Comparing learning classifier systems and Genetic Programming: a case study

被引：5
作者：
Sette, S
Wyns, B
Boullart, L
机构：
[1] Hogesch W Vlaanderen, ICT, B-8500 Kortrijk, Belgium
[2] State Univ Ghent, Dept Elect Energy Syst & Automat, B-9052 Zwijnaarde, Belgium
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2004年 / 17卷 / 02期
关键词：
Genetic Algorithms; genetic programming; textile production process; learning classifier systems; rule-based machine learning;
D O I：
10.1016/j.engappai.2004.02.006
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 GP (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. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码：199 / 204
页数：6
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