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Authors: Athanasios Tsakonas and Bogdan Gabrys

Affiliation: Bournemouth University, United Kingdom

ISBN: 978-989-8425-83-6

Keyword(s): Genetic programming, Fuzzy rule based systems, Evolutionary computation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Representation Techniques ; Soft Computing

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.

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Paper citation in several formats:
Tsakonas, A. and Gabrys, B. (2011). EVOLVING TAKAGI-SUGENO-KANG FUZZY SYSTEMS USING MULTI POPULATION GRAMMAR-GUIDED GENETIC PROGRAMMING.In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 278-281. DOI: 10.5220/0003637702780281

@conference{ecta11,
author={Athanasios Tsakonas. and Bogdan Gabrys.},
title={EVOLVING TAKAGI-SUGENO-KANG FUZZY SYSTEMS USING MULTI POPULATION GRAMMAR-GUIDED GENETIC PROGRAMMING},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)},
year={2011},
pages={278-281},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003637702780281},
isbn={978-989-8425-83-6},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2011)
TI - EVOLVING TAKAGI-SUGENO-KANG FUZZY SYSTEMS USING MULTI POPULATION GRAMMAR-GUIDED GENETIC PROGRAMMING
SN - 978-989-8425-83-6
AU - Tsakonas, A.
AU - Gabrys, B.
PY - 2011
SP - 278
EP - 281
DO - 10.5220/0003637702780281

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