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A New Evolutionary Optimisation Approach to Deal With the Many-Objective Phylogenetic Inference Problem

The phylogenetic inference strategies aim to propose hypotheses to explain the evolutionary relationships for different organisms. These resultant evolutionary histories are often represented as phylogenetic trees. In computer science, the phylogenetic inference has been treated as an optimisation problem. The literature has proposed different criteria to select the optimal tree between the possible topologies. In order to reduce the bias associated to the dependency on the selected criterion, different multi-objective optimisation strategies have been proposed during the last decade. These strategies search by solutions using operators and metrics based on the objective space. However, a recent work concluded that the topological features of the trees (decision space) and the objective space in the multi-objective phylogenetic inference context are not related, becoming phylogeny in a multimodal problem. It means that the current multi-objective strategies could discard solutions from different regions of the decision space, limiting the searching process and the resultant topologies. In this work, we propose a new version of the Memetic algorithm based on an NSGA-II scheme for phylogenetic inference, which include a multimodal operator that considers the diversity of the topologies of the trees based on the decision space to rank the solutions. The inclusion of this operator improved the diversity of solutions according to the decision and the objective space, increasing the hypervolume metric compared to the base version of this memetic algorithm.

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