Evolving fuzzy rules: evaluation of a new approach
Date
2010
Authors
Ghandar, A.
Michalewicz, Z.
Neumann, F.
Editors
Deb, K.
Bhattacharya, A.
Chakraborti, N.
Chakroborty, P.
Das, S.
Dutta, J.
Gupta, S.K.
Jain, A.
Aggarwal, V.
Branke, J.
Louis, S.J.
Tan, K.C.
Bhattacharya, A.
Chakraborti, N.
Chakroborty, P.
Das, S.
Dutta, J.
Gupta, S.K.
Jain, A.
Aggarwal, V.
Branke, J.
Louis, S.J.
Tan, K.C.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
SEAL '10 Proceedings of 8th International Conference on Simulated Evolution and Learning (SEAL 2010), 2010: pp.250-259
Statement of Responsibility
Adam Ghandar, Zbigniew Michalewicz and Frank Neumann
Conference Name
International Conference on Simulated Evolution and Learning (2010 : India)
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. © 2010 Springer-Verlag.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2010 Springer-Verlag Berlin, Heidelberg