@inproceedings{0e4e3939788447b68e8e97b3f180c287,
title = "Adaptive Splines and Genetic Algorithms",
abstract = "An adaptive genetic spline (AGS) technique was proposed for fitting adaptive splines via adjusted generalized cross-validation (GCV) and genetic search. The AGS program was designed to determine the model from the space S m,t which minimizes an adjusted GCV criterion. Simulations were used to examine the performance of AGS. AGS was set to fit cubic splines with a maximum of 400 generations and a crossover probability of 0.8. It was shown that AGS performed well, due to the directed global selection of basis functions.",
author = "Jennifer Pittman",
year = "2000",
language = "English (US)",
isbn = "0964345692",
series = "Proceedings of the Joint Conference on Information Sciences",
number = "1",
pages = "547--550",
editor = "P.P. Wang and P.P. Wang",
booktitle = "Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 1",
edition = "1",
note = "Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 ; Conference date: 27-02-2000 Through 03-03-2000",
}