Thus, when using hyper-heuristics, we are attempting to find the right method or sequence of heuristics in a given situation rather than trying to solve a problem directly.
They aim to be generic methods, which should produce solutions of acceptable quality, based on a set of easy-to-implement low-level heuristics.
Despite the significant progress in building search methodologies for a wide variety of application areas so far, such approaches still require specialists to integrate their expertise in a given problem domain.
One of the main ideas for automating the design of heuristics requires the incorporation of machine learning mechanisms into algorithms to adaptively guide the search.
Both learning and adaptation processes can be realised on-line or off-line, and be based on constructive or perturbative heuristics.
The goal is to raise the level of generality of decision support methodology perhaps at the expense of reduced - but still acceptable - solution quality when compared to tailor-made metaheuristic approaches.
[10] Subsequently, Cowling, Soubeiga, Kendall, Han, Ross and other authors investigated and extended this idea in areas such as evolutionary algorithms, and pathological low level heuristics.
More specifically, it comes from work on automated planning systems, and its eventual focus towards the problem of learning control knowledge.
"[19] The process requires, as in the first class of hyper-heuristics, the selection of a suitable set of heuristics known to be useful in solving the target problem.
The learning takes place while the algorithm is solving an instance of a problem, therefore, task-dependent local properties can be used by the high-level strategy to determine the appropriate low-level heuristic to apply.