Muhlenbein H. Limitations of multi-layer perception networks-steps towards genetic neural networks. Parallel Computing, 1990, 14:249 ～260.
[2]
Vittorio M. Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactons on Neural Networks, 1994, 5:39 ～53.
[3]
Yao X. A reviews of evoiutionary artificial neural networks. International Journal of Intelligent Systems, 1993, 8:539～ 567.
[4]
Bomholdt S, Graudenz D. General asymmetric neural networks and structure design by genetic algorithms. Neural Networks, 1992, 5:327 ～ 334.
[5]
Solis F J, Wets J B. Minimization by random senrch techniques. Mathematics of Operations Research, 1981, 6:19～50.
[6]
Schraudolph N N, Belew R K. Dynamic parameter encoding for genetic algorithms. Machinn Learning, 1992, 9:9～21.
[7]
Mcdonnell J R, Waagen D. Evolving recurrent perceptrons for time-series modeling. IEEE Transactions on Neural Networks, 1994, 5:24～38.
[8]
David B F. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks, 1994, 5:3 ～ 14.
[9]
Bruce A W, Timothy D C. Evolving space-filling curves to distribute radial basis functions over an input space. IEEE Transactions on Neural Networks, 1994, 5:15～23.
[10]
Koza J R, Rice J P. Genetic generation of both the weights and architecture for a neural network. International Joint Conference on Neural Networks, IJCNN-91-Seattle, 1991:397 ～ 404.
[11]
Harp S A, Samad T, Guha A. Towards the genetic synthesis of neural networks. In: Proceedings of the Third International Conference on Genetic Algorithms, 1989.
[12]
Peter J A, Gregory M S, Jordan B P. An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 1994, 5:54～64.
