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 :
بازگشت