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dc.contributor.authorBlum, Christian
dc.contributor.authorBlesa Aguilera, Maria Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2019-02-14T10:35:21Z
dc.date.available2019-02-14T10:35:21Z
dc.date.issued2017
dc.identifier.citationBlum, C.; Blesa, M. "A hybrid evolutionary algorithm based on solution merging for the longest arc-preserving common subsequence problem". 2017.
dc.identifier.urihttp://hdl.handle.net/2117/129102
dc.description.abstractThe longest arc-preserving common subsequence problem is an NP-hard combinatorial optimization problem from the field of computational biology. This problem finds applications, in particular, in the comparison of arc-annotated Ribonucleic acid (RNA) sequences. In this work we propose a simple, hybrid evolutionary algorithm to tackle this problem. The most important feature of this algorithm concerns a crossover operator based on solution merging. In solution merging, two or more solutions to the problem are merged, and an exact technique is used to find the best solution within this union. It is experimentally shown that the proposed algorithm outperforms a heuristic from the literature.
dc.format.extent13 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat
dc.subject.lcshEvolutionary computation
dc.subject.lcshRNA
dc.subject.lcshComputational biology
dc.subject.otherContext
dc.subject.otherMerging
dc.subject.otherOptimized production technology
dc.subject.otherHeuristic algorithms
dc.titleA hybrid evolutionary algorithm based on solution merging for the longest arc-preserving common subsequence problem
dc.typeExternal research report
dc.subject.lemacComputació evolutiva
dc.subject.lemacBiologia computacional
dc.contributor.groupUniversitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
dc.relation.publisherversionhttps://arxiv.org/pdf/1702.00318.pdf
dc.rights.accessOpen Access
local.identifier.drac23659739
dc.description.versionPreprint
dc.relation.projectidinfo:eu-repo/grantAgreement/MICINN/TIN2012-37930-C02-02
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/V PRI 2010-2013/2014 SGR 1034
local.citation.authorBlum, C.; Blesa, M.


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