Tsakonas, A. and Gabrys, B., 2011. Evolving Takagi-Sugeno-Kang fuzzy systems using multi-population grammar guided genetic programming. In: International Conference on Evolutionary Computation Theory and Applications (ECTA'11), 24-26 Oct 2011, Paris, France.
Full text available as:
|PDF (Conference Paper) - Accepted Version|
Official URL: http://www.ecta.ijcci.org/
This work proposes a novel approach for the automatic generation and tuning of complete Takagi-Sugeno-Kang fuzzy rule based systems. The examined system aims to explore the effects of a reduced search space for a genetic programming framework by means of grammar guidance that describes candidate structures of fuzzy rule based systems. The presented approach applies context-free grammars to generate individuals and evolve solutions through the search process of the algorithm. A multi-population approach is adopted for the genetic programming system, in order to increase the depth of the search process. Two candidate grammars are examined in one regression problem and one system identification task. Preliminary results are included and discussion proposes further research directions.
|Item Type:||Conference or Workshop Item (Poster)|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
|Group:||School of Design, Engineering & Computing|
|Deposited By:||Dr Athanasios Tsakonas LEFT|
|Deposited On:||09 Sep 2011 12:12|
|Last Modified:||10 Sep 2013 10:44|
Document DownloadsMore statistics for this item...
|Repository Staff Only -|
|BU Staff Only -|
|Help Guide -||Editing Your Items in BURO|