DocumentCode
2995378
Title
Learning, evolution and generalisation
Author
Kushchu, Ibrahim
Author_Institution
GSIM, Int. Univ. of Japan, Niigata, Japan
Volume
4
fYear
2003
fDate
8-12 Dec. 2003
Firstpage
2441
Abstract
The conventional machine learning approaches can provide well established experimental basis for genetic based learners. Examination of the research imply that the link between evolutionary learning and conventional learning studies may be improved. This is especially true in terms of practices adopting generalisation as a performance evaluation criterion for learning. In this paper an overview of significant number of experiments from classifier systems and genetic programming are presented. Suggestions to accommodate generalisation in the context of evolutionary learning are provided within a generic learning framework and its implication for evolutionary generalisation is discussed.
Keywords
evolutionary computation; generalisation (artificial intelligence); learning (artificial intelligence); pattern classification; classifier systems; evolutionary learning; generalisation; genetic programming; machine learning; Artificial intelligence; Bridges; Design methodology; Genetic programming; Learning systems; Machine learning; Problem-solving; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN
0-7803-7804-0
Type
conf
DOI
10.1109/CEC.2003.1299394
Filename
1299394
Link To Document