Greedy Randomized Adaptive Search Procedures Export

@article{citeulike:2198932, abstract = {Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.}, author = {Feo, Thomas A. and Resende, Mauricio G.}, citeulike-article-id = {2198932}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/BF01096763}, day = {21}, doi = {10.1007/BF01096763}, journal = {Journal of Global Optimization}, keywords = {grasp, heuristics, phd, publi1}, month = {March}, number = {2}, pages = {109--133}, posted-at = {2009-06-17 15:24:01}, priority = {2}, title = {Greedy Randomized Adaptive Search Procedures}, url = {http://dx.doi.org/10.1007/BF01096763}, volume = {6}, year = {1995} }

TY - JOUR ID - citeulike:2198932 L3 - citeulike-article-id:2198932 N2 - Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications. IS - 2 JF - Journal of Global Optimization EP - 133 TI - Greedy Randomized Adaptive Search Procedures VL - 6 SP - 109 KW - grasp KW - heuristics KW - phd KW - publi1 AU - Feo, Thomas AU - Resende, Mauricio PY - 1995/03/21/ UR - http://dx.doi.org/10.1007/BF01096763 ER -