A Hybrid Genetic Algorithm using Progressive Alignment and Consistency based Approach for Multiple Sequence Alignments

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniv Paulista-
Autor(es): dc.creatorGomes, Vitoria Zanon-
Autor(es): dc.creatorAndrade, Matheus Carreira-
Autor(es): dc.creatorAmorim, Anderson Rici-
Autor(es): dc.creatorZafalon, Geraldo Francisco Donega-
Autor(es): dc.creatorFilipe, J.-
Autor(es): dc.creatorSmialek, M.-
Autor(es): dc.creatorBrodsky, A.-
Autor(es): dc.creatorHammoudi, S.-
Data de aceite: dc.date.accessioned2025-08-21T15:36:18Z-
Data de disponibilização: dc.date.available2025-08-21T15:36:18Z-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2022-11-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0011082900003179-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/237766-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/237766-
Descrição: dc.descriptionThe multiple sequence alignment is one of the most important tasks in bioinformatics, since it allows to analyze multiple sequences at the same time. There are many approaches for this problem such as heuristics and metaheuristics, that generally lead to great results in a plausible time, being among the most used approaches. The genetic algorithm is one of the most used methods because of its results quality, but it had a problematic disadvantage: it can be easily trapped in a local optima result, not being able to reach better alignments. In this work we propose a hybrid genetic algorithm with progressive and consistency-based methods as a way to smooth the local optima problem and improve the quality of the alignments. The obtained results show that our method was able to improve the quality of AG results 2 a 27 times, smoothing the local maximum problem and providing results with more biological significance.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionUniversidade Paulista (Unip/ICET)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionUniv Estadual Paulista UNESP, Dept Comp Sci & Stat, Rua Cristovelo Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionUniv Sao Paulo, Dept Comp & Digital Syst Engn, Escola Politecn, Av Prof Luciano Gualberto,Travessa 3,158 Butanta, BR-05508010 Sao Paulo, SP, Brazil-
Descrição: dc.descriptionUniv Paulista, Dept ICET, Ave Presidente Juscelino Kubitschek Oliveira S-N, BR-15091450 Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista UNESP, Dept Comp Sci & Stat, Rua Cristovelo Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil-
Descrição: dc.descriptionFAPESP: 2019/00030-3-
Descrição: dc.descriptionUniversidade Paulista (Unip/ICET): 7-03-1169/2021-
Formato: dc.format167-174-
Idioma: dc.languageen-
Publicador: dc.publisherScitepress-
Relação: dc.relationIceis: Proceedings Of The 24th International Conference On Enterprise Information Systems - Vol 2-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectBioinformatics-
Palavras-chave: dc.subjectMultiple Sequence Alignment-
Palavras-chave: dc.subjectGenetic Algorithm-
Palavras-chave: dc.subjectHybrid Multiple Sequence Alignment-
Título: dc.titleA Hybrid Genetic Algorithm using Progressive Alignment and Consistency based Approach for Multiple Sequence Alignments-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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