evolutionary computation, neural networks, training sets
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). All Faculty Publications. 1149.
Physical and Mathematical Sciences
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