Multi-armed bandits, Thomson sampling and unsupervised machine learning in phylogenetic graph search

Abstract

A phylogenetic graph search relies on a large number of highly parameterized search procedures (e.g. branch-swapping, perturbation, simulated annealing, genetic algorithm). These procedures vary in effectiveness over datasets and at alternative points in analytical pipelines. The multi-armed bandit problem is applied to phylogenetic graph searching to more effectively utilize these procedures. Thompson sampling is applied to a collection of search and optimization "bandits" to favour productive search strategies over those that are less successful. This adaptive random sampling strategy is shown to be more effective in producing heuristically optimal phylogenetic graphs and more time efficient than existing uniform probability randomized search strategies. The strategy acts as a form of unsupervised machine learning that can be applied to a diversity of phylogenetic datasets without prior knowledge of their properties.