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

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Abstract

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.

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Notes

  1. jMetalMSA project: https://github.com/jMetal/jMetalMSA.

  2. Maven project: https://maven.apache.org/.

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Acknowledgements

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).

Author information

Authors and Affiliations

  1. Ingeniería en Sistemas, Universidad Técnica Estatal de Quevedo, Quevedo, Ecuador

    Cristian Zambrano-Vega

  2. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Bulevar Louis Pasteur 35, 29071, Málaga, Spain

    Antonio J. Nebro, José García-Nieto & José F. Aldana-Montes

Authors
  1. Cristian Zambrano-Vega
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  2. Antonio J. Nebro
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  3. José García-Nieto
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  4. José F. Aldana-Montes
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Corresponding author

Correspondence to Antonio J. Nebro.

Additional information

This work has been partially funded by the Secretaría Nacional de Educación Superior Ciencia y Tecnología SENESCYT from Ecuador, and Spanish Grants TIN2014-58304-R (Spanish Ministry of Education and Science) P11-TIC-7529 (Innovation, Science and Enterprise Ministry of the regional government of the Junta de Andalucía) and P12-TIC-1519 (Plan Andaluz de Investigación, Desarrollo e Innovación). José García-Nieto is recipient of a Post-Doctoral fellowship of “Captación de Talento para la Investigación” at Universidad de Málaga.

Appendix 1: Supplementary results

Appendix 1: Supplementary results

The following tables contain the medians and interquartile range of resulting distributions of \(I_{e+}\) and \(I_{\mathrm{IGD+}}\) (out of 25 independent runs), for each compared algorithm and family of MSA instances. A total number of 140 different MSA instances from 5 problem families (RV-11, RV-12, RV-30, RV-40, and RV-50) have been tackled, which lead us to suggest that reported conclusions are unbiased and general enough.

Table 3 Median and interquartile range of \(I_{\mathrm{IGD+}}\) for MSA problem family RV11 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Table 4 Median and interquartile range of \(I_{e+}\) for MSA problem family RV12 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Table 5 Median and interquartile range of \(I_{\mathrm{IGD+}}\) for MSA problem family RV12 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Table 6 Median and interquartile range of \(I_{e+}\) for MSA problem family RV30 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Table 7 Median and interquartile range of \(I_{\mathrm{IGD+}}\) for MSA problem family RV30 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Table 8 Median and interquartile range of \(I_{e+}\) for MSA problem family RV40 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Table 9 Median and interquartile range of \(I_{\mathrm{IGD+}}\) for MSA problem family RV40 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Table 10 Median and interquartile range of \(I_{e+}\) for MSA problem family RV50 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Table 11 Median and interquartile range of \(I_{\mathrm{IGD+}}\) for MSA problem family RV50 and for algorithms: GWASF-GA, NSGA-II, MOCell, and NSGA-III
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Zambrano-Vega, C., Nebro, A.J., García-Nieto, J. et al. Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment. Prog Artif Intell 6, 195–210 (2017). https://doi.org/10.1007/s13748-017-0116-6

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