A Differential Evolution Algorithm for computing the Fuzzy Variance

Luciano Stefanini.

We propose a differential evolution (DE) algorithm for the calculation of the interval and fuzzy variance. In particular, we see that the DE methods can be efficient for the fuzzy variance of a relatively high number of fuzzy data; computational results with up to 100 data show that the number of function evaluations to obtain the estimated global solutions grows less then quadratically with the number of data.

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