Main menu
You are here
Historical Interest Only
This is a static HTML version of an old Drupal site. The site is no longer maintained and could be deleted at any point. It is only here for historical interest.
Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
23 August 2007 - 1:54pm — Jano.van.Hemert
| Title | Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems |
| Publication Type | Conference Paper |
| Year of Publication | 2001 |
| Authors | Eggermont, J, van Hemert, JI |
| Conference Name | Springer Lecture Notes on Computer Science |
| Publisher | Springer-Verlag, Berlin |
| Editor | Miller, J, Tomassini, M, Lanzi, PL, Ryan, C, Tettamanzi, AGB, Langdon, WB |
| ISBN Number | 9-783540-418993 |
| Keywords | data mining |
| Abstract | In this paper we continue our study on adaptive genetic pro-gramming. We use Stepwise Adaptation of Weights to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression prob-lems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three GP variants. |
| Full Text |
