Hyper-heuristic - Classification of Approaches

Classification of Approaches

Hyper-heuristic approaches so far can be classified into two main categories. In the first class, captured by the phrase heuristics to choose heuristics, the hyper-heuristic framework is provided with a set of pre-existing, generally widely known heuristics for solving the target problem. The task is to discover a good sequence of applications of these heuristics for efficiently solving the problem. In the second class, heuristics to generate heuristics, the key idea is to "evolve new heuristics by making use of the components of known heuristics." 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. However, instead of supplying these directly to the framework, the heuristics are first decomposed into their basic components.

These two main broad types can be further categorised according to whether they are based on constructive or perturbative search. An additional orthogonal classification of hyper-heuristics considers the source providing feedback during the learning process, which can be either one instance (on-line learning) or many instances of the underlying problem studied (off-line learning).