| dc.identifier.citation | B. Verma and R. Ghosh (2002), “A novel evolutionary neural learning algorithm, evolutionary computation,” in Proceedings of CEC’02, May 12-17, pp. 1884–1889. C. Blum and K. Socha (2005), “Training feed-forward neural networks with ant colony optimization: An application to pattern classification,” pp. 233–238. C. Blum and K. Socha (2005), “Training feed-forward neural networks with ant colony optimization: An application to pattern classification,” pp. 233–238. D. Karaboga and C. Ozturk (2009), “Neural networks training by artificial bee colony algorithm on pattern classification,” Neural Nerwork World, vol. 19(3), pp. 279–292. D. Karaboga and C. Ozturk (2011), “A novel clustering approach: Artificial bee colony (abc) algorithm,” Applied Soft Computing, vol. 11, no. 1, pp. 652–657. D. Karaboga, B. Akay, and C. Ozturk (2007), Modeling Decisions for Artificial Intelligence, ser. LNCS. Springer- Verlag, vol. 4617/2007, ch. Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed- Forward Neural Networks, pp. 318–329. D. Rumelhart, G. Hinton, and R. Williams (1986), “Learning representations by backpropagation errors,” Nature, vol. 323, pp. 533–536. E. Alba and J. Chicano (2004), Training Neural Networks with GA Hybrid Algorithms, ser. Proc. of Gecco, LNCS. Springer-Verlag, pp. 852–863. Hagan, M.T., and M. Menhaj (1994), "Training feed-forward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, Vol. 5, No. 6, pp. 989–993. J. Dayhoff (1990), Neural-Network Architectures: An Introduction. NewYork: Van Nostrand Reinhold. J. Zhang, J. Zhang, T. Lok, and M. Lyu (2007), “A hybrid particle swarm optimization backpropagation algorithm for feedforward neural network training,” Applied Mathematics and Computation, vol. 185, pp. 1026–1037. K. Mehrotra, C. Mohan and S. Ranka (1997), Elements of Artificial Neural Networks. Cambridge, MA: MIT Press Kenneth Levenberg (1944). “A Method for the Solution of Certain Non-Linear Problems in Least Squares”. Quarterly of Applied Mathematics 2: 164–168. L. Wang, Y. Zeng, C. Gui, and H. Wang (2007), “Application of artificial neural network supported by bp and particle swarm optimization algorithm for evaluating the criticality class of spare parts,” in Third International Conference on Natural Computation (ICNC 2007), Haikou, China, August 24-27. M. Carvalho and T. Ludermir (2007), “Hybrid Training of Feed-Forward Neural Networks with Particle Swarm Optimization”, ser. LNCS. Springer-Verlag, vol. 4233, pp. 1061–1070. N. Treadgold and T. Gedeon (1998), “Simulated annealing and weight decay in adaptive learning: the sarprop algorithm,” IEEE Transactions on Neural Networks, vol. 9, pp. 662–668. R. Sexton, B. Alidaee, R. Dorsey, and J. Johnson (1998), “Global optimization for artificial neural networks: a tabu search application,” European Journal of Operational Research, vol. 106, pp. 570–584. R. Sexton, R. Dorsey, and J. Johnson (1999), “Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing,” European Journal of Operational Research, vol. 114, pp. 589– 601. S. Haykin (1999), Neural Networks a Comprehensive Foundation. Prentice Hall, New Jersey. T. Back and H. P. Schwefel (1993), “An overview of evolutionary algorithms for parameter optimization,” Evolutionary Computation, vol. 1, no. 1, pp. 1–23. T. Ludermir, A. Yamazaki, and C. Zanchetin (2006), “An optimization methodology for neural network weights and architectures,” IEEE Transactions on Neural Networks, vol. 17(5), pp. 1452–1460. X. Yao (1999), “Evolving artificial neural networks,” in Proceeedings of the IEEE, vol. 87(9), pp. 1423–1447. X.S. Yang and S. Deb (2009), “Cuckoo search via Levy flights”, in: Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), December 2009, India, IEEE Publications, USA, pp. 210- 214. X.S. Yang and S. Deb (2010),”Engineering optimization by cuckoo search”, Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, 330-343. | en_US |
