DocumentCode
2218810
Title
A study on Genetic Programming with layered learning and incremental sampling
Author
Hien, Nguyen Thi ; Hoai, Nguyen Xuan ; McKay, Bob
Author_Institution
Le Quy Don Univ., Hanoi, Vietnam
fYear
2011
fDate
5-8 June 2011
Firstpage
1179
Lastpage
1185
Abstract
In this paper, we investigate the impact of a layered learning approach with incremental sampling on Genetic Programming (GP). The new system, called GPLL, is tested and compared with standard GP on twelve symbolic regression problems. While GPLL does not differ from standard GP on univariate target functions, it has better training efficiency on problems with bivariate targets. This indicates the potential usefulness of layered learning with incremental sampling in improving the efficiency of GP evolutionary learning.
Keywords
genetic algorithms; learning (artificial intelligence); regression analysis; GP evolutionary learning; genetic programming; incremental sampling; layered learning; symbolic regression problem; univariate target function; Accuracy; Genetic programming; Machine learning; Robustness; Testing; Training; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location
New Orleans, LA
ISSN
Pending
Print_ISBN
978-1-4244-7834-7
Type
conf
DOI
10.1109/CEC.2011.5949750
Filename
5949750
Link To Document