Keywords
evolutionary computation, neural networks, training sets
Abstract
Training Set Evolution is an eclectic optimization technique that combines evolutionary computation (EC) with neural networks (NN). The synthesis of EC with NN provides both initial unsupervised random exploration of the solution space as well as supervised generalization on those initial solutions. An assimilation of a large amount of data obtained over many simulations provides encouraging empirical evidence for the robustness of Evolutionary Training Sets as an optimization technique for feedback and control problems.
Original Publication Citation
Ventura, D. and Martinez, T. R., "Robust Optimization Using Training Set Evolution", Proceedings of the ICNN'96 IEEE International Conference on Neural Networks, pp. 524-528, 1996.
BYU ScholarsArchive Citation
Martinez, Tony R. and Ventura, Dan A., "Robust Optimization Using Training Set Evolution" (1996). Faculty Publications. 1149.
https://scholarsarchive.byu.edu/facpub/1149
Document Type
Peer-Reviewed Article
Publication Date
1996-06-06
Permanent URL
http://hdl.lib.byu.edu/1877/2438
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
http://lib.byu.edu/about/copyright/