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C. FERREIRA 9th Online World Conference on Soft Computing in Industrial Applications, 2004 Designing Neural Networks Using Gene Expression Programming Bibliography   Anderson, J. A., An Introduction to Neural Networks, MIT Press, 1995. Angeline, P. J., G. M. Saunders, and J. B. Pollack, 1993. An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5: 54-65. Braun, H. and J. Weisbrod. Evolving feedforward neural networks. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms. Innsbruck, Springer-Verlag, 1993. Dasgupta, D. and D. McGregor. Designing application-specific neural networks using the structured genetic algorithm. In Proceedings of the International Conference on Combinations of Genetic Algorithms and Artificial Neural Networks, pp. 87-96, 1992. Ferreira, C., 2001. Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13 (2): 87-129. Ferreira, C., 2002. Genetic representation and genetic neutrality in gene expression programming. Advances in Complex Systems, 5 (4): 389-408. Ferreira, C. Function finding and the creation of numerical constants in gene expression programming. In J. M. Benitez, O. Cordon, F. Hoffmann, and R. Roy, eds., Advances in Soft Computing: Engineering Design and Manufacturing, pages 257-266, Springer-Verlag, 2003. Gruau, F., D. Whitley, and L. Pyeatt. A comparison between cellular encoding and direct encoding for genetic neural networks. In J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pp. 81-89, Cambridge, MA, MIT Press, 1996. Koza, J. R. and J. P. Rice. Genetic generation of both the weights and architecture for a neural network. In Proceedings of the International Joint Conference on Neural Networks, Volume II, IEEE Press, 1991. Lee, C.-H. and J.-H. Kim. Evolutionary ordered neural network with a linked-list encoding scheme. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 665-669, 1996. Mandischer, M. Representation and evolution of neural networks. In R. F. Albrecht, C. R. Reeves, and U. C. Steele, editors, Artificial Neural Nets and Genetic Algorithms, pp. 643-649, Springer Verlag, 1993. Maniezzo, V., 1994. Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks, 5 (1): 39-53. Opitz, D. W. and J. W. Shavlik, 1997. Connectionist theory refinement: Genetically searching the space of network topologies. Journal of Artificial Intelligence Research, 6: 177-209. Pujol, J. C. F. and R. Poli, 1998. Evolving the topology and the weights of neural networks using a dual representation. Applied Intelligence Journal, Special Issue on Evolutionary Learning, 8(1): 73-84. Yao, X. and Y. Liu, 1996. Towards designing artificial neural networks by evolution. Applied Mathematics and Computation, 91(1): 83-90. Zhang, B.-T. and H. Muhlenbein, 1993. Evolving optimal neural networks using genetic algorithms with Occam’s razor. Complex Systems, 7: 199-220. Home | Contents | Previous