TL;DR
This paper investigates the practical impact of kernelization on computing the TBR distance between unrooted phylogenetic trees, demonstrating that new reduction rules significantly improve instance size reduction and computational efficiency.
Contribution
The authors implement and compare new and existing reduction rules for kernelization, showing their effectiveness in reducing problem size and improving TBR distance computation in practice.
Findings
New reduction rules produce smaller reduced instances.
Reduction often decreases TBR distance in a controlled manner.
Practical reduction exceeds known worst-case bounds significantly.
Abstract
Phylogenetic trees are leaf-labelled trees used to model the evolution of species. Here we explore the practical impact of kernelization (i.e. data reduction) on the NP-hard problem of computing the TBR distance between two unrooted binary phylogenetic trees. This problem is better-known in the literature as the maximum agreement forest problem, where the goal is to partition the two trees into a minimum number of common, non-overlapping subtrees. We have implemented two well-known reduction rules, the subtree and chain reduction, and five more recent, theoretically stronger reduction rules, and compare the reduction achieved with and without the stronger rules. We find that the new rules yield smaller reduced instances and thus have clear practical added value. In many cases they also cause the TBR distance to decrease in a controlled fashion. Next, we compare the achieved reduction to…
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