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Identification of fuzzy measures from sample data with genetic algorithms

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Combarro, Elías F. y Miranda Menéndez, Pedro (2006) Identification of fuzzy measures from sample data with genetic algorithms. Computers and Operations Research, 33 (10). pp. 3046-3066. ISSN 0305-0548

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URL Oficial: http://www.sciencedirect.com/science/article/pii/S0305054805000900


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In this paper, we introduce a method for the identification of fuzzy measures from sample data. It is implemented using genetic algorithms and is flexible enough to allow the use of different subfamilies of fuzzy measures for the learning, as k-additive or p-symmetric measures. The experiments performed to test the algorithm suggest that it is robust in situations where there exists noise in the considered data. We also explore some possibilities for the choice of the initial population, which lead to the study of the extremes of some subfamilies of fuzzy measures, as well as the proposal of a method for random generation of fuzzy measures.


Tipo de documento:Artículo
Palabras clave:Genetic algorithms; Fuzzy measures; k-Additivity; p-Symmetry
Materias:Ciencias > Matemáticas > Investigación operativa
Código ID:17079
Depositado:13 Nov 2012 10:13
Última Modificación:07 Feb 2014 09:41

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