Optimized BP Neural Network Model Based on Niche Genetic Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 223)


According to the shortcomings of BP neural network model, such as slower convergence speed, entrapment in local optimum, unstable network structure etc., and an improved BP neural network model based on niche genetic algorithm (NGA-BP) was presented. The proposed model first makes full use of the global searching ability of genetic algorithm and the nonlinear reflection ability and the association learning ability of BP neural network to optimize the initial connection weights and thresholds of the neural network by means of selection operation, crossover operation, mutation operation and niche pass, and then adopts BP algorithm to train network, which can effectively solve the problems of BP network about unreasonable initial value and network nonconvergence, and improve the convergence speed and the stability of network. The experimental results show that the model is more feasible and effective than the traditional methods.


BP neural network Niche genetic algorithms Nonlinear reflection Genetic operations 



This work is supported by the Foundation and Frontier Technologies Research Plan Projects of Henan Province of China (No. 102300410266 and No. 122300410287) and a grant from the Ph.D. Research Funded Projects of Zhengzhou University of Light Industry (No. 2010BSJJ038). In addition, this work also received guidance from Huang De-Shuang who is a distinguished professor in Henan Province.


  1. 1.
    Pajares G, Guijarro M (2010) A hopfield neural network for combining classifiers applied to textured images. Neural Netw 23:144–153CrossRefGoogle Scholar
  2. 2.
    Zhang H, Wei W, Yao M (2012) Roundedness and convergence of batch back-propagation algorithm with penalty for feedforward neural networks. Neurocomputing 89:141–146CrossRefGoogle Scholar
  3. 3.
    Ma G-Z, Song E, Hung C-C (2012) Multiple costs based decision making with back-propagation neural networks. Decis Support Syst 52:657–663CrossRefGoogle Scholar
  4. 4.
    Krishnamurti S, Drake L, King J (2011) Neural network modeling of central auditory dysfunction in Alzheimer’s disease. Neural Netw 24:646–651CrossRefGoogle Scholar
  5. 5.
    Wang J-Z, Wang J-J, Zhang Z-G (2011) Forecasting stock indices with back propagation neural network. Expert Syst Appl 38:14346–14355Google Scholar
  6. 6.
    C D-X, Z X-D (2010) A robust dynamic niching genetic algorithm with niche migration for automatic clustering problem. Pattern Recogn 43:1346–1360CrossRefGoogle Scholar
  7. 7.
    Chang D, Zhang X (2009) Dynamic niching genetic algorithm with data attraction for automatic clustering. Tsinghua Sci Technol 14:718–724MATHCrossRefGoogle Scholar
  8. 8.
    Ye F, Qi W, Xiao J (2011) Research of niching genetic algorithm for optimization in electromagnetics. Procedia Eng 16:383–389CrossRefGoogle Scholar
  9. 9.
    Li H, Zhu H (2011) Research on timetabling problem based on niche genetic algorithm. Chin J Comput Eng 37:194–196Google Scholar
  10. 10.
    Fayek MB, Darwish NM, Ali MM (2010) Context based clearing procedure a niching method for genetic algorithm. J Adv Res 1:301–307Google Scholar
  11. 11.
    Dong N, Wu C-H, Ip W-H (2011) An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection. Expert Syst Appl 38:15172–15182CrossRefGoogle Scholar
  12. 12.
    Docekal A, Smid R, Kreidl M (2011) Detecting dominant resonant modes of rolling bearing faults using the niching genetic algorithm. Mech Syst Signal Process 25:2559–2572CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.No.203, School of Computer and Communication EngineeringZhengzhou University of Light IndustryZhengzhouChina

Personalised recommendations