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dc.contributor.authorArza E.en_US
dc.contributor.authorPérez A.en_US
dc.contributor.authorIrurozki E.en_US
dc.contributor.authorCeberio J.en_US
dc.date.accessioned2020-07-26T14:53:56Z
dc.date.available2020-07-26T14:53:56Z
dc.date.issued2020-07
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1138
dc.description.abstractThe Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard problem represents, it has captured the attention of the optimization community for decades. As a result, a large number of algorithms have been proposed to tackle this problem. Among these, exact methods are only able to solve instances of size $n<40$. To overcome this limitation, many metaheuristic methods have been applied to the QAP. In this work, we follow this direction by approaching the QAP through Estimation of Distribution Algorithms (EDAs). Particularly, a non-parametric distance-based exponential probabilistic model is used. Based on the analysis of the characteristics of the QAP, and previous work in the area, we introduce Kernels of Mallows Model under the Hamming distance to the context of EDAs. Conducted experiments point out that the performance of the proposed algorithm in the QAP is superior to (i) the classical EDAs adapted to deal with the QAP, and also (ii) to the specific EDAs proposed in the literature to deal with permutation problems.en_US
dc.description.sponsorshipSevero Ochoa SEV-2013-0323 TIN2016-78365-R PID2019-106453GAI00 SVP-2014-068574 TIN2017-82626-Ren_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.publisherSwarm and Evolutionary Computationen_US
dc.relationES/1PE/TIN2017-82626-Ren_US
dc.relationEUS/BERC/BERC.2018-2021en_US
dc.relationEUS/ELKARTEKen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectEstimation of Distribution Algorithmen_US
dc.subjectQuadratic Assignment Problemen_US
dc.subjectHamming distanceen_US
dc.subjectMallows Modelen_US
dc.titleKernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problemen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeinfo:eu-repo/semantics/acceptedVersionen_US
dc.identifier.doi10.1016/j.swevo.2020.100740
dc.relation.publisherversionhttps://doi.org/10.1016/j.swevo.2020.100740en_US


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