DECOR {ConsRank} | R Documentation |

## Differential Evolution algorithm for Median Ranking

### 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

```
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

### Examples

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

*ConsRank*version 2.1.4 Index]