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Smooth fitting with a method for determining the regularization parameter under the genetic programming algorithm
- Issue Date
- 4월-2001
- Publisher
- ELSEVIER SCIENCE INC
- Keywords
- genetic programming; smooth fitting; regularization parameter
- Citation
- INFORMATION SCIENCES, v.133, no.3-4, pp 175 - 194
- Pages
- 20
- Journal Title
- INFORMATION SCIENCES
- Volume
- 133
- Number
- 3-4
- Start Page
- 175
- End Page
- 194
- ISSN
- 0020-0255
1872-6291
- 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 (LAM) and the Hook and Jeeves (HJ) 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 (GCV) B-splines. (C) 2001 Elsevier Science Inc. All rights reserved.
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