Abstract
The application of multiobjective optimization techniques to solve biological problems has significantly grown in the last years. In order to generate satisfying approximations to the Pareto-optimal set, two key problems must be addressed. Firstly, we must distinguish solution quality in accordance with the optimization goal, usually measured by means of multiobjective quality indicators. Secondly, we must undertake the development of parallel designs to carry out searches over exponentially growing solution spaces. This work tackles the reconstruction of phylogenetic relationships by applying an Indicator-Based Evolutionary Algorithm. For this purpose, we propose a parallel design based on OpenMP which considers the computation of hypervolume-based indicators in fitness assignment procedures. Experiments on four biological data sets show significant results in terms of parallel scalability and multiobjective performance with regard to other methods from the literature.
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References
Bader, D.A., Stamatakis, A., Tseng, C.W.: Computational Grand Challenges in Assembling the Tree of Life: Problems and Solutions. In: Advances in Computers, vol. 68, pp. 127–176. Elsevier (2006)
Cancino, W., Jourdan, L., Talbi, E.-G., Delbem, A.C.B.: Parallel multi-objective approaches for inferring phylogenies. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 26–37. Springer, Heidelberg (2010)
Coello, C., Dhaenens, C., Jourdan, L.: Advances in Multi-Objective Nature Inspired Computing. Springer, Heidelberg (2010)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi–Objective Genetic Algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Fogel, G.B.: Evolutionary Computation for the Inference of Natural Evolutionary Histories. IEEE Connections 3(1), 11–14 (2005)
Goëffon, A., Richer, J.M., Hao, J.K.: Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem. IEEE/ACM Trans. Comput. Biol. Bioinform. 5(1), 136–145 (2008)
Goloboff, P.A., Farris, J.S., Nixon, K.C.: TNT, a free program for phylogenetic analysis. Cladistics 24(5), 774–786 (2008)
Guéquen, L., et al.: Bio++: efficient extensible libraries and tools for computational molecular evolution. Molecular Biology and Evolution 30(8), 1745–1750 (2013)
Lemey, P., Salemi, M., Vandamme, A.-M.: The Phylogenetic Handbook: a Practical Approach to Phylogenetic Analysis and Hypothesis Testing. Cambridge Univ. Press, Cambridge (2009)
Lewis, P.O.: A Genetic Algorithm for Maximum-Likelihood Phylogeny Inference Using Nucleotide Sequence Data. Mol. Biol. Evol. 15(3), 277–283 (1998)
Macey, J.R.: Plethodontid salamander mitochondrial genomics: A parsimony evaluation of character conflict and implications for historical biogeography. Cladistics 21(2), 194–202 (2005)
Poladian, L.: A GA for Maximum Likelihood Phylogenetic Inference using Neighbour-Joining as a Genotype to Phenotype Mapping. In: Genetic and Evolutionary Computation Conference, pp. 415–422 (2005)
Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 5th edn. Chapman & Hall/CRC Press, New York (2011)
Stamatakis, A.: RAxML Version 8: A Tool for Phylogenetic Analysis and Post-Analysis of Large Phylogenies. Bioinformatics 30(9), 1312–1313 (2014)
Talbi, E.-G., Mostaghim, S., Okabe, T., Ishibuchi, H., Rudolph, G., Coello Coello, C.A.: Parallel approaches for multiobjective optimization. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 349–372. Springer, Heidelberg (2008)
Zitzler, E., Künzli, S.: Indicator-Based Selection in Multiobjective Search. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)
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Santander-Jiménez, S., Vega-Rodríguez, M.A. (2014). Inferring Multiobjective Phylogenetic Hypotheses by Using a Parallel Indicator-Based Evolutionary Algorithm. In: Dediu, AH., Lozano, M., Martín-Vide, C. (eds) Theory and Practice of Natural Computing. TPNC 2014. Lecture Notes in Computer Science, vol 8890. Springer, Cham. https://doi.org/10.1007/978-3-319-13749-0_18
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DOI: https://doi.org/10.1007/978-3-319-13749-0_18
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