Paper Info
Title
Evolving fuzzy rule based controllers using genetic algorithms
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.
Year
DOI
Venue
1996
10.1016/0165-0114(95)00196-4
Fuzzy Sets and Systems
Keywords
Field
DocType
artificial intelligence,engineering,genetic algorithms,evolutionary computation,control theory,genetic algorithm,fuzzy rule,computer network,artificial intelligent,fuzzy set,learning classifier system,dynamic routing,evolutionary computing,membership function,fuzzy logic
Neuro-fuzzy,Defuzzification,Fuzzy classification,Fuzzy set operations,Computer science,Fuzzy logic,Artificial intelligence,Fuzzy number,Membership function,Machine learning,Fuzzy rule
Journal
Volume
Issue
ISSN
80
3
Fuzzy Sets and Systems
Citations 
PageRank 
References 
111
9.50
15
Authors
3
Search Limit
100111
Name
Order
Citations
PageRank
Brian Carse125926.31
T C Fogarty21147152.53
Alistair Munro316618.26