Identification of fuzzy measures from sample data with genetic algorithms

Identification of fuzzy measures from sample data with genetic algorithms

0.00 Avg rating0 Votes
Article ID: iaor20072538
Country: United Kingdom
Volume: 33
Issue: 10
Start Page Number: 3046
End Page Number: 3066
Publication Date: Oct 2006
Journal: Computers and Operations Research
Authors: ,
Keywords: heuristics: genetic algorithms
Abstract:

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.

Reviews

Required fields are marked *. Your email address will not be published.