1. Anderson, J.A.: A simple neural network generating an interactive memory. Mathematical Biosciences 14, 197–220 (1972)

   Article MATH  Google Scholar 

2. Garro, B.A., Sossa, H., Vázquez, R.A.: Design of Artificial Neural Networks using a Modified Particle Swarm Optimization Algorithm. In: International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, GE, USA, June 14-19, pp. 938–945 (2009)

   Google Scholar 

3. Garro, B.A., Sossa, H., Vázquez, R.A.: Design of Artificial Neural Networks Using Differential Evolution Algorithm. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part II. LNCS, vol. 6444, pp. 201–208. Springer, Heidelberg (2010)

   Chapter  Google Scholar 

4. Garro, B.A., Sossa, H., Vázquez, R.A.: Evolving Neural Networks: A Comparison between Differential Evolution and Particle Swarm Optimization. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part I. LNCS, vol. 6728, pp. 447–454. Springer, Heidelberg (2011)

   Chapter  Google Scholar 

5. Garro, B.A., Sossa, H., Vázquez, R.A.: Artificial Neural Network Synthesis by means of Artificial Bee Colony (ABC) Algorithm. In: CEC 2011, New Orleans, June 5-8 (2011)

   Google Scholar 

6. Garro, B.A., Sossa, H., Vázquez, R.A.: Back-Propagation vs Particle Swarm Optimization Algorithm: which Algorithm is better to adjust the Synaptic Weights of a Feed-Forward ANN? International Journal of Artificial Intelligence 7(11), 208–218 (2011)

   Google Scholar 

7. Gurney, K.: An Introduction to Neural Networks. Taylor and Francis Group (1997)

   Google Scholar 

8. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)

   Article MathSciNet MATH  Google Scholar 

9. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)

   Google Scholar 

10. Kohonen, T.: Correlation matrix memories. IEEE Transactions on Computers C-21(4), 353–359 (1972)

    Article  Google Scholar 

11. Koza, J.R.: Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems, Stanford University Computer Science Department technical report (1990)

    Google Scholar 

12. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)

    Google Scholar 

13. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press (1994)

    Google Scholar 

14. Koza, J.R., Bennett, F.H., Andre, D., Keane, M.A.: Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann (1999)

    Google Scholar 

15. McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 7, 115–133 (1943)

    Article MathSciNet  Google Scholar 

16. Murphy, P.M., Aha, D.W.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA, US., Technical Report (1994)

    Google Scholar 

17. Ritter, G., Dìaz, J.: Morphological associative memories. IEEE Transactions on Neural Networks 9(2), 281–293 (1998)

    Article  Google Scholar 

18. Rosenblatt, F.: The Perceptron-a perceiving and recognizing automaton. Report 85-460-1, Cornell Aeronautical Laboratory (1957)

    Google Scholar 

19. Rojas, R.: Neural networks - A systematic introduction. Chapter 7: The back propagation algorithm (1996)

    Google Scholar 

20. Steinbuch, K.: Die Lernmatrix. Kybernetik 1(1), 36–45 (1961)

    Article MATH  Google Scholar 

21. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)

    Article MathSciNet MATH  Google Scholar 

22. Vazquez, R.A.: Izhikevich Neuron Model and its Application in Pattern Recognition. Australian Journal of Intelligent Information Processing Systems 11(1), 53–60 (2010)

    Google Scholar 

23. Vázquez, R.A.: Pattern Recognition Using Spiking Neurons and Firing Rates. In: Kuri-Morales, A., Simari, G.R. (eds.) IBERAMIA 2010. LNCS, vol. 6433, pp. 423–432. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

24. Vazquez, R.A.: A computational approach for modelling the biological olfactory system during an odour discrimination task using spiking neuron. BMC Neuroscience 12(supp.1), 360 (2011)

    Article  Google Scholar 

25. Vázquez, R.A., Garro, B.A.: Training Spiking Neurons by Means of Particle Swarm Optimization. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part I. LNCS, vol. 6728, pp. 242–249. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

26. Villegas, J., Sossa, H., Avilés, C., Olague, G.: Automatic Synthesis of Associative Memories by genetic Programming, a First Approach. Research in Computing Science 42, 91–102 (2009)

    Google Scholar 

27. Villegas, J., Sossa, H., Avilés, C., Olague, G.: Automatic Synthesis of Associative Memories through Genetic Programming: a co-evolutionary approach. Revista Mexicana de Fsica 57(2), 110–116 (2011)

    Google Scholar 

28. Yao, X.: A review of evolutionary artificial neural networks. Int. J. Intell. Syst. 8(4), 539–567 (1993)

    Article  Google Scholar 

29. Yao, X.: Evolutionary artificial neural networks. In: Kent, A., Williams, J.G. (eds.) Encyclopedia of Computer Science and Technology, vol. 33, pp. 137–170. Marcel Dekker, New York (1995)

    Google Scholar 

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