A top-level general strategy which guides other heURIstics to search for feasible solutions in domains where the task is hard. MetaheURIstics have been most generally applied to problems classified as NP-Hard or NP-Complete by the theory of computational complexity. However, metaheURIstics would also be applied to other combinatorialoptimisation problems for which it is known that a polynomial-time solution exists but is not practical. Examples of metaheURIstics are Tabu Search, {simulated annealing}, {genetic algorithms} and {memetic algorithms}. (1997-10-30)