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Evolving fuzzy rule based controllers using genetic algorithms |
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| Authors: | Brian Carse Terence C. Fogarty Alistair Munro |
| Affiliation: | a Intelligent Autonomous Systems Laboratory, Faculty of Engineering, University of the West of England, Bristol, Coldharbour Lane, Frenchay, Bristol BS16 IQY, UK b Bristol Transputer Centre, Faculty of Computer Studies and Mathematics, University of the West of England, Bristol, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK c Centre for Communications Research, Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1TR, UK |
| Abstract: | The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (P-FCS1) is proposed. P-FCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rule-sets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using P-FCS 1 are reported and compared with other published results. Application of P-FCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented. |
| Keywords: | Artificial Intelligence Engineering Control theory Evolutionary computation Genetic algorithms |
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