Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems
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Materia
Fuzzy rule-based systems Approximate Mamdani-type knowledge bases Genetic fuzzy rule-based systems Genetic algorithms Evolution strategies Niching Inductive learning
Date
2001Referencia bibliográfica
Cordon, O., & Herrera, F. (2001). Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems. Fuzzy Sets and Systems, 118(2), 235-255. [doi: 10.1016/S0165-0114(98)00349-2]
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.