Quantum-based algorithm for optimizing artificial neural networks

IEEE Transactions on Neural Networks and Learning Systems

Volumn 24, Issue 8, 2013, Pages 1266-1278

  Lu, Tzyy Chyang ^a   Yu, Gwo Ruey ^a   Juang, Jyh Ching ^b  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Classification problem; mapping problem; quantum neural network (QNN)

Indexed keywords

BENCH-MARK PROBLEMS; CLASSIFICATION PERFORMANCE; EVOLVING ARTIFICIAL NEURAL NETWORKS; GENERALIZATION ABILITY; MAPPING PROBLEM; NETWORK STRUCTURES; NOISY FITNESS EVALUATION; QUANTUM NEURAL NETWORKS;

NEURAL NETWORKS; OPTIMIZATION; QUANTUM COMPUTERS;

ALGORITHMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BREAST TUMOR; COMPUTER SIMULATION; DIABETES MELLITUS; HEART; HUMAN; IRIS; PROBABILITY; QUALITY CONTROL;

ALGORITHMS; BENCHMARKING; BREAST NEOPLASMS; COMPUTER SIMULATION; DIABETES MELLITUS; HEART; HUMANS; IRIS; NEURAL NETWORKS (COMPUTER); PROBABILITY;

EID: 84880899852     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2249089     Document Type: Article

References (47)

1. R. Xuemei and L. Xiaohua, "Identification of extended Hammerstein systems using dynamic self-optimizing neural networks," IEEE Trans. Neural Netw., vol. 22, no. 8, pp. 1169-1179, Aug. 2011.
2. P. K. Patra, M. Sahu, S. Mohapatra, and R. K. Samantray, "File access prediction using neural networks," IEEE Trans. Neural Netw., vol. 21, no. 6, pp. 869-882, Jun. 2010.
3. T. H. Oong and N. A. M. Isa, "Adaptive evolutionary artificial neural networks for pattern classification," IEEE Trans. Neural Netw., vol. 22, no. 11, pp. 1823-1836, Nov. 2011.
4. K. Jonghoon, L. Seongiun, and B. H. Cho, "Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction," IEEE Trans. Power Electron., vol. 27, no. 1, pp. 436-451, Jan. 2012.
5. X. Yao, "Evolving artificial neural networks," Proc. IEEE, vol. 87, no. 9, pp. 1423-1447, Sep. 1999. (Pubitemid 30503424)
6. S. Van den Dries and M. A. Wiering, "Neural-fitted TD-leaf learning for playing othello with structured neural networks," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 11, pp. 1701-1713, Nov. 2012.
7. M. H. Shih and F. S. Tsai, "Decirculation process in neural network dynamics," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 11, pp. 1677-1689, Nov. 2012.
8. C. Xiao, Z. Cai, Y. Wang, and X. Liu, "Tuning of the structure and parameters of a neural network using a good points set evolutionary strategy," in Proc. 9th Int. Conf. Young Comput. Sci., Nov. 2008, pp. 1749-1754.
9. R. Parekh, J. Yang, and V. Honavar, "Constructive neural-network learning algorithms for pattern classification," IEEE Trans. Neural Netw., vol. 11, no. 2, pp. 436-450, Mar. 2000.
10. M. M. Islam, X. Yao, and K. Murase, "A constructive algorithm for training cooperative neural network ensembles," IEEE Trans. Neural Netw., vol. 14, no. 4, pp. 820-834, Jul. 2003.
11. H. H. Thodberg, "Improving generalization of neural networks through pruning," Int. J. Neural Syst., vol. 1, no. 4, pp. 317-326, Jul. 1991.
12. R. Reed, "Pruning algorithms-a survey," IEEE Trans. Neural Netw., vol. 4, no. 5, pp. 740-747, Sep. 1993.
13. P. J. Angeline, G. M. Sauders, and J. B. Pollack, "An evolutionary algorithm that constructs recurrent neural networks," IEEE Trans. Neural Netw., vol. 5, no. 1, pp. 54-65, Jan. 1994.
14. G. F. Miller, P. M. Todd, and S. U. Hedge, "Designing neural networks," Neural Netw., vol. 4, pp. 53-60, Nov. 1991.
15. N. Nikolaev and H. Iba, "Learning polynomial feedforward neural networks by genetic programming and backpropagation," IEEE Trans. Neural Netw., vol. 14, no. 2, pp. 337-350, Mar. 2003.
16. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing, vol. 1, D. E. Rumelhart and J. L. McClelland, Eds. Cambridge, MA, USA: MIT Press, 1986, pp. 318-362.
17. X. Yao and Y. Liu, "A new evolutionary system for evolving artificial neural networks," IEEE Trans. Neural Netw., vol. 8, no. 3, pp. 694-713, May 1997. (Pubitemid 127767812)
18. F. H. F. Leung, H. K. Lam, S. H. Ling, and P. K. S. Tam, "Tuning of the structure and parameters of a neural network using an improved genetic algorithm," IEEE Trans. Neural Netw., vol. 14, no. 1, pp. 79-88, Jan. 2003.
19. J. Tsai, J. Chou, and T. Liu, "Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm," IEEE Trans. Neural Netw., vol. 17, no. 1, pp. 69-80, Jan. 2006. (Pubitemid 43263941)
20. T. B. Ludermir and C. Zanchettin, "An optimization methodology for neural network weights and architectures," IEEE Trans. Neural Netw., vol. 17, no. 6, pp. 1452-1459, Nov. 2006. (Pubitemid 44824259)
21. L. Li and B. Niu, "A hybrid evolutionary system for designing artificial neural networks," in Proc. Int. Conf. Comput. Sci. Softw. Eng., vol. 4. Dec. 2008, pp. 859-862.
22. R. K. Belew, J. McInerney, and N. N. Schraudolph, "Evolving networks: Using genetic algorithm with connectionist learning," Dept. Comput. Sci. Eng., Univ. California, San Diego, CA, USA, Tech. Rep. CS90-174, Feb. 1991.
23. P. J. B. Hancock, "Genetic algorithms and permutation problems: A comparison of recombination operators for neural net structure specification," in Proc. Int. Workshop Combinat. Genet. Algorithms Neural Netw., Jun. 1992, pp. 108-122.
24. J. D. Schaffer, L. D. Whitley, and L. J. Eshelman, "Combinations of genetic algorithms and neural networks: A survey of the state of the art," in Proc. Int. Workshop Combinat. Genet. Algorithms Neural Netw., Jun. 1992, pp. 1-37.
25. V. Maniezzo, "Genetic evolution of the topology and weight distribution of neural networks," IEEE Trans. Neural Netw., vol. 5, no. 1, pp. 39-53, Jan. 1994.
26. J. Fang and Y. Xi, "Neural network design based on evolutionary programming," Artif. Intell. Eng., vol. 11, no. 2, pp. 155-161, Apr. 1997. (Pubitemid 127415965)
27. X. Yao and Y. Liu, "Toward designing artificial neural networks by evolution," Appl. Math. Comput., vol. 91, no. 1, pp. 83-90, Apr. 1998. (Pubitemid 128682457)
28. P. Kohn, "Combining genetic algorithms and neural networks," M.S. thesis, Dept. Comput. Sci., Univ. Tenessee, Knoxville, TN, USA, 1994.
29. K. O. Stanley and R. Miikkulainen, "Evolving neural networks through augmenting topologies," Evol. Comput., vol. 10, no. 2, pp. 99-127, Jun. 2002.
30. K. O. Stanley, B. D. Bryant, and R. Miikkulainen, "Real-time neuroevolution in the NERO video game," IEEE Trans. Evol. Comput., vol. 9, no. 6, pp. 653-668, Dec. 2005. (Pubitemid 41825241)
31. T. Mu, J. Jiang, Y. Wang, and J. Y. Goulermas, "Adaptive data embedding framework for multiclass classification," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 8, pp. 1291-1303, Aug. 2012.
32. J. C. F. Pujol and R. Poli, "Evolving the topology and the weights of neural networks using a dual representation," Appl. Intell., vol. 8, no. 1, pp. 73-84, Jan.-Feb. 1998. (Pubitemid 128513770)
33. K. H. Han and J. H. Kim, "Quantum-inspired evolutionary algorithm for a class of combinatorial optimization," IEEE Trans. Evol. Comput., vol. 6, no. 6, pp. 580-593, Dec. 2002.
34. K. H. Han and J. H. Kim, "On the analysis of the quantum-inspired evolutionary algorithm with a single individual," in Proc. Congr. Evol. Comput., Jul. 2006, pp. 2622-2629.
35. R. Zhang and H. Gao, "Improved quantum evolutionary algorithm for combinational optimization problem," in Proc. 6th Int. Conf. Mach. Learn. Cybern., 2007, pp. 3501-3505.
36. K. H. Han and J. H. Kim, "Quantum-inspired evolutionary algorithms with a new termination criterion, gate, and two-phase scheme," IEEE Trans. Evol. Comput., vol. 8, no. 2, pp. 156-169, Apr. 2004.
37. L. Jiao, Y. Li, M. Gong, and X. Zhang, "Quantum-inspired immune colonial algorithm for global optimization," IEEE Trans. Syst. Man Cybern., vol. 35, no. 5, pp. 1234-1253, Apr. 2008.
38. S. Yang, M. Wang, and L. Jiao, "A novel quantum evolutionary algorithm and its application," in Proc. Congr. Evol. Comput., Jun. 2004, pp. 820-826.
39. H. Liu, G. Zhang, C. Liu, and C. Fang, "A novel memetic algorithm based on real-observation quantum-inspired evolutionary algorithms," in Proc. Conf. Intell. Syst. Knowl. Eng., vol. 1. Nov. 2008, pp. 486-490.
40. G. Bebis, M. Georgiopoulos, and T. Kasparis, "Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization," Neurocomputing, vol. 17, pp. 167-194, May 1997. (Pubitemid 27496108)
41. L. Prechelt, "PROBEN1-A set of neural network benchmark problems and benchmarking rules," Faculty Informat., Univ. Karlsruhe, Karlsruhe, Germany, Tech. Rep. 21/94, Sep. 1994.
42. C. K. Goh, E. J. Teoh, and K. C. Tan, "Hybrid multiobjective evolutionary design for artificial neural networks," IEEE Trans. Neural Netw., vol. 19, no. 9, pp. 1531-1548, Sep. 2008.
43. M. M. Islam, M. A. Sattar, M. F. Amin, X. Yao, and K. Murase, "A new adaptive merging and growing algorithm for designing artificial neural networks," IEEE Trans. Syst. Man Cybern., vol. 39, no. 3, pp. 705-722, Jun. 2009.
44. M. M. Islam, M. A. Sattar, M. F. Amin, X. Yao, and K. Murase, "A new constructive algorithm for architectural and functional adaptation of artificial neural networks," IEEE Trans. Syst. Man Cybern., vol. 39, no. 6, pp. 1590-1605, Dec. 2009.
45. E. Cant-Paz and C. Kamath, "Inducing oblique decision trees with evolutionary algorithms," IEEE Trans. Evol. Comput., vol. 7, no. 1, pp. 54-68, Feb. 2003.
46. N. Garcia-Pedrajas, C. Hervas-Martinez, and D. Ortiz-Boyer, "Cooperative coevolution of artificial neural networks ensembles for pattern classification," IEEE Trans. Evol. Comput., vol. 9, no. 3, pp. 271-302, Jun. 2005. (Pubitemid 40871133)
47. L. Ma and K. Khorasani, "Constructive feedforward neural networks using Hermite polynomial activation functions," IEEE Trans. Neural Netw., vol. 16, no. 4, pp. 821-833, Jul. 2005. (Pubitemid 41064314)