A parallel niched pareto evolutionary algorithm for multiple sequence alignment

Fernando José Mateus Da Silva, Juan Manuel Sánchez Pérez, Juan Antonio Gómez Pulido, Miguel A.Vega Rodríguez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Multiple sequence alignment is one of the most common tasks in Bioinformatics. However, there are not biologically accurate methods for performing sequence alignment. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces, such as the ones present when aligning a set of sequences. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, presenting results that are better than those provided by the sum of several sequential Genetic Algorithms. Although these methods are often used to optimize a single objective, they can also be used in multidimensional domains, finding all possible tradeoffs among multiple conflicting objectives. Parallel AlineaGA is an evolutionary algorithm which makes use of a Parallel Genetic Algorithm for performing multiple sequence alignment. We present a multiple objective approach of Parallel AlineaGA that uses a Parallel Niched Pareto Genetic Algorithm. We compare the performance of both versions using eight BAliBASE datasets. We also measure up the quality of the obtained solutions with the ones achieved by T-Coffee and ClustalW2, allowing us to observe that our algorithm reaches for better solutions in the majority of the datasets.

Original languageEnglish
Title of host publication5th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB 2011)
PublisherSpringer Verlag
Pages157-165
Number of pages9
ISBN (Print)9783642199134
DOIs
StatePublished - 2011
Externally publishedYes

Publication series

NameAdvances in Intelligent and Soft Computing
Volume93
ISSN (Print)1867-5662

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