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A niched pareto genetic algorithm: For multiple sequence alignment optimization

2010-01Conference paper Open access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
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.date.accessioned2025-11-07T15:55:22Z
dc.date.available2025-11-07T15:55:22Z
dc.date.issued2010-01
dc.descriptionConference name - 2nd International Conference on Agents and Artificial Intelligence, ICAART 2010; Conference date - 22 January 2010 - 24 January 2010; Conference code - 81591
dc.descriptionFontes: https://scholar.google.com/scholar?q=A%20niched%20pareto%20genetic%20algorithm%3A%20For%20multiple%20sequence%20alignment%20optimization https://www.researchgate.net/publication/221539748_A_Niched_Pareto_Genetic_Algorithm_-_For_Multiple_Sequence_Alignment_Optimization
dc.description.abstractThe alignment of molecular sequences is a recurring task in bioinformatics, but it is not a trivial problem. The size and complexity of the search space involved difficult the task of finding the optimal alignment of a set of sequences. Due to its adaptive capacity in large and complex spaces, Genetic Algorithms emerge as good candidates for this problem. Although they are often used in single objective domains, its use in multidimensional problems allows finding a set of solutions which provide the best possible optimization of the objectives - the Pareto front. Niching methods, such as sharing, distribute these solutions in space, maximizing their diversity along the front. We present a niched Pareto Genetic Algorithm for sequence alignment which we have tested with six BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. Whereas methods for finding the best alignment are mathematical, not biological, having a set of solutions which facilitate experts' choice, is a possibility to consider.eng
dc.identifier.citationSilva, Fernando & Sánchez-Pérez, Juan & Gómez-Pulido, Juan A. & Vega-Rodríguez, Miguel A.. (2010). A Niched Pareto Genetic Algorithm - For Multiple Sequence Alignment Optimization. ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings. 1. 323-329. DOI: https://doi.org/10.5220/0002729303230329.
dc.identifier.doi10.5220/0002729303230329
dc.identifier.isbn978-989674022-1
dc.identifier.isbn978-989674021-4
dc.identifier.urihttp://hdl.handle.net/10400.8/14566
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSciTePress
dc.relation.ispartofProceedings of the 2nd International Conference on Agents and Artificial Intelligence
dc.rights.uriN/A
dc.subjectMultiple sequence alignments
dc.subjectGenetic algorithms
dc.subjectMultiobjective optimization
dc.subjectNiched Pareto
dc.subjectEquivalence class sharing
dc.subjectBioinformatics
dc.titleA niched pareto genetic algorithm: For multiple sequence alignment optimizationeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2010-01
oaire.citation.conferencePlaceValencia, Spain
oaire.citation.endPage329
oaire.citation.startPage323
oaire.citation.titleICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
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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The alignment of molecular sequences is a recurring task in bioinformatics, but it is not a trivial problem. The size and complexity of the search space involved difficult the task of finding the optimal alignment of a set of sequences. Due to its adaptive capacity in large and complex spaces, Genetic Algorithms emerge as good candidates for this problem. Although they are often used in single objective domains, its use in multidimensional problems allows finding a set of solutions which provide the best possible optimization of the objectives - the Pareto front. Niching methods, such as sharing, distribute these solutions in space, maximizing their diversity along the front. We present a niched Pareto Genetic Algorithm for sequence alignment which we have tested with six BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. Whereas methods for finding the best alignment are mathematical, not biological, having a set of solutions which facilitate experts' choice, is a possibility to consider.
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