The greedy randomized adaptive search procedure (also known as GRASP) is a metaheuristic algorithm commonly applied to combinatorial optimization problems.
GRASP typically consists of iterations made up from successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search.
This kind of greedy randomized construction method is also known as a semi-greedy heuristic, first described in Hart and Shogan (1987).
In this variation, the basic parameter that defines the restrictiveness of the RCL during the construction phase is self-adjusted according to the quality of the solutions previously found.
[5] There are also techniques for search speed-up, such as cost perturbations, bias functions, memorization and learning, and local search on partially constructed solutions.