Abstract
Neural tree model has been successfully applied to solving a variety of interesting problems. In most previous studies, optimization of the neural tree model was divided into two steps: first structure optimization, then parameter optimization. One major problem in the evolution of structure without parameter information was noisy fitness evaluation. In this paper, an improved breeder genetic programming algorithm is proposed to the synthesis of neural tree model. The effectiveness and performance of the method are evaluated on time series prediction problems and compared with those of related methods. Simulation results show that the proposed algorithm is a potential method with better performance and effectiveness.
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References
Chen YH, Abraham A (2005) Feature selection and intrusion detection using hybrid flexible neural tree. Lecture Notes Comput Sci 3498:439–444
Chen YH, Yang B, Dong J, Abraham A (2005) Time-series forecasting using flexible neural tree model. Inf Sci 174:219–235
Chen YH, Yang B, Dong J (2004) Evolving flexible neural networks using ant programming and PSO algorithm. In: Proceeding of international symposium on neural networks, Lecture notes on computer science, pp 211–216
Chen YH, Yang B, Dong J (2004) Nonlinear system modeling via optimal design of nerual trees. J Neural Syst 14:125–137
Chen YH, Yang B, Abraham A (2007) Flexible neural trees ensemble for stock index modeling. Neruocomputing 70:697–703
Chen YH, Peng L, Abraham A (2006) Exchange rate forecasting using flexible nerual trees. International symposium on neural networks, pp 518–523
Chen YH, Abraham A, Yang B (2007) Hybird flexible neural- tree-based intrusion detection systems. Int J Intell Syst 22:337–352
Yao X, Lin Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Netw 8:694–713
Yao X (1999) Evolving artificial neural networks. Proc IEEE 87:1423–1447
Fogel DB (1995) Evolutionary computation: towards a new philosophy of machine intelligence. IEEE Press, New York
Angeline PJ, Sauders GM, Pollack JB (1994) An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans Neural Netw 5:54–65
Fogel DB (1995) Phenotypes, geneotypes, and operators in evolutionary computation. In: Proceeding of the IEEE international conference on evolutionary computation, Perth, Australia, IEEE Press, New York, pp 193–198
Zhang BT, Mühlenbein H (1993) Genetic programming of minimal neural nets using Occam’s razor. In: Forrest S (ed) Proceeding of fifth international conference on genetic algorithms, Morgan Kaufmann, pp 342–349
Zhang BT, Mühlenbein H (1993) Evolving optimal neural networks using genetic algorithm with Occam’s razor. Complex Syst
Zhang BT, Mühlenbein H (1994) Synthesis of sigma-pi neural networks by the breeder genetic programming. In: Proceeding of IEEE international conference on evolutionary computation, pp 318–324
Zhang BT, Ohm P, Mühlenbein H (1997) Evolutionary induction of sparse neural trees. Evol Comput 5:213–236
Mühlenbein H (1993) Predictive models for the breeder genetic algorithm I: continuous parameter optimization. Evol Comput 1:25–49
Eiben AE, Smith JE (2008) Introduction to evolutionary computing. Springer, Natural Computing Series (2nd)
Mitchell M, Holland JH (1993) When will a genetic algorithm outperform hill climbing? In: Proceedings of the 5th international conference on genetic algorithms, Morgan Kaufmann Publishers Inc., San Francisco, pp 647–656
Mühlenbein H (1992) How Genetic Algorithms really work. Mutation and hill-climbing. In: Parallel problem solving from nature PPSN II, North-Holland, Amsterdam, 15C25
Box GE (1970) Time series analysis. Forecasting and control. Holden Day, San Francisco
Krzysztof S (2010) Rule weights in a neuro-fuzzy system with a hierarchical domain partition. Int J Appl Math Comput Sci 20(2):337–347
Tong RM (1980) The evaluation of fuzzy models derived from experimental data. Fuzzy Sets Syst 4:1–12
Xu CW, Lu YZ (1987) Fuzzy model identification and self-learning for dynamic systems. IEEE Trans Syst Man Cybern 17:683–389
Lee YC, Wang EH, Shih YP (1994) A combined approach to fuzzy model iddentification. IEEE Trans Syst Man Cybern 24:736–744
Lin Y, Cunningham GA (1995) A new approach to fuzzy-neural system modelling. IEEE Trans Fuzzy Syst 3:190–197
Nie J (1995) Constructing fuzzy model by self-organising counter propagation network. IEEE Trans Syst Man Cybern 25:963–970
Jang JS, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentic-Hall, Upper Saddle River
Kasabov N, Kim JS, Watts M, Gray A (1996) FuNN/2-a fuzzy neural network architecture for adaptive learning and knowledge acquisition. Inf Sci 101:155–175
Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22:1414–1427
Rojas I, Pomares H, Luis BJ et al (2002) Time series analysis using normalized PG-RBF network with regression weights. Neurocomputing 42:267C285
Acknowledgments
This project is carried out under the Taishan Scholar project of Shandong China, and this research is also supported by the Natural Science Foundation of China (No. 60873058), the Natural Science Foundation of Shandong Province (No. Z2007G03, No. Z2008G04), and the Science and Technology Project of Shandong Education Bureau, Shangdong Province Young Scientists Research Awards Fund (BS2009DX005).
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Qi, F., Liu, X. & Ma, Y. Synthesis of neural tree models by improved breeder genetic programming. Neural Comput & Applic 21, 515–521 (2012). https://doi.org/10.1007/s00521-010-0451-z
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DOI: https://doi.org/10.1007/s00521-010-0451-z