetic Algorithms," Proceedings of the International Joint Conference on Artificial Intelligence, pp. 762–767.
- Schaffer J. D. 1992. D. Whitley, and L. J. Eshelman, "Combinations of Genetic Algorithms and Neural Networks: A Survey of the State of the Art," Proceedings of the IEEE Workshop on Combinations of Genetic Algorithms and Neural Network.
- Prechelt, L. : Proben1. 1994. "A Set of Neural Network Benchmark Problems and Benchmarking Rules". Technical Report 21, FakultÄat fÄur Informatik UniversitÄat Karlsruhe, 76128 Karlsruhe, Germany.
- Salzberg, S. L. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1, 317–328.
- Simon Haykin, "Neural Networks: A comprehensive foundation," Pearson Education Asia, Seventh Indian Reprint, 2004.
- Michalewicz, Z. 1992 Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, New York.
- UCI repository of machine learning databases, Department of Information and Computer Sciences, University of California, Irvine, http://www. ics. uci. edu/~mlearn/MLRepositor
Index Terms
Keywords