- author = "Yun Seog Yeun and Kyung Ho Lee and Sang Min Han and Young Soon Yang",
- title = "Smooth Fitting with a Method for Determining the Regularization Parameter under the Genetic Programming Algorithm",
- journal = "Information Sciences",
- year = "2001",
- volume = "133",
- number = "3-4",
- pages = "175--194",
- month = apr,
- email = "yeonyun@road.daejin.ac.kr",
- keywords = "genetic algorithms, genetic programming, Smooth fitting, Regularization parameter",
-
DOI = "
doi:10.1016/S0020-0255(01)00084-6",
- abstract = "This paper deals with the smooth fitting problem under the genetic programming(GP) algorithm. To reduce the computational cost required for evaluating the fitness value of GP trees, numerical weights of GP trees are estimated by adopting both linear associative memories and the Hook & Jeeves method. The quality of smooth fitting is critically dependent on the choice of the regularization parameter. So, we present a novel method for choosing the regularization parameter. Two numerical examples are given with the comparison of generalized cross-validation B-splines",
-
notes = "Euclidean norm = zero-order Tikhonov regularisation,
is not sufficient p178 uses(?) weighting based on first
derivative of evolved function but too CPU
expensive(?). LAM HJ discrepancy principle DP
cross-validation CV composite residual and smoothing
operator CRESO L-curve zero crossing ZC considered in
text but use heuristic
two test functions 2*(sin(t))**4 1.5*(exp(-30*(t-0.25)**2)+sin(pi(t-0.2))**2)
{"}..sometimes the GP tree that is discarded by the criterion proposed in this paper is far better than the tree selected as the best one.{"} p192
Information Sciences http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
