Identification of λ-fuzzy Measure by Modified Genetic Algorithms

Fuzzy measure is subjective scale for the degrees of fuzziness and suitable for analyzing human subjective evaluation processes. It is not easy to provide consistent fuzzy measure values with fuzzy measure properties since they have to be subjective determined. Thus it induces an identification prob...

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Bibliographic Details
Main Authors: Chuanjun Zhu, Yurong Chen, Xinhai Lu, Chaoyong Zhang
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Anthropometry
Automotive engineering
Biological cells
Encoding
fuzzy measure
Fuzzy measure identification
Fuzzy sets
Fuzzy systems
Genetic algorithms
Genetic mutations
Mechanical variables measurement
Modified genetic algorithms
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Summary:Fuzzy measure is subjective scale for the degrees of fuzziness and suitable for analyzing human subjective evaluation processes. It is not easy to provide consistent fuzzy measure values with fuzzy measure properties since they have to be subjective determined. Thus it induces an identification problem that determines measure values with fuzzy measure properties from human-provided. The λ-fuzzy measure is a typical fuzzy measure widely used. Although several studies have been made on λ-fuzzy measure identification, the corresponding computation process is rather complicated and the result is not ideal. In this paper, we introduce a method for identification of ¿-fuzzy measures from data set. It is implemented by using modified genetic algorithm and example data is tested, the result shows its applicability.
DOI:10.1109/FSKD.2009.383