Evolving Takagi-Sugeno-Kang fuzzy systems using multi-population grammar guided genetic programming.

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.

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Abstract

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:Faculty of Science and Technology
ID Code:18460
Deposited By:Dr Athanasios Tsakonas LEFT
Deposited On:09 Sep 2011 12:12
Last Modified:10 Sep 2014 15:52

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