Constraint-based local search for Golomb rulers

This paper presents a constraint-based local search algorithm to find an optimal Golomb ruler of a specified order. While the state-of-the-art search algorithms for Golomb rulers hybridise a range of sophisticated techniques, our algorithm relies on simple tabu meta-heuristics and constraint-driven variable selection heuristics. Given a reasonable time limit, our algorithm effectively finds 16-mark optimal rulers with success rate 60 % and 17-mark rulers with 6 % near-optimality.This paper presents a constraint-based local search algorithm to find an optimal Golomb ruler of a specified order. While the state-of-the-art search algorithms for Golomb rulers hybridise a range of sophisticated techniques, our algorithm relies on simple tabu meta-heuristics and constraint-driven variable selection heuristics. Given a reasonable time limit, our algorithm effectively finds 16-mark optimal rulers with success rate 60 % and 17-mark rulers with 6 % near-optimality.
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Journal Title

Lecture Notes in Computer Science

Volume

9075

DOI

https://doi.org/10.1007/978-3-319-18008-3_22

Copyright Statement

© 2015 Springer International Publishing AG. This is an electronic version of an article published in Lecture Notes In Computer Science (LNCS), Vol. 9075, pp 322-331, 2015. Lecture Notes In Computer Science (LNCS) is available online at: http://link.springer.com// with the open URL of your article.

Subject

Bioinformatics

Publication URI

http://hdl.handle.net/10072/125178

Collection

Journal articles