Improving algorithms in phylogenetics using machine learning

Master thesis (2020)

Authors

B. Versendaal Applied Sciences

Contributors

L.J.J. van Iersel Discrete Mathematics and Optimization - EEMCS (supervisor 1)
K.I. Aardal Discrete Mathematics and Optimization - EEMCS (supervisor 2)
J.L.A. Dubbeldam Mathematical Physics - EEMCS (supervisor 2)
M Jones (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Bryan Versendaal
Machine Learning Optimisation Phyolgenetics Maximum Agreement Forest Hybridisation Number Problem Tail-Move Problem
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Published Date

11-09-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In this thesis we look at three different algorithms within the field of phylogenetics and create a proof of concept for using machine learning to improve the algorithms. The problems are the maximum agreement forest problem, the hybridization number problem and finally the tail move problem. A study of the problems show that they can all benefit from machine learning. For each of the problems we find a machine leanring implementation using basic decision trees. The results show that the MAF problem and the tail move problem can both benefit from machine learning and for the hybridization number problem we give an implementation that is faster than the studied algorithm.