Learning Combinatorial Interaction Test Generation Strategies Using Hyperheuristic Search

The surge of search based software engineering research has been hampered by the need to develop customized search algorithms for different classes of the same problem. For instance, two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development, our exemplar problem, has left software engineers with a bewildering choice of CIT techniques, each specialized for a particular task. This paper proposes the use of a single hyperheuristic algorithm that learns search strategies across a broad range of problem instances, providing a single generalist approach. We have developed a Hyperheuristic algorithm for CIT, and report experiments that show that our algorithm competes with known best solutions across constrained and unconstrained problems: For all 26 real-world subjects, it equals or outperforms the best result previously reported in the literature. We also present evidence that our algorithm's strong generic performance results from its unsupervised learning. Hyperheuristic search is thus a promising way to relocate CIT design intelligence from human to machine

LIPIcs, Volume 248, ISAAC 2022, Complete Volume

LIPIcs, Volume 248, ISAAC 2022, Complete Volum

Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education

International audienceThis volume contains the Proceedings of the Seventh Congress of the European Society for Research in Mathematics Education (ERME), which took place 9-13 February 2011, at Rzeszñw in Poland

Applications of matching theory in constraint programming

[no abstract

2007-2008 UNM CATALOG

Course catalog for the years 2007-2008.https://digitalrepository.unm.edu/course_catalogs/1022/thumbnail.jp

2008-2009-UNM CATALOG

Course catalog for 2008-2009https://digitalrepository.unm.edu/course_catalogs/1098/thumbnail.jp