Evaluating the process of a genetic algorithm to improve the back-propagation network: A Monte Carlo study

Expert Systems with Applications

Volumn 36, Issue 2 PART 1, 2009, Pages 1459-1465

  Huang, Chien Yu ^a   Chen, Long Hui ^a   Chen, Yueh Li ^b   Chang, Fengming M ^c  

^a SHU TE UNIVERSITY   (Taiwan)

^b CHENG SHIU UNIVERSITY   (Taiwan)

^c ASIA UNIVERSITY   (Taiwan)

Author keywords

Back propagation network; Overfitting; Topology

Indexed keywords

ASSEMBLY; BACKPROPAGATION ALGORITHMS; GENETIC ALGORITHMS; INTRUSION DETECTION; MONTE CARLO METHODS; NETWORK ARCHITECTURE; NEURAL NETWORKS; NONLINEAR PROGRAMMING; TOPOLOGY;

ALGORITHM OPERATION; BACKPROPAGATION NETWORK; IMPROVE PERFORMANCE; NEURAL NETWORK TRAINING; NON-LINEAR OPTIMIZATION PROBLEMS; NONLINEAR PROBLEMS; OVERFITTING; PREDICTION ERRORS;

COMPLEX NETWORKS;

EID: 56349166764     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.11.055     Document Type: Article

References (26)

1. Arena, P., Caponetto, R., Fortuna, L., Xibilia, M. G. (1992). Genetic algorithm to select optimal neural network topology. In Proceedings of the 35th Midwest Symposium on Circuits and Systems, (Vol. 2, pp. 1381-1383).
2. Chang P.C., and Wang Y.W. Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert Systems with Applications 30 (2006) 715-726
3. Goldberg D.E. Genetic algorithms in search, optimization, and machine learning (1989), Addison Wesley, New York
4. Gracia J.D., Saravia M.L.M.F.S., Araujo A.N., Lima J.L.F.C., Valle M.D., and Poch M. Evaluation of natural computation techniques in the modeling and optimization of a sequential injection flow system for colorimetric iron (III) determination. Analytica Chimica Acta 348 (1997) 143-150
5. Gupta J.N.D., and Sexton R.S. Comparing back propagation with a genetic algorithm for neural network training. Omega-International Journal of Management Science 27 (1999) 679-684
6. Han J., Morgag C., and Sinne S. Optimization of feedforward neural networks. Engineering Applications of Artificial Intelligence 9 2 (1996) 109-119
7. Heckerling P.S., Gerber B.S., Tape T.G., and Wigton R.S. Use of genetic algorithm for neural networks to predict community-acquired pneumonia. Artificial Intelligence in Medicine 30 (2004) 71-84
8. Jiang N., Zhao Z., and Ren L. Design of structural modular neural networks with genetic algorithm. Advance in Engineering Software 34 (2003) 17-24
9. Kamrani A.K., and Gonzalez R. A genetic algorithm-based solution methodology for modular design. Journal of Intelligent Manufacturing 14 (2003) 599-616
10. Khaw J.F.C., Lim B.S., and Lim L. Optimal design of neural networks using the Taguchi method. Neurocomputing 7 3 (1995) 225-245
11. Kim K., and Han I. Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications 19 (2000) 125-132
12. Li T.S., Su C.T., and Chiang T.L. Applying robust multi-response quality engineering for parameter selection using a novel neural-genetic algorithm. Computers in Industry 50 (2003) 113-122
13. Liu Y., Liu W., and Zhang Y. Inspection of defects in optical fibers based on back-propagation neural networks. Optics Communications 198 4-6 (2001) 369-378
14. Mackey M.C., and Glass L. Oscillation and chaos in physiological control systems. Science 197 (1977) 287-289
15. Maniezzo V. Genetic evolution of the topology and weight distribution of neural networks. IEEE Neural Network 5 (1994) 39-53
16. Ripley B.D. Statistical aspects of neural networks. In: Barndorff-Nielsen O.E., et al. (Ed). Chaos and networks-statistical and probabilistic aspects (1993), Chapman and Hall, London
17. Samanta B., Al-Balushi K.R., and Al-Araimi S.A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 16 (2003) 657-665
18. Schittenkopf C., Deco G., and Brauer W. Two strategies to avoid overfitting in feedforward networks. Neural Network 10 3 (1997) 505-516
19. Schumacher M., Robner R., and Vach W. Neural networks and logistic regression: Part 1. Computational Statistics & Data Analysis 21 (1996) 661-682
20. Sexton R.S., Dorsey R.E., and Jhonson J.D. Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation. Decision Support Systems 22 (1998) 171-185
21. Srinivas M., and Patnaik L.M. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics 24 (1994) 656-666
22. Yasumasa I., Hiroaki K., Yoshiaki S., Masaya H., Toshikuni N., and Yashio I. Application of an improved genetic algorithm to the learning of neural networks. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E77-A 4 (1994) 731-735
23. Zhang L., Jiang J.H., Liu P., Liang Y.Z., and Yu R.Q. Multivariate nonlinear modeling of fluorescence data by neural network with hidden mode pruning algorithm. Analytica Chimica Acta 344 (1997) 29-39
24. Zhang G.P., Patuwo B.E., and Hu M.Y. A simulation study of artificial neural networks for nonlinear time-series forecasting. Computers & Operations Research 28 4 (2001) 381-396
25. Zou H., Xia G., Yang F., and Yang H. A neural network model based on the multi-stage optimization approach for short-term food price forecasting in China. Expert Systems with Applications 33 (2007) 347-356
26. Zurada J.M. Introduction to artificial neural system (1995), West Publishing Company, St. Paul