Abstract
The inference of phylogenetic trees is one of the most important tasks in computational biology. In this paper, we propose an extension to multi-objective evolutionary algorithms to address this problem. Here, we adopt an enhanced indirect encoding for a tree using the corresponding Prüfer code represented in Newick format. The algorithm generates a range of non-dominated trees given alternative fitness measures such as statistical likelihood and maximum parsimony. A key feature of this approach is the preservation of the evolutionary hierarchy between species. Preliminary experimental results indicate that our model is capable of generating a set of optimized phylogenetic trees for given species data and the results are comparable with other techniques.
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References
Baldauf, S.L.: Phylogeny for the faint of heart: A tutorial. Trends in Genetics 19(6), 345–351 (2003)
Felsenstein, J.: PHYLIP – Phylogeny Inference Package (2000), http://evolution.genetics.washington.edu/phylip.html
Congdon, C.B., Septor, K.J.: Phylogenetic trees Using Evolutionary Search: Initial Progress in Extending Gaphyl to Work with Genetic Data. In: The 2003 Congress on Evolutionary Computation (CEC 2003), vol. 1, pp. 320–326 (December 2003)
Cancino, W., Delbem, A.C.B.: A Multi-Objective Evolutionary Approach for Phylogenetic Inference. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 428–442. Springer, Heidelberg (2007)
Cotta, C., Moscato, P.: Inferring phylogenetic trees using evolutionary algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 720–729. Springer, Heidelberg (2002)
Gottlieb, J., Julstrom, B.A., Raidl, G.R., Rothlauf, F.: Prüfer Numbers: A Poor Representation of Spanning Trees for Evolutionary Search. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 343–350. Morgan Kaufmann, San Francisco (2001)
Gascuel, O.: BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Molecular Biology and Evolution 14(7), 685–695 (1997)
Reijmers, T.H., Wehrens, R., Buydens, L.M.C.: Quality Criteria of Genetic Algorithms for Construction of Phylogenetic Trees. Journal of Computational Chemistry 20(8), 867–876 (1999)
Gen, M., Li, Y.: Spanning tree-based genetic algorithm for the bicriteria fixedcharge transportation problem. In: Proceedings of the 1999 Congress on Evolutionary Computation (CEC 1999), Washington, DC, USA, vol. 3, p. 2271 (1999)
Handl, J., Kell, D.B., Knowles, J.: Multiobjective Optimization in Bioinformatics and Computational Biology. IEEE/ACM Transaction on Computational Biology and Bioinformatics 4(2), 279–292 (2007)
Deb, K., Agarwal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 2000001, Indian Institute of Technology, Kanpur, India (2000)
Zelwer, M., Daubin, V.: Detecting phylogenetic incongruence using BIONJ: an improvement of the ILD test. Molecular Phylogenetics and Evolution 33(3), 687–693 (2004)
Poladian, L., Jermiin, L.S.: Multi-objective evolutionary algorithms and phylogentic inference with multiple data sets. Soft Computing 10, 359–368 (2006)
Huelsenbeck, J.P., Ronquist, F.: MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17(8), 754–755 (2001)
Saitou, N., Nei, M.: The Neighbor-Joining Method: A New Method for reconstructing Phylogenetic Trees. Molecular Biology and Evolution 4(4), 406–425 (1987)
Takezaki, N., Nei, M.: Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA. Genetics 144(1), 389–399 (1996)
Swofford, D., Olsen, G., Waddell, P., Hillis, D.: Phylogeny Reconstruction. In: Molecular Systematics, 3rd edn., pp. 407–514. Sinauer (1996)
Lewis, P.O.: A Genetic Algorithm for Maximum-Likelihood Phylogeny Inference Using Nucleotide Data. Molecular Biology and Evolution 15(3), 277–283 (1998)
Fitch, W.: Toward Difining the Course of Evolution: Minimum Change for a Specific Tree Topology. Systemetic Zoology 20(4), 406–416 (1972)
Felsenstein, J.: Evolutionary Trees from DNA Sequences: A Maximum Likelihood Approach. Journal of Molecular Evolution 17, 368–376 (1981)
Cole, J., Chai, B., Farris, R., Wang, K.S., McGarrell, D., Garrity, G., Tiedje, J.: The Ribosomal Database Project (RDP-II): Sequences and Tools for High-throughput rRNA Analysis. Nucleic Acids Research 33, D294–D296 (2005)
Ingman, M., Gyllensten, U.: mtDB: Human Mitochondrial Genome Database, a Resource for Population Genetics and Medical Sciences. Nucleic Acids Research 34, D749– D751 (2006)
Felsenstein, J.: DNAPARS – DNA Parsimony Program (1996), http://cmgm.stanford.edu/phylip/dnapars.html
Felsenstein, J.: DNAML – DNA Maximum Likelihood program (1993), http://cmgm.stanford.edu/phylip/dnaml.html
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Hassan, M.R., Hossain, M.M., Karmakar, C.K., Kirley, M. (2008). Phylogeny Inference Using a Multi-objective Evolutionary Algorithm with Indirect Representation. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_5
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DOI: https://doi.org/10.1007/978-3-540-89694-4_5
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