Abstarct: GRASP is a multi-start mextaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. Builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this thesis, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different appli- cations. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discus in detail implementation strategies of memory-baxsed intensification and post-optimization tech- niques using path-relixnking. Hybridizations with other mextaheuristics, parallelization strategies, and applications are also reviewed.