Finding Optimal Architectures and Weights for ANN: A Combined Hierarchical Approach

1. Verma, B., Ghosh, R.: A novel evolutionary neural learning algorithm. In: IEEE World Congress on Computational Intelligence, Honolulu, Hawaii, USA, pp. 1884–1889 (2002)

   Google Scholar 

2. Petridis, V., Kazarlis, S., Papaikonomu, A., Filelis, A.: A hybrid genetic algorithm for training neural network. Artificial Neural Networks 2, 953–956 (1992)

   Google Scholar 

3. Likartsis, A., Vlachavas, I., Tsoukalas, L.H.: New hybrid neural genetic methodology for improving learning. In: Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, Piscataway, NJ, USA, pp. 32–36. IEEE Press, Los Alamitos (1997)

   Chapter  Google Scholar 

4. Whitley, D., Starkweather, T., Bogart, C.: Genetic algorithms and neural networks - optimizing connections and connectivity. Parallel Computing 14, 347–361 (1990)

   Article  Google Scholar 

5. Koeppen, M., Teunis, M., Nickolay, B.: Neural network that uses evolutionary learning. In: Proceedings of the IEEE International Conference on Neural Networks, Part 5 (of 7), Piscstaway, NJ, USA, pp. 635–639. IEEE press, Los Alamitos (1994)

   Google Scholar 

6. Topchy, A.P., Lebedko, O.A.: Neural network training by means of cooperative evolutionary search, Nuclear Instruments & Methods in Physics Research. Section A: Accelerators, Spectometers, Detectors and Associated Equipment 389(1-2), 240–241 (1997)

   Google Scholar 

7. Gutierrez, G., Isasi, P., Molina, J.M., Sanchis, A., Galvan, I.M.: Evolutionary cellular configurations for designing feedforward neural network architectures. In: Mira, J., Prieto, A.G., et al. (eds.) IWANN 2001. LNCS, vol. 2084, pp. 514–521. Springer, Heidelberg (2001)

   Google Scholar 

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