A multi-modal algorithm based on an NSGA-II scheme for phylogenetic tree inference

The analysis of evolutionary data allows uncovering information about the organisms and how they have adapted and evolved. This information could provide us with new insights about the specialisation of organisms (or part of them), how they adapt, how similar they are with other species, among others. Unfortunately, this evolutionary history can only be estimated, and for that, several computational methods exist. Among the methods, optimisation methods are one of the main approaches to deal with this problem, with multiobjective optimisation producing promising results. In this paper, we deal with multiobjective phylogenetic inference, using a multi-modal metaheuristic approach that exploits the decision space in the multiobjective formulation of the problem. In particular, we incorporate a new metric based on a topological tree distance. We compare the method with state of the art algorithms in terms of performance. Additionally, we perform a thorough analysis of a study case on a yeast Saccharomyces cerevisiae dataset. Results show that our proposal is able to improve the diversity of solutions while improving or keeping the quality of solutions in terms of hypervolume.