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

• Autor(a) principal: Gomes, Vitoria Zanon [UNESP]
• Data de Publicação: 2022
• Outros Autores: Andrade, Matheus Carreira [UNESP], Amorim, Anderson Rici [UNESP], Zafalon, Geraldo Francisco Donega [UNESP], Filipe, J., Smialek, M., Brodsky, A., Hammoudi, S.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.5220/0011082900003179; http://hdl.handle.net/11449/237766
• topic: Bioinformatics; Multiple Sequence Alignment; Genetic Algorithm; Hybrid Multiple Sequence Alignment

The 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.