DECOR

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

Genetic

median

differential

evolution

ranking

algorithms

Compute the median ranking according to the Kemeny's axiomatic approach.
Rankings can or cannot contain ties, rankings can be both complete or incomplete.
The package contains both branch-and-bound algorithms and heuristic solutions recently proposed.
The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties.
The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient.
This release declare as deprecated some functions that are still in the package for compatibility. Next release will not contains these functions.
Please type '?ConsRank-deprecated'
Essential references:
Emond, E.J., and Mason, D.W. (2002) <doi:10.1002/mcda.313>;
D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) <doi:10.1285/i20705948v8n2p198>;
Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) <doi:10.1016/j.ejor.2015.08.048>;
D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) <doi:10.1016/j.cor.2017.01.017>;
Albano, A., and Plaia, A. (2021) <doi:10.1285/i20705948v14n1p117>.

Antonio DAmbrosio

ConsRank

Compute the Median Ranking(s) According to the Kemeny's
Axiomatic Approach

Antonio D'Ambrosio

Sonia Amodio

Giulio Mazzeo

Alessandro Albano

Antonella Plaia

DECOR function

<dl><dt>X</dt>
<dd>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</dd>
<dt>Wk</dt>
<dd>Optional: the frequency of each ranking in the data</dd>
<dt>NP</dt>
<dd>The number of population individuals</dd>
<dt>L</dt>
<dd>Generations limit: maximum number of consecutive generations without improvement</dd>
<dt>FF</dt>
<dd>The scaling rate for mutation. Must be in [0,1]</dd>
<dt>CR</dt>
<dd>The crossover range. Must be in [0,1]</dd>
<dt>FULL</dt>
<dd>Default FULL=FALSE. If FULL=TRUE, the searching is limited to the space of full rankings.</dd></dl>

Arguments

Antonio D'Ambrosio <a href="/link/antdambr%40unina.it?package=ConsRank&version=2.1.5" data-mini-rdoc="ConsRank::antdambr@unina.it">antdambr@unina.it</a> and Giulio Mazzeo <a href="/link/giuliomazzeo%40gmail.com?package=ConsRank&version=2.1.5" data-mini-rdoc="ConsRank::giuliomazzeo@gmail.com">giuliomazzeo@gmail.com</a>

Author

Differential Evolution algorithm for Median Ranking — DECOR

<dl>

<dt>X</dt>
<dd>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</dd>


<dt>Wk</dt>
<dd>Optional: the frequency of each ranking in the data</dd>


<dt>NP</dt>
<dd>The number of population individuals</dd>


<dt>L</dt>
<dd>Generations limit: maximum number of consecutive generations without improvement</dd>


<dt>FF</dt>
<dd>The scaling rate for mutation. Must be in [0,1]</dd>


<dt>CR</dt>
<dd>The crossover range. Must be in [0,1]</dd>


<dt>FULL</dt>
<dd>Default FULL=FALSE. If FULL=TRUE, the searching is limited to the space of full rankings.</dd>

</dl>

Antonio D'Ambrosio <a href='mailto:antdambr@unina.it'>antdambr@unina.it</a> and Giulio Mazzeo <a href='mailto:giuliomazzeo@gmail.com'>giuliomazzeo@gmail.com</a>

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

DECOR: Differential Evolution algorithm for Median Ranking

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples