Defensa de la Tesis Doctoral de D. Sergio Santander Jiménez, dirigida por D. Miguel Ángel Vega Rodríguez del grupo de investigación ARCO - viernes 29 de enero

Tribunal:

D. Julio Ortega Lopera. Universidad de Granada

D. Miquel Àngel Senar Rosell. Universidad Autónoma de Barcelona

D. Ignacio Rojas Ruiz. Universidad de Granada

D. Leonel Augusto Pires Seabra de Sousa. Universidad de Lisboa

D. José María Granado Criado. Universidad de Extremadura

Resumen:

By studying the molecular features of living organisms, phylogenetic inference seeks to provide hypotheses about the evolutionary events which led to the current biodiversity in nature. When inferring such evolutionary hypotheses, several key problems must be addressed. Firstly, these analyses involve the processing of a search space of phylogenetic trees whose size grows exponentially with the number of species under study. Additional difficulties are given by the fact that biological evaluation procedures require complex computations whose number grows linearly with the length of the input molecular sequences. A more controversial problem lies on the choice of the preferred optimality criterion, as it represents one of the most troublesome sources of conflict in phylogenetics. Situations where different optimality criteria give support to conflicting evolutionary histories for a given dataset can be solved by proposing a compromise view of phylogenetics based on the application of multiobjective optimization techniques.

This PhD Thesis is focused on the application of parallel and bioinspired computing to tackle the phylogenetic inference problem. The key goals of this research include the definition of a multiobjective formulation of phylogenetics to address conflicts in real biological analyses, the study and assessment of a variety of bioinspired multiobjective designs, and their efficient parallelization on different hardware architectures. Through the comparison with other state-of-the-art phylogenetic tools, we give account of the relevant parallel, multiobjective, and biological performance attained by the proposed multiobjective designs.