Adaptive splines and genetic algorithms

Jennifer Pittman

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


Most existing algorithms for fitting adaptive splines are based on nonlinear optimization and/or stepwise selection. Stepwise knot selection, although computationally fast, is necessarily suboptimal while determining the best model over the space of adaptive knot splines is a very poorly behaved nonlinear optimization problem. A possible alternative is to use a genetic algorithm to perform knot selection. An adaptive modeling technique referred to as adaptive genetic splines (AGS) is introduced which combines the optimization power of a genetic algorithm with the flexibility of polynomial splines. Preliminary simulation results comparing the performance of AGS to those of existing methods such as HAS, SUREshrink and automatic Bayesian curve fitting are discussed. A real data example involving the application of these methods to a fMRI dataset is presented.

Original languageEnglish (US)
Pages (from-to)615-638
Number of pages24
JournalJournal of Computational and Graphical Statistics
Issue number3
StatePublished - Sep 2002
Externally publishedYes


  • Generalized cross-validation
  • Nonparametric regression
  • Splines

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty


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