Learning Fuzzy Rules with Evolutionary Algorithms -- An Analytic Approach

Authors:
Jens Kroeske;Adam Ghandar;Zbigniew Michalewicz;Frank Neumann
Affiliations:
School of Mathematics, University of Adelaide, Adelaide, Australia SA 5005;School of Computer Science, University of Adelaide, Adelaide, Australia SA 5005;School of Computer Science, University of Adelaide, Adelaide, Australia SA 5005 and Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland 01-237 and Polish-Japanese Institute o ...;Max-Planck-Institut für Informatik, 66123 Saarbrücken, Germany
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

Citing 10
Cited 1

Why triangular membership functions?

Fuzzy Sets and Systems
Learning feed-forward and recurrent fuzzy systems: a genetic approach

Journal of Systems Architecture: the EUROMICRO Journal - Special issue on evolutionary computing
A Genetic Approach for Simultaneous Design of Membership Functions and Fuzzy Control Rules

Journal of Intelligent and Robotic Systems
Solving Underdefined Systems of Multivariate Quadratic Equations

PKC '02 Proceedings of the 5th International Workshop on Practice and Theory in Public Key Cryptosystems: Public Key Cryptography
Evolving fuzzy systems

Journal of Computing Sciences in Colleges
Case Study: An Intelligent Decision-Support System

IEEE Intelligent Systems
Evolutionary learning of a fuzzy controller for wall-following behavior in mobile robotics

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Computational intelligence for evolving trading rules

IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Design of hierarchical fuzzy model for classification problem using GAs

Computers and Industrial Engineering
Coevolutionary optimization of fuzzy logic intelligence for strategic decision support

IEEE Transactions on Evolutionary Computation

Evolving fuzzy rules: evaluation of a new approach

SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper provides an analytical approach to fuzzy rule base optimization. While most research in the area has been done experimentally, our theoretical considerations give new insights to the task. Using the symmetry that is inherent in our formulation, we show that the problem of finding an optimal rule base can be reduced to solving a set of quadratic equations that generically have a one dimensional solution space. This alternate problem specification can enable new approaches for rule base optimization.