文章基本信息

标题：Improve and Compact Population in XCSFCA using Polynomial Equation
本地全文：下载
作者：Saeid Goodarzian ; Ali Hamzeh ; Sattar Hashemi 等
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：1
出版社：IJCSI Press
摘要：XCS is a rule-based evolutionary online learning system. XCSFCA is an extension of XCS where compute continuous actions directly from input states. In XCSFCA, computed actions of a classifier, demonstrated as a straight lines. But in very problems, the desired best action curves are not linear and there are arched; therefore a system with linear action computation needs a large population. This paper studies a new method for compute continuous actions directly from input states. In new proposed method action computes by polynomial equation. Consequently each classifier represents a nonlinear action curve and the classifiers are more generalized. In comparison with XCSFCA, our method proves to be more efficient and smaller population size.
关键词：XCS; XCSF; XCSFCA; continuous action; polynomial equation