A Neuro-Genetic Technique for Pruning and Optimization of ANN Weights

References

• Aleksander, I., and J. Taylor. 1992. Pruning neural nets by genetic algorithm. Artificial neural networks 2. International Conference on Artificial Neural Networks (ICANN-92). Brighton, United Kingdom: Elsevier Science Publishers.
   Google Scholar
• Asmaa, A. N., A. T. Erdogan, and T. Arslan. 2013. Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes. Soft Computing 17:1145–57. doi:10.1007/s00500-013-0990-1.
   Web of Science ®Google Scholar
• Augasta, M. G., and T. Kathirvalavakumar. 2015. Pruning algorithms of neural networks – a comparative study. Central European Journal of Computer Science 3 (3):105–15.
   Google Scholar
• Bull, L. 2001. On Co-evolutionary genetic algorithms. Soft Computing 5:201–07. doi:10.1007/s005000100082.
   Google Scholar
• Christiansen, N., J. Job, K. Klyver, and J. Hogsberg. 2012. Optimal brain surgeon on artificial neural networks in nonlinear structural dynamics. 25th Nordic seminar on computational mechanics. Lund, Sweden.
   Google Scholar
• Haupt, S. E., and R. L. Haupt. 2011. A mixed integer genetic algorithm used in biological and chemical defense applications. Soft Computation 15 (1):51–59. doi:10.1007/s00500-009-0516-z.
   Web of Science ®Google Scholar
• Haykin, S. 2007. Neural network and learning machines. India: Prentice-HALL.
   Google Scholar
• Hu, L., L. Qin, K. Mao, and W. Chen. 2015. Optimization of neural network by genetic algorithm for flowrate determination in multipath ultrasonic gas flowmeter. IEEE Sensors Journal 16 (5):1158–67. doi:10.1109/JSEN.2015.2501427.
   Web of Science ®Google Scholar
• Hunter, A., and K. S. Chiu. 2000. Genetic algorithm design of neural network and fuzzy logic controllers. Soft Computation 4:186–92. doi:10.1007/s005000000050.
   Google Scholar
• Karlra, P., and N. R. Prakash. 2003. A neuro-genetic algorithm approach for solving the inverse kinematics of robotic manipulators. IEEE Conference on Systems, Man and Cybernetics 2. Washington, USA: IEEE Publishers.
   Google Scholar
• Karthikeyan, P., S. Baskar, and A. Alphones. 2012. Improved genetic algorithm using different genetic operator combinations (GOCs) for multicast routing in ad hoc networks. Soft Computing 17 (9):1563–72. doi:10.1007/s00500-012-0976-4.
   Web of Science ®Google Scholar
• Kingston, G., H. Maier, and M. Lambert. 2004. A statistical input pruning method for artificial neural networks used in environmental modeling. Biennial Meeting of the International Environmental Modelling and Software Society. Osnabruck, Germany: iEMSs Publisher.
   Google Scholar
• Kumar, P., D. Gospodaric, and P. Bauer. 2007. Improved genetic algorithm inspired by biological evolution. Soft Computation 11 (10):923–41. doi:10.1007/s00500-006-0143-x.
   Web of Science ®Google Scholar
• Kwon, Y. K., and B. R. Moon. 2007. A hybrid neurogenetic approach for stock forecasting. IEEE Trans. Neural Network 18 (3):851–64. doi:10.1109/TNN.2007.891629.
   Web of Science ®Google Scholar
• Libelli, S., Marsili, and P. Alba. 2000. Adaptive mutation in genetic algorithms. Soft Computation 4 (2):76–80. doi:10.1007/s005000000042.
   Google Scholar
• Lin, C. T., M. Prasad, and A. Saxena. 2015. An improved polynomial neural network classifier using real-coded genetic algorithm. IEEE transactions on systems. Man, and Cybernetics: Systems 45 (11):1389–401.
   Web of Science ®Google Scholar
• Ling, S. H., and F. H. F. Leung. 2007a. An improved genetic algorithm with average-bound cross over and wavelet mutation operations. Soft Computation 11 (1):7–31. doi:10.1007/s00500-006-0049-7.
   Web of Science ®Google Scholar
• Ling, S. H., F. H. F. Leung, and H. K. Lam. 2007b. Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications. Soft Computation 11 (11):1033–52. doi:10.1007/s00500-007-0151-5.
   Web of Science ®Google Scholar
• Louis, S. J. 2005. Genetic learning for combinational logic design. Soft Computation 9 (1):38–43. doi:10.1007/s00500-003-0332-9.
   Web of Science ®Google Scholar
• Mantzaris, D., G. Anastassopoulos, and A. Adamopoulos. 2011. Genetic algorithm pruning of probabilistic neural networks in medical disease estimation. Neural Networks 24 (8):831–35. doi:10.1016/j.neunet.2011.06.003.
   PubMed Web of Science ®Google Scholar
• Medeiros, C. M., and G. A. Barreto. 2013. A novel weight pruning method for MLP classifiers based on the MAXCORE principle. Neural Computing and Applications 22 (1):71–84. doi:10.1007/s00521-011-0748-6.
   Web of Science ®Google Scholar
• Mesquita, D. P., J. Gomes, R. L. Rodrigues, and R. K. Galvao. 2015. Pruning extreme learning machines using the successive projections algorithm. IEEE Latin America Transactions 13 (12):3974–79. doi:10.1109/TLA.2015.7404935.
   Web of Science ®Google Scholar
• Ramasubramanian, P., and A. Kannan. 2006. A genetic-algorithm based neural network short-term forecasting framework for database intrusion prediction system. Soft Computation 10 (8):699–714. doi:10.1007/s00500-005-0513-9.
   Web of Science ®Google Scholar
• Reed, R. 1993. Pruning algorithms-A survey. IEEE Transactions on Neural Networks 4 (5):740–47. doi:10.1109/72.248452.
   PubMed Web of Science ®Google Scholar
• Sabo, D., and X. Yu. 2008. A new pruning algorithm for neural network dimension analysis. IEEE World Congress on computational Intelligence Proc. Hong Kong, China.
   Google Scholar
• Samanta, B., K. R. Al-Balushi, and S. A. Al-Araimi. 2006. Artificial neural networks and genetic algorithm for bearing fault detection. Soft Computation 10 (3):264–71. doi:10.1007/s00500-005-0481-0.
   Web of Science ®Google Scholar
• Sivanandam, S. N., and S. N. Deepa. 2007. Introduction to Genetic Algorithms. Berlin, Heidelberg: Springer Science & Business Media.
   Google Scholar
• Srivastava, A. K., K. K. Shukla, and S. K. Srivastava. 1998. Exploring neuro-genetic processing of electronic-noise data. Microelectronics Journal 29 (11):921–31. doi:10.1016/S0026-2692(98)00056-1.
   Web of Science ®Google Scholar
• Tan, F., X. Fu, Y. Zhang, and A. G. Bourgeois. 2008. A genetic algorithm-based method for feature subset selection. Soft Computation 12 (2):111–20. doi:10.1007/s00500-007-0193-8.
   Web of Science ®Google Scholar
• Tong, Z., and G. Tanaka. 2015. A pruning method based on weight variation information for feedforward neural networks. 4th IFAC Conference on Analysis and Control of Chaotic Systems 48(18): 221–226. Elsevier Science Publishers
   Google Scholar
• UCI machice learning repository http://archive.ics.uci.edu/ml/datasets
   Google Scholar
• Yang, S. H., and Y. P. Chen. 2012. An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications. Neurocomputing 86 (1):140–49. doi:10.1016/j.neucom.2012.01.024.
   Google Scholar