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Approaching rank aggregation problems by using evolution strategies: The case of the optimal bucket order problem

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  • Aledo, Juan A.
  • Gámez, José A.
  • Rosete, Alejandro

Abstract

The optimal bucket order problem consists in obtaining a complete consensus ranking (ties are allowed) from a matrix of preferences (possibly obtained from a database of rankings). In this paper, we tackle this problem by using (1+λ) evolution strategies. We designed specific mutation operators which are able to modify the inner structure of the buckets, which introduces more diversity into the search process. We also study different initialization methods and strategies for the generation of the population of descendants. The proposed evolution strategies are tested using a benchmark of 52 databases and compared with the current state-of-the-art algorithm LIAGMP2. We carry out a standard machine learning statistical analysis procedure to identify a subset of outstanding configurations of the proposed evolution strategies. The study shows that the best evolution strategy improves upon the accuracy obtained by the standard greedy method (BPA) by 35%, and that of LIAGMP2 by 12.5%.

Suggested Citation

  • Aledo, Juan A. & Gámez, José A. & Rosete, Alejandro, 2018. "Approaching rank aggregation problems by using evolution strategies: The case of the optimal bucket order problem," European Journal of Operational Research, Elsevier, vol. 270(3), pages 982-998.
    • Handle: RePEc:eee:ejores:v:270:y:2018:i:3:p:982-998
    DOI: 10.1016/j.ejor.2018.04.031
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    Cited by:

    1. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.
    2. Akbari, Sina & Escobedo, Adolfo R., 2023. "Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties," Omega, Elsevier, vol. 119(C).
    3. Montes, Ignacio & Rademaker, Michael & Pérez-Fernández, Raúl & De Baets, Bernard, 2020. "A correspondence between voting procedures and stochastic orderings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 977-987.
    4. Fu, Yelin & Lu, Yihe & Yu, Chen & Lai, Kin Keung, 2022. "Inter-country comparisons of energy system performance with the energy trilemma index: An ensemble ranking methodology based on the half-quadratic theory," Energy, Elsevier, vol. 261(PA).

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