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Comparing Performances of Cuckoo Search Based Neural Networks

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Recent Advances on Soft Computing and Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 287))

Abstract

Nature inspired meta-heuristic algorithms provide derivative-free solutions to solve complex problems. Cuckoo Search (CS) algorithm is one of the latest additions to the group of nature inspired optimization heuristics. In this paper, Cuckoo Search (CS) is implemented in conjunction with Back propagation Neural Network (BPNN), Recurrent Neural Network (RNN), and Levenberg Marquardt back propagation (LMBP) algorithms to achieve faster convergence rate and to avoid local minima problem. The performances of the proposed Cuckoo Search Back propagation (CSBP), Cuckoo Search Levenberg Marquardt (CSLM) and Cuckoo Search Recurrent Neural Network (CSRNN) algorithms are compared by means of simulations on OR and XOR datasets. The simulation results show that the CSRNN performs better than other algorithms in terms of convergence speed and Mean Squared Error (MSE).

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References

  1. Radhika, Y., Shashi, M.: Atmospheric Temperature Prediction using Support Vector Machines. Int. J. of Computer Theory and Engineering 1(1), 1793–8201 (2009)

    Google Scholar 

  2. Akcayol, M.A., Cinar, C.: Artificial neural network based modeling of heated catalytic converter performance. J. Applied thermal Engineering 25, 2341–2350 (2005)

    Article  Google Scholar 

  3. Shereef, K.I., Baboo, S.S.: A New Weather Forecasting Technique using Back Propagation Neural Network with Modified Levenberg-Marquardt Algorithm for Learning. Int. J. of Computer Science 8(6-2), 1694–1814 (2011)

    Google Scholar 

  4. Kosko, B.: Neural Network and Fuzzy Systm, 1st edn. Prentice Hall of India (1994)

    Google Scholar 

  5. Krasnopolsky, V.M., Chevallier, F.: Some Neural Network application in environmental sciences. Part II: Advancing Computational Efficiency of environmental numerical models. J. Neural Networks 16(3-4), 335–348 (2003)

    Article  Google Scholar 

  6. Coppin, B.: Artificial Intelligence Illuminated, USA. Jones and Bartlet Illuminated Series, ch. 11, pp. 291–324 (2004)

    Google Scholar 

  7. Basheer, I.A., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. of Microbiological Methods 43(1), 3–31 (2000)

    Article  Google Scholar 

  8. Zheng, H., Meng, W., Gong, B.: Neural Network and its Application on Machine fault Diagnosis. In: ICSYSE, pp. 576–579 (1992)

    Google Scholar 

  9. Rehman, M.Z., Nawi, N.M.: Improving the Accuracy of Gradient Descent Back Propagation Algorithm (GDAM) on Classification Problems. Int. J. of New Computer Architectures and their Applications (IJNCAA) 1(4), 838–847 (2012)

    Google Scholar 

  10. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back– Propagating Errors. J. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  11. Lahmiri, S.: A comparative study of backpropagation algorithms in financial prediction. Int. J. of Computer Science, Engineering and Applications (IJCSEA) 1(4) (2011)

    Google Scholar 

  12. Nawi, M.N., Ransing, R.S., AbdulHamid, N.: BPGD-AG: A New Improvement of Back-Propagation Neural Network Learning Algorithms with Adaptive Gain. J. of Science and Technology 2(2) (2011)

    Google Scholar 

  13. Wam, A., Esm, S., Esa, A.: Modified Back Propagation Algorithm for Learning Artificial Neural Networks. In: 8th NRSC, pp. 345–352 (2001)

    Google Scholar 

  14. Wen, J., Zhao, J.L., Luo, S.W., Han, Z.: The Improvements of BP Neural Network Learning Algorithm. In: 5th WCCC-ICSP, vol. 3, pp. 1647–1649 (2000)

    Google Scholar 

  15. Lahmiri, S.: Wavelet transform, neural networks and the prediction of s & p price index: a comparative paper of back propagation numerical algorithms. J. Business Intelligence 5(2) (2012)

    Google Scholar 

  16. Nawi, N.M., Ransing, R.S., Salleh, M.N.M., Ghazali, R., Hamid, N.A.: An improved back propagation neural network algorithm on classification problems. In: Zhang, Y., Cuzzocrea, A., Ma, J., Chung, K.-i., Arslan, T., Song, X. (eds.) DTA and BSBT 2010. CCIS, vol. 118, pp. 177–188. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  17. Gupta, J.N.D., Sexton, R.S.: Comparing back propagation with a genetic algorithm for neural network training. J. International Journal of Management Science 27, 679–684 (1999)

    Google Scholar 

  18. Rehman, M.Z., Nawi, N.M.: Studying the Effect of adaptive momentum in improving the accuracy of gradient descent back propagation algorithm on classification problems. J. Int. Journal of Modern Physics: Conference Series 9, 432–439 (2012)

    Google Scholar 

  19. Yam, J.Y.F., Chow, T.W.S.: Extended least squares based algorithm for training feed forward networks. J. IEEE Transactions on Neural Networks 8, 806–810 (1997)

    Article  Google Scholar 

  20. Yam, J.Y.F., Chow, T.W.S.: A weight initialization method for improving training speed in feed forward neural networks. J. Neurocomputing 30, 219–232 (2000)

    Article  Google Scholar 

  21. Yam, J.Y.F., Chow, T.W.S.: Feed forward networks training speed enhancement by optimal initialization of the synaptic coefficients. J. IEEE Transactions on Neural Networks 12, 430–434 (2001)

    Article  Google Scholar 

  22. Kwok, T.Y., Yeung, D.Y.: Objective functions for training new hidden units in constructive neural networks. J. IEEE Transactions on Neural Networks 8, 1131–1147 (1997)

    Article  Google Scholar 

  23. Zhang, J., Lok, T., Lyu, M.: A hybrid particle swarm optimization back propagation algorithm for feed forward neural network training. J. Applied Mathematics and Computation 185, 1026–1037 (2007)

    Article  MATH  Google Scholar 

  24. Shah, H., Ghazali, R., Nawi, N.M., Deris, M.M.: Global hybrid ant bee colony algorithm for training artificial neural networks. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part I. LNCS, vol. 7333, pp. 87–100. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Shah, H., Ghazali, R., Nawi, N.M.: Hybrid ant bee colony algorithm for volcano temperature prediction. In: Chowdhry, B.S., Shaikh, F.K., Hussain, D.M.A., Uqaili, M.A. (eds.) IMTIC 2012. CCIS, vol. 281, pp. 453–465. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  26. Karaboga, D.: Artificial bee colony algorithm. J. Scholarpedia 5(3), 6915 (2010)

    Article  Google Scholar 

  27. Yao, X.: Evolutionary artificial neural networks. J. International Journal of Neural Systems 4(3), 203–222 (1993)

    Article  Google Scholar 

  28. Mendes, R., Cortez, P., Rocha, M., Neves, J.: Particle swarm for feedforward neural network training. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1895–1899 (2002)

    Google Scholar 

  29. Lonen, I., Kamarainen, I.J., Lampinen, J.I.: Differential Evolution Training Algorithm for Feed-Forward Neural Networks. J. Neural Processing Letters 17(1), 93–105 (2003)

    Article  Google Scholar 

  30. Liu, Y.-P., Wu, M.-G., Qian, J.-X.: Evolving Neural Networks Using the Hybrid of Ant Colony Optimization and BP Algorithms. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 714–722. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  31. Khan, A.U., Bandopadhyaya, T.K., Sharma, S.: Comparisons of Stock Rates Prediction Accuracy using Different Technical Indicators with Back propagation Neural Network and Genetic Algorithm Based Back propagation Neural Network. In: The First International Conference on Emerging Trends in Engineering and Technology, pp. 575–580 (2008)

    Google Scholar 

  32. Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, India, pp. 210–214 (2009)

    Google Scholar 

  33. Yang, X.S., Deb, S.: Engineering Optimisation by Cuckoo Search. J. of Mathematical Modelling and Numerical Optimisation 1(4), 330–343 (2010)

    Article  MATH  Google Scholar 

  34. Tuba, M., Subotic, M., Stanarevic, N.: Modified cuckoo search algorithm for unconstrainedoptimization problems. In: The European Computing Conference, pp. 263–268 (2011)

    Google Scholar 

  35. Tuba, M., Subotic, M., Stanarevic, N.: Performance of a Modified Cuckoo Search Algorithm for Unconstrained Optimization Problems. J. Faculty of Computer Science 11(2), 62–74 (2012)

    Google Scholar 

  36. Chaowanawate, K., Heednacram, A.: Implementation of Cuckoo Search in RBF Neural Network for Flood Forecasting. In: Fourth International Conference on Computational Intelligence, Communication Systems and Networks, pp. 22–26 (2012)

    Google Scholar 

  37. Pavlyukevich, I.: Levy flights, non-local search and simulated annealing. J. of Computational Physics 226(2), 1830–1844 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  38. Walton, S., Hassan, O., Morgan, K., Brown, M.: Modified cuckoo search: A new gradient free optimisation algorithm. J. Chaos, Solitons& Fractals 44(9), 710–718 (2011)

    Article  Google Scholar 

  39. Hagan, M.T., Menhaj, M.B.: Training Feedforward Networks with the Marquardt Algorithm. J. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)

    Article  Google Scholar 

  40. Nawi, N.M., Khan, A., Rehman, M.Z.: A New Cuckoo Search based Levenberg-Marquardt (CSLM) Algorithm. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part I. LNCS, vol. 7971, pp. 438–451. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  41. Nawi, N.M., Khan, A., Rehman, M.Z.: A New Back-propagation Neural Network optimized with Cuckoo Search Algorithm. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part I. LNCS, vol. 7971, pp. 413–426. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  42. Nawi, N.M., Khan, A., Rehman, M.Z.: A New Optimized Cuckoo Search Recurrent Neural Network (CSRNN) Algorithm. In: Sakim, H.A.M., Mustaffa, M.T. (eds.) ROVISP. LNEE, vol. 291, pp. 335–341. Springer, Heidelberg (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Software and Multimedia Centre (SMC), Parit Raja, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi, Abdullah Khan, M. Z. Rehman & Mustafa Mat Deris

  3. Department of Information System, University of Malaya, 50603, Pantai Valley, Kuala Lumpur, Malaysia

    Tutut Herawan

  4. AMCS Research Center, Yogyakarta, Indonesia

    Tutut Herawan

Authors
  1. Nazri Mohd Nawi
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  2. Abdullah Khan
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  3. M. Z. Rehman
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  4. Tutut Herawan
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  5. Mustafa Mat Deris
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Corresponding author

Correspondence to Nazri Mohd Nawi .

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Editors and Affiliations

  1. Department of Information System Faculty of Comp. Sci. & Info. Tech., University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Faculty of Comp. Sci. and Info. Tech, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Rozaida Ghazali

  3. Faculty of Comp. Sci. and Info. Tech., Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Mustafa Mat Deris

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Nawi, N.M., Khan, A., Rehman, M.Z., Herawan, T., Deris, M.M. (2014). Comparing Performances of Cuckoo Search Based Neural Networks. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-07692-8_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07691-1

  • Online ISBN: 978-3-319-07692-8

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