New Constraints on Generation of Uniform Random Samples from Evolutionary Trees

In this paper, we introduce new algorithms for selecting taxon samples from large evolutionary trees, maintaining uniformity and randomness, under certain new constraints on the taxa. The algorithms are efficient as their runtimes and space complexities are polynomial. The algorithms have direct applications to the evolution of phylogenetic tree and efficient supertree construction using biologically curated data. We also present new lower bounds for the problem of constructing evolutionary tree from experiment under some earlier stated constraints. All the algorithms have been implemented