Progress in Artificial Intelligence

, Volume 6, Issue 3, pp 195–210 | Cite as

Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

  • Cristian Zambrano-Vega
  • Antonio J. NebroEmail author
  • José García-Nieto
  • José F. Aldana-Montes
Regular Paper


Multiple sequence alignment (MSA) is an optimization problem consisting in finding the best alignment of more than two biological sequences according to a number of scores or objectives. In this paper, we consider a three-objective formulation of MSA, which includes the STRIKE score, the percentage of aligned columns, and the percentage of non-gap symbols. The two last objectives introduce many plateaus in the search space, thus increasing the complexity of the problem. By taking as benchmark the BAliBASE data set, we carry out a rigorous comparative study by using four multi-objective metaheuristics, including the classical NSGA-II evolutionary algorithm and the more recent ones MOCell, GWASF-GA, and NSGA-III. Our study concludes that NSGA-II provides the best overall performance.


Multiple sequence alignment Multi-objective optimization Metaheuristics Comparative study 



The first author acknowledges Universidad Técnica Estatal de Quevedo (Ecuador) for supporting his doctoral stays at Departamento de Lenguajes y Ciencias de la Computación of Universidad de Málaga (Spain).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Cristian Zambrano-Vega
    • 1
  • Antonio J. Nebro
    • 2
    Email author
  • José García-Nieto
    • 2
  • José F. Aldana-Montes
    • 2
  1. 1.Ingeniería en SistemasUniversidad Técnica Estatal de QuevedoQuevedoEcuador
  2. 2.Departamento de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain

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