Using metaheuristic algorithms for parameter estimation in generalized Mallows models

•We deal with the problem of parameter estimation in Generalized Mallows model (GMM).•We deal with 22 real datasets, all of them but one created by the authors.•We have designed two experiments varying the maximum evaluations allowed.•Obtained results significantly improve the previous competing app...

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Vydané v:Applied soft computing Ročník 38; s. 308 - 320
Hlavní autori: Aledo, Juan A., Gámez, Jose A., Molina, David
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.01.2016
Predmet:
Generalized Mallows model
Metaheuristic algorithms
Parameter estimation
Search methods
Parameter estimation
Search methods
Generalized Mallows model
Metaheuristic algorithms
ISSN:1568-4946, 1872-9681
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Popis
Shrnutí:•We deal with the problem of parameter estimation in Generalized Mallows model (GMM).•We deal with 22 real datasets, all of them but one created by the authors.•We have designed two experiments varying the maximum evaluations allowed.•Obtained results significantly improve the previous competing approaches. This paper deals with the problem of parameter estimation in the generalized Mallows model (GMM) by using both local and global search metaheuristic (MH) algorithms. The task we undertake is to learn parameters for defining the GMM from a dataset of complete rankings/permutations. Several approaches can be found in the literature, some of which are based on greedy search and branch and bound search. The greedy approach has the disadvantage of usually becoming trapped in local optima, while the branch and bound approach, basically A* search, usually comes down to approximate search because of memory requirements, losing in this way its guaranteed optimality. Here, we carry out a comparative study of several MH algorithms (iterated local search (ILS) methods, variable neighborhood search (VNS) methods, genetic algorithms (GAs) and estimation of distribution algorithms (EDAs)) and a tailored algorithm A* to address parameter estimation in GMMs. We use 22 real datasets of different complexity, all but one of which were created by the authors by preprocessing real raw data. We provide a complete analysis of the experiments in terms of accuracy, number of iterations and CPU time requirements.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2015.09.050