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Optimizing Multiple Sequence Alignment by Improving Mutation Operators of a Genetic Algorithm

2009-12Conference paper Open access
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dc.contributor.authorSilva, Fernando José Mateus da
dc.contributor.authorPérez, Juan Manuel Sánchez
dc.contributor.authorPulido, Juan Antonio Gómez
dc.contributor.authorRodríguez, Miguel A. Vega
dc.contributor.authorSilva, Fernando
dc.contributor.editor
dc.date.accessioned2025-04-14T14:55:42Z
dc.date.available2025-04-14T14:55:42Z
dc.date.issued2009-12
dc.description.abstractSearching for the best possible alignment for a set of sequences is not an easy task, mainly because of the size and complexity of the search space involved. Genetic algorithms are predisposed for optimizing general combinatorial problems in large and complex search spaces. We have designed a Genetic Algorithm for this purpose, AlineaGA, which introduced new mutation operators with local search optimization. Now we present the contribution that these new operators bring to this field, comparing them with similar versions present in the literature that do not use local search mechanisms. For this purpose, we have tested different configurations of mutation operators in eight BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. We conclude that the new operators represent an improvement in this area, and that their combined use with mutation operators that do not use optimization strategies, can help the algorithm to reach quality solutions.eng
dc.identifier.citationF. J. M. da Silva, J. M. S. Pérez, J. A. G. Pulido and M. A. V. Rodríguez, "Optimizing Multiple Sequence Alignment by Improving Mutation Operators of a Genetic Algorithm," 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy, 2009, pp. 1257-1262, doi: 10.1109/ISDA.2009.106.
dc.identifier.doi10.1109/isda.2009.106
dc.identifier.eissn2164-7151
dc.identifier.isbn978-1-4244-4735-0
dc.identifier.issn2164-7143
dc.identifier.urihttp://hdl.handle.net/10400.8/12794
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/5364001
dc.relation.ispartof2009 Ninth International Conference on Intelligent Systems Design and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMultiple sequence alignments
dc.subjectgenetic algorithms
dc.subjectlocal search
dc.subjectoptimization
dc.subjectbioinformatics
dc.titleOptimizing Multiple Sequence Alignment by Improving Mutation Operators of a Genetic Algorithmeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2009-11
oaire.citation.conferencePlacePisa, Italy
oaire.citation.endPage1262
oaire.citation.startPage1257
oaire.citation.title9th International Conference on Intelligent Systems Design and Applications, ISDA 2009
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSilva
person.givenNameFernando
person.identifier.ciencia-id9D19-84F9-F1CA
person.identifier.orcid0000-0001-9335-1851
person.identifier.scopus-author-id24402946400
relation.isAuthorOfPublication2db213d9-a071-4f43-9544-1295ebb6ffde
relation.isAuthorOfPublication.latestForDiscovery2db213d9-a071-4f43-9544-1295ebb6ffde

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Searching for the best possible alignment for a set of sequences is not an easy task, mainly because of the size and complexity of the search space involved. Genetic algorithms are predisposed for optimizing general combinatorial problems in large and complex search spaces. We have designed a Genetic Algorithm for this purpose, AlineaGA, which introduced new mutation operators with local search optimization. Now we present the contribution that these new operators bring to this field, comparing them with similar versions present in the literature that do not use local search mechanisms. For this purpose, we have tested different configurations of mutation operators in eight BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. We conclude that the new operators represent an improvement in this area, and that their combined use with mutation operators that do not use optimization strategies, can help the algorithm to reach quality solutions.
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