Paper Info
Title
Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems
Abstract
Genetic algorithms and evolution strategies are combined in order to build a multi-stage hybrid evolutionary algorithm for learning constrained approximate Mamdani-type knowledge bases from examples. The genetic algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed fuzzy rule-based systems. The proposed genetic fuzzy rule-based system is used to solve an electrical engineering problem and the results obtained are compared with other methods presenting different characteristics.
Year
DOI
Venue
2001
10.1016/S0165-0114(98)00349-2
Fuzzy Sets and Systems
Keywords
Field
DocType
Fuzzy rule-based systems,Approximate Mamdani-type knowledge bases,Genetic fuzzy rule-based systems,Genetic algorithms,Evolution strategies,Niching,Inductive learning
Evolutionary algorithm,Fuzzy logic,Genetic representation,Artificial intelligence,Fuzzy control system,Knowledge base,Mathematics,Genetic algorithm,Machine learning,Fuzzy rule,Fuzzy rule based systems
Journal
Volume
Issue
ISSN
118
2
0165-0114
Citations 
PageRank 
References 
46
2.40
18
Authors
2
Name
Order
Citations
PageRank
O. Cordón1138066.74
Francisco Herrera2273911168.49