Cross-domain Algorithm Selection: Algorithm Selection across Selection Hyper-heuristics
The present study introduces algorithm selection on selection hyper-heuristics. Hyper-heuristics are known as problem-independent methods utilized to solve different instances from varying problem domains. In the literature, there has been effective hyper-heuristic designs providing a certain level of generality in problem solving. Still, the relevant existing research indicates that there is no single hyper-heuristic which performs always the best on different problem solving scenarios. Algorithm selection has been investigated essentially to address this issue, mainly for the problem-specific algorithms, by automatically identifying the (near) best algorithm(s) for each given problem instance. This paper performs algorithm selection on selection hyper-heuristics, for the first time, delivering cross-domain algorithm selection. For this purpose, a suite of problem-independent features is initially introduced. Then, algorithm selection is examined across 9 single-objective combinatorial optimization problems with 6 online selection hyper-heuristics. The experimental results carried out on these problems indicated that algorithm selection is effective for choosing hyper-heuristics while offering improved generality and robustness.
