Evolving fuzzy rules: evaluation of a new approach

Authors:
Adam Ghandar;Zbigniew Michalewicz;Frank Neumann
Affiliations:
School of Computer Science, University of Adelaide, Adelaide, SA, Australia;School of Computer Science, University of Adelaide, Adelaide, SA, Australia and Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, and Polish-Japanese Institute of Informat ...;Max-Planck-Institut für Informatik, Saarbrücken, Germany
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

Evolutionary algorithms have been successfully applied to optimize the rulebase of fuzzy systems. This has lead to powerful automated systems for financial applications. We experimentally evaluate the approach of learning fuzzy rules by evolutionary algorithms proposed by Kroeske et al. [10]. The results presented in this paper show that the optimization of fuzzy rules may be universally simplified regardless of the complex fitness surface for the overall optimization process. We incorporate a local search procedure that makes use of these theoretical results into an evolutionary algorithms for rule-base optimization. Our experimental results show that this improves a state of the art approach for financial applications.