Binary phylogenetic trees inferred from biological data are central to
understanding the shared evolutionary history of organisms. Inferring the
placement of latent nodes in a tree by any optimality criterion (e.g., maximum
likelihood) is an NP-hard problem, propelling the development of myriad
heuristic approaches. Yet, these heuristics often lack a systematic means of
uniformly sampling random trees or effectively exploring a tree space that
grows factorially, which are crucial to optimisation problems such as machine
learning. Accordingly, we present Phylo2Vec, a new parsimonious representation
of a phylogenetic tree. Phylo2Vec maps any binary tree with $n$ leaves to an
integer vector of length $n$. We prove that Phylo2Vec is both well-defined and
bijective to the space of phylogenetic trees. The advantages of Phylo2Vec are
twofold: i) easy uniform sampling of binary trees and ii) systematic ability to
traverse tree space in very large or small jumps. As a proof of concept, we use
Phylo2Vec for maximum likelihood inference on five real-world datasets and show
that a simple hill climbing-based optimisation efficiently traverses the
vastness of tree space from a random to an optimal tree.

本文介绍了一种新的用于最大似然推断的优化方法 —— 基于 Phylo2Vec 的 hill climbing-based 优化算法，该算法结合了二叉进化树和机器学习优化的优点，旨在解决 NP - 难问题。