# DECOR: Differential Evolution algorithm for Median Ranking In ConsRank: Compute the Median Ranking(s) According to the Kemeny's Axiomatic Approach

## Description

Differential evolution algorithm for median ranking detection. It works with full, tied and partial rankings. The solution con be constrained to be a full ranking or a tied ranking

## Usage

 `1` ```DECOR(X, Wk = NULL, NP = 15, L = 100, FF = 0.4, CR = 0.9, FULL = FALSE) ```

## Arguments

 `X` A N by M data matrix, in which there are N judges and M objects to be judged. Each row is a ranking of the objects which are represented by the columns. Alternatively X can contain the rankings observed only once. In this case the argument Wk must be used `Wk` Optional: the frequency of each ranking in the data `NP` The number of population individuals `L` Generations limit: maximum number of consecutive generations without improvement `FF` The scaling rate for mutation. Must be in [0,1] `CR` The crossover range. Must be in [0,1] `FULL` Default FULL=FALSE. If FULL=TRUE, the searching is limited to the space of full rankings.

## Details

This function is deprecated and it will be removed in the next release of the package. Use function 'consrank' instead.

## Value

a "list" containing the following components:

 Consensus the Consensus Ranking Tau averaged TauX rank correlation coefficient Eltime Elapsed time in seconds

## Author(s)

Antonio D'Ambrosio antdambr@unina.it and Giulio Mazzeo giuliomazzeo@gmail.com

## References

D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017). A differential evolution algorithm for finding the median ranking under the Kemeny axiomatic approach. Computers and Operations Research, vol. 82, pp. 126-138.

## See Also

`consrank`

## Examples

 ```1 2 3``` ```#not run #data(EMD) #CR=DECOR(EMD[,1:15],EMD[,16]) ```

ConsRank documentation built on Sept. 28, 2021, 5:07 p.m.