Erratum to: e-GRASP: an integrated evolutionary and GRASP resource for exploring disease associations

Grasp with Evolutionary Path-relinking

We propose GRASP with evolutionary path-relinking, a metaheuristic resulting from the hybridization of GRASP, path-relinking, and evolutionary path-relinking. This metaheuristic is applied to a network migration problem. Experiments show that a GRASP with evolutionary path-relinking heuristic finds solutions faster than a heuristic based on GRASP with path-relinking as well as one based on pure...

Grasp: an Annotated Bibliography

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. It is a multi-start or iterative process, in which each GRASP iteration consists of two phases, a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Since 1989, numerous p...

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A Hybrid GRASP - Evolutionary Algorithm Approach to Golomb Ruler Search

We consider the problem of finding small Golomb rulers, a hard combinatorial optimization task. This problem is here tackled by means of a hybrid evolutionary algorithm (EA). This EA incorporates ideas from greedy randomized adaptive search procedures (GRASP) in order to perform the genotype-to-phenotype mapping. As it will be shown, this hybrid approach can provide high quality results, better...

How to GRASP GOOD

The e orts of the past decade to provide database designers and users with more powerful tools for both modeling the considered application domain and manipulating this model, has resulted in a variety of declarative data languages of usually high complexity. In view of the implementation and optimization of such languages, it is often bene cial to use a second language as an intermediate, mach...