A parameter free choice function based hyper-heuristic strategy for pairwise test generation

Hyper-heuristics are advanced high-level search methodologies that solve hard computational problems indirectly via low-level heuristics. Choice function based hyper-heuristics are selection and acceptance hyper-heuristics that use statistical information to rank low-level heuristics for selection. In this paper, we describe a choice function based hyper-heuristic called Pairwise Choice Function based Hyper-heuristic (PCFHH) for the pairwise test generation problem. PCFHH uses a combination of three measures to select and apply an effective low-level heuristic from a set of four low-level heuristics at any stage of the search. Our experimental results have been encouraging as PCFHH outperforms most of pairwise test generation strategies on many of the problem instances.