A new hyper‐heuristic as a general problem solver: an implementation in HyFlex

This study provides a new hyper‐heuristic design using a learning‐based heuristic selection mechanism together with an adaptive move acceptance criterion. The selection process was supported by an online heuristic subset selection strategy. In addition, a pairwise heuristic hybridization method was designed. The motivation behind building an intelligent selection hyper‐heuristic using these adaptive hyper‐heuristic sub‐mechanisms is to facilitate generality. Therefore, the designed hyper‐heuristic was tested on a number of problem domains defined in a high‐level framework, i.e., HyFlex. The framework provides a set of problems with a number of instances as well as a group of low‐level heuristics. Thus, it can be considered a good environment to measure the generality level of selection hyper‐heuristics. The computational results demonstrated the generic performance of the proposed strategy in comparison with other tested hyper‐heuristics composed of the sub‐mechanisms from the literature. Moreover, the performance and behavior analysis conducted for the hyper‐heuristic clearly showed its adaptive characteristics under different search conditions. The principles comprising the here presented algorithm were at the heart of the algorithm that won the first international cross‐domain heuristic search competition.