(Enter summary)
Abstract: Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks
(ANNs) in which evolution is another fundamental form of adaptation in addition to learning. The
evolution in EANNs is often simulated by genetic algorithms (GAs), evolutionary programming (EP), or
other evolutionary algorithms. This paper describes an EP-based EANNs which learn both their weights
and architectures through the combination of a hybrid learning algorithm and the EP algorithm. A... (Update)
Context of citations to this paper: More
.... Neural Networks (EANNs) which adapt their architectures through simulated evolution and their weights through learning (training) [10]. Neural networks are based on local learning, while the evolutionary approach is not [11] Concerning FPGAs, this approach could be...
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BibTeX entry: (Update)
X. Yao and Y. Liu, "Evolutionary artificial neural networks that learn and generalise well," in 1996 IEEE International Conference on Neural Networks, Washington, DC, USA, Volume on Plenary, Panel and Special Sessions, pp. 159--164, IEEE Press, New York, NY, 3-6 June 1996. http://citeseer.ist.psu.edu/article/yao96evolutionary.html More
@inproceedings{ yao96evolutionary,
author = "Xin Yao and Yong Liu",
title = "Evolutionary artificial neural networks that learn and generalise well",
booktitle = "Proc. of the 1996 {IEEE} {I}nt. {C}onf. on {N}eural {N}etworks ({ICNN}'96), {V}ol. on {P}lenary, {P}anel and {S}pecial {S}essions",
address = "?",
pages = "159--164",
year = "1996",
url = "citeseer.ist.psu.edu/article/yao96evolutionary.html" }
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