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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan / Hassani-Pak, Keywan


CiteScore 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
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Volume 8, Issue 3

Issues

Parallel Niche Pareto AlineaGA – an Evolutionary Multiobjective approach on Multiple Sequence Alignment

Fernando José Mateus da Silva
  • Corresponding author
  • School of Technology and Management, Computer Science and Communication Research Centre, Polytechnic Institute of Leiria, Leiria, Portugal
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/ Juan Manuel Sánchez Pérez
  • Dept. Tecnologías Computadores y Comunicaciones, Escuela Politécnica, Universidad de Extremadura, Cáceres, Spain
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/ Juan Antonio Gómez Pulido
  • Dept. Tecnologías Computadores y Comunicaciones, Escuela Politécnica, Universidad de Extremadura, Cáceres, Spain
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/ Miguel A. Vega Rodríguez
  • Dept. Tecnologías Computadores y Comunicaciones, Escuela Politécnica, Universidad de Extremadura, Cáceres, Spain
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  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2011-174

Summary

Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. 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 uses a Parallel Genetic Algorithm for performing multiple sequence alignment. We now present the Parallel Niche Pareto AlineaGA, a multiobjective version of Parallel AlineaGA.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.

About the article

Published Online: 2016-10-18

Published in Print: 2011-12-01


Citation Information: Journal of Integrative Bioinformatics, Volume 8, Issue 3, Pages 57–72, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2011-174.

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© 2011 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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