Comparing learning classifier systems and Genetic Programming: a case study

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...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2004-03, Vol.17 (2), p.199-204
Main Authors: Sette, S., Wyns, B., Boullart, L.
Format: Article
Language:English
Subjects:
Genetic algorithms
Genetic programming
Learning classifier systems
Rule-based machine learning
Textile production process
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Summary: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.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2004.02.006