Approaching Rank Aggregation Problems by using Evolution Strategies: the case of the Optimal Bucket Order Problem Juan A. Aledo * , Jos´ e A. G´ amez † , Alejandro Rosete ‡ Abstract The optimal bucket order problem consists in obtaining a complete consensus ranking (ties are allowed) from a matrix of preferences (pos- sibly 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 LIA MP 2 G . 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 LIA MP 2 G by 12.5%. Keywords: rank aggregation; preference learning; optimal bucket order problem; evolution estrategies; consensus ranking; metaheuristics; weak order This is the accepted version of: Juan A. Aledo, Jose A. G´ amez, Alejandro Rosete Approaching Rank Aggregation Problems by using Evolution Strategies: the case of the Optimal Bucket Order Problem European Journal of Operational Research, 270(3):982-998 (2018) https://doi.org/10.1016/j.ejor.2018.04.031 Please, visit the provided url to obtain the published version. * Department of Mathematics, University of Castilla-La Mancha [juanangel.aledo@uclm.es] † Department of Computing Systems, University of Castilla-La Mancha [jose.gamez@uclm.es] ‡ Universidad Tecnol´ogica de la Habana Jos´ e Antonio Echeverr´ ıa (Cujae) [Alejan- dro.Rosete.Suarez@gmail.com 1