Comparing learning classifier systems and genetic programming: A case study

被引：0
|
作者：
Boullart, L [1 ]
Sette, S [1 ]
Wyns, B [1 ]
机构：
[1] Ghent Univ, Dept Text, Dept Control Engn & Automat, B-9052 Zwijnaarde, Belgium
来源：
INTELLIGENT CONTROL SYSTEMS AND SIGNAL PROCESSING 2003 | 2003年
关键词：
genetic algorithms; learning algorithms; programming approaches; rule-based systems;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Genetic Algorithms has given rise to two new fields of research where (global) optimisation is of crucial importance: 'Genetic based Machine Learning' (GBML) and 'Genetic Programming' (GP). An advanced implementation of GBML (Fuzzy Efficiency based Classifier System, FECS, developed by the authors) and Genetic Programming (as defined by Koza) are both applied to the case study 'fibre-to-yarn production process'. Results for both systems are presented and compared. Finally, the GP generated equations are transformed into rule-sets similar to those obtained from FECS. Copyright (C) 2003 IFAC
引用
收藏
页码：457 / 462
页数：6
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