Evolving fuzzy rule based controllers using genetic algorithms

doi:10.1016/0165-0114(95)00196-4

1996-06-01

164

Carse, B *

Fogarty, TC

Munro, A

DOI：10.1016/0165-0114(95)00196-4

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摘要

En 中文

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-FCS1 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.

Keyword:

artificial intelligence

engineering

control theory

evolutionary computation

genetic algorithms