DocumentCode
416948
Title
Analyzing state space segmentation in learning classifier system
Author
Wada, Atsushi ; Takadama, Keiki ; Shimohara, Katsunori ; Katai, Osamu
Author_Institution
ATR, Kyoto, Japan
Volume
2
fYear
2003
fDate
4-6 Aug. 2003
Firstpage
1487
Abstract
We present an analysis on state space segmentation for the learning classifier system (LCS). An LCS model is proposed that can segment input state space into variable granularity. A preliminary experiment on a real-valued 6-multiplexor problem is conducted which result revealed that small granularity of segmentation affects the size of the classifier population by causing it to increase.
Keywords
learning (artificial intelligence); learning systems; pattern classification; state-space methods; classifier population size; learning classifier system; real valued 6-multiplexor problem; state space segmentation; variable granularity;
fLanguage
English
Publisher
ieee
Conference_Titel
SICE 2003 Annual Conference
Conference_Location
Fukui, Japan
Print_ISBN
0-7803-8352-4
Type
conf
Filename
1324191
Link To Document