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Training a Feed-Forward Neural Network Using Cuckoo Search

  • Adit KotwalEmail author
  • Jai Kotia
  • Rishika Bharti
  • Ramchandra Mangrulkar
Chapter
  • 3 Downloads
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

Cuckoo Search (CS) is a nature-inspired and metaheuristic algorithm which is based on a brood reproductive strategy of cuckoo birds to increase their population. This algorithm mainly serves to determine the maximum or minimum value of a particular problem which is known as the objective function. CS has reportedly outperformed other nature-inspired algorithms in terms of computational efficiency and the speed of convergence to reach an optimal solution. This chapter aims at exploring the application of CS to determine the parameters of Artificial Neural Networks (ANN). The inherent problem with traditional training of ANNs using backpropagation is that the learning process cannot guarantee a global minimum solution and has a tendency of getting trapped in local minima. The working of such ANN models is restricted to a differentiable neuron transfer function. The CS algorithm has been observed to provide a solution without the use of derivates to optimize such convoluted problems. The usage of ANNs across a wide range of problems including classification tasks, image processing, signal processing, etc. justifies the application of CS to the backpropagation procedure of ANNs to achieve a faster rate of convergence and avoid the local minima problem. This chapter also presents discussions and results on how ANNs optimized with variants of CS perform when applied to the detection of chronic kidney disease, modelling of operating photovoltaic module temperature and forest type classification.

Keywords

Cuckoo Search Algorithm Artificial Neural Networks Machine Learning Backpropagation Optimization 

References

  1. 1.
    McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133MathSciNetCrossRefGoogle Scholar
  2. 2.
    Abraham A Artificial neural networks. In: Sydenham PH, Thorn R (eds) Handbook of measuring system design. https://doi.org/10.1002/0471497398.mm421
  3. 3.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536CrossRefGoogle Scholar
  4. 4.
    Yang X-S, Deb S (2009) Cuckoo Search via Lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC). Coimbatore, pp 210–214.  https://doi.org/10.1109/NABIC.2009.5393690
  5. 5.
    Mareli M, Twala B (2018) An adaptive Cuckoo search algorithm for optimisation. Appl Comput Inform 14(2):107–115CrossRefGoogle Scholar
  6. 6.
    Nawi NM, Khan A, Rehman MZ, Aziz MA, Herawan T, Abawajy JH (2014) Neural network training by hybrid accelerated Cuckoo particle Swarm optimization algorithm. In: Loo CK, Yap KS, Wong KW, Teoh A, Huang K (eds) Neural information processing. ICONIP, Lecture Notes in Computer Science, vol 8835. Springer, ChamGoogle Scholar
  7. 7.
    Nur AM, Radzi NH, Ibrahim AO (2014) Artificial neural network weight optimization: a reviewGoogle Scholar
  8. 8.
    Kajornrit J (2015) A comparative study of optimization methods for improving artificial neural network performance. In: 2015 7th international conference on information technology and electrical engineering (ICITEE), Chiang Mai, pp 35–40Google Scholar
  9. 9.
    Chatterjee S, Sarkar S, Dey N, Ashour AS, Sen S, Hassanien AE (2017) Application of Cuckoo search in water quality prediction using artificial neural network. Int J Comput Intell Stud 6:229.  https://doi.org/10.1504/IJCISTUDIES.2017.089054
  10. 10.
    Chatterjee S, Dey N, Ashour AS, Drugarin CVA (2018) Electrical energy output prediction using Cuckoo search based artificial neural network. In: Yang XS, Nagar A, Joshi A (eds) Smart trends in systems, security and sustainability. Lecture Notes in Networks and Systems, vol 18. Springer, SingaporeGoogle Scholar
  11. 11.
    Sulaiman SI, Zainol NZ, Othman Z, Zainuddin H (2014) Cuckoo search for determining artificial neural network training parameters in modeling operating photovoltaic module temperature. In: Proceedings of 2014 international conference on modelling, identification & control, Melbourne, VIC, pp 306–309Google Scholar
  12. 12.
    Chatterjee S, Sarkar S, Dey N, Sen S, Ashour AS, Fong S, Fuqian S (2017) Modified Cuckoo search based neural networks for forest types classification. In: 2nd international conference on information technology and intelligent transportation systems (ITITS 2017)Google Scholar
  13. 13.
    Chatterjee S, Banerjee S, Basu P, Debnath M, Sen S (2017) Cuckoo search coupled artificial neural network in detection of chronic kidney disease. In: 2017, 1st international conference on electronics, materials engineering and nano-technology (IEMENTech), Kolkata, pp 1–4Google Scholar
  14. 14.
    Dua D, Graff C (2019) UCI machine learning repository. University of California, School of Information and Computer Science, Irvine, CAGoogle Scholar
  15. 15.
    Zhang Z, Chen Y (2014) An improved Cuckoo search algorithm with adaptive method. In: 2014, seventh international joint conference on computational sciences and optimization, Beijing, pp 204–207Google Scholar
  16. 16.
    Wang J, Zhou B, Zhou S (2016) An improved Cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation. Comput Intell Neurosci 2016(Article ID 2959370):8 pp.  https://doi.org/10.1155/2016/2959370
  17. 17.
    Chen H-L, Yu B, Zhou H-L, Meng Z (2019) Improved Cuckoo search algorithm for solving inverse geometry heat conduction problems. Heat Transfer Eng 40(3–4):362–374.  https://doi.org/10.1080/01457632.2018.1429060CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Adit Kotwal
    • 1
    Email author
  • Jai Kotia
    • 1
  • Rishika Bharti
    • 1
  • Ramchandra Mangrulkar
    • 1
  1. 1.Dwarkadas J. Sanghvi College of EngineeringVile ParleIndia

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