Toward subheuristic search

In previous work, we have introduced an effective, resource-efficient and self-adapting hyperheuristic that uses genetic programming (GP) as its method of search in the space of domain-specific metaheuristics. GP employs user-provided, local heuristics from which it produces these metaheuristics (MHs). Here, we show that the hyperheuristic performs even better when working at the subheuristic level, i.e., when building MHs from generic components and specific elementary operations. In particular, this approach supports efficiency of the better MHs. Specifically, these MHs do not excessively iterate local search steps, i.e., their good performance comes from smart patterns of calls of the provided, basic components. Also, a moderate reduction of the maximum allowed MH size does not reduce performance significantly.