Next Article in Journal
GASP: Genetic Algorithms for Service Placement in Fog Computing Systems
Previous Article in Journal
Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems
Previous Article in Special Issue
Parameterised Enumeration for Modification Problems
Open AccessArticle

A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth

Department of Data Science and Knowledge Engineering, Maastricht University, 6211 LK Maastricht, The Netherlands
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(10), 200; https://doi.org/10.3390/a12100200
Received: 25 July 2019 / Revised: 12 September 2019 / Accepted: 16 September 2019 / Published: 20 September 2019
(This article belongs to the Special Issue New Frontiers in Parameterized Complexity and Algorithms)
We present an algorithm selection framework based on machine learning for the exact computation of treewidth, an intensively studied graph parameter that is NP-hard to compute. Specifically, we analyse the comparative performance of three state-of-the-art exact treewidth algorithms on a wide array of graphs and use this information to predict which of the algorithms, on a graph by graph basis, will compute the treewidth the quickest. Experimental results show that the proposed meta-algorithm outperforms existing methods on benchmark instances on all three performance metrics we use: in a nutshell, it computes treewidth faster than any single algorithm in isolation. We analyse our results to derive insights about graph feature importance and the strengths and weaknesses of the algorithms we used. Our results are further evidence of the advantages to be gained by strategically blending machine learning and combinatorial optimisation approaches within a hybrid algorithmic framework. The machine learning model we use is intentionally simple to emphasise that speedup can already be obtained without having to engage in the full complexities of machine learning engineering. We reflect on how future work could extend this simple but effective, proof-of-concept by deploying more sophisticated machine learning models. View Full-Text
Keywords: treewidth; tree decomposition; algorithm selection; machine learning; combinatorial optimisation treewidth; tree decomposition; algorithm selection; machine learning; combinatorial optimisation
Show Figures

Figure 1

MDPI and ACS Style

Slavchev, B.; Masliankova, E.; Kelk, S. A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth. Algorithms 2019, 12, 200.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop