@inproceedings{BauPer03a, author = {J. Bautista and J. Pereira}, title = {Procedimientos para la localizaci\'on de \'areas de aportaci\'on de residuos urbanos}, booktitle = {{27 Congreso Nacional de Estad\'{\i}stica e Investigaci\'on Operativa}}, address = {Lleida, Spain}, month = {April}, annote = {The authors address the problem of locating areas where to collect waste products. They show that it can be easily interpreted as a special Set Covering Problem. They propose an exact method, a Genetic Algorithm, and a {GRASP}. The two metaheuristics share the same local search strategy that simply looks for redudant elements to be removed from the solution. In Spanish.}, year = {2003} } @Article{DesTri98a, author = "A.S. Deshpande and E. Triantaphyllou", title = "A greedy randomized adaptive search procedure ({GRASP}) for inferring logical clauses from examples in polynomial time and some extensions", journal = "Mathematical and Computer Modelling", volume = "27", pages = "75--99", year = "1998", annote = "Two heuristics are presented in this article for inferring a small size Boolean function from complete and incomplete examples in polynomial time. Each example can be positive or negative depending on whether it must be accepted or rejected, respectively, by the target function. Both of the proposed heuristics are randomized in the sense that instead of choosing the best candidate element, a candidate list is built whose elements are assigned with evaluative function values close to the highest one.", } @Article{ParPitRes96a, author = "P.M. Pardalos and L.S. Pitsoulis and M.G.C. Resende", title = "A parallel {GRASP} for {MAX-SAT} problems", journal = "Lecture Notes in Computer Science", volume = "1184", pages = "575--585", year = "1996", annote = "A parallel {GRASP} for weighted maximum satisfiability (MAX-SAT) problem is proposed. The {GRASP} is based on the serial {GRASP} presented by Resende, Pitsoulis, and Pardalos (1997). The parallel implementation distributes the {GRASP} iterations among several processors operating in parallel, avoiding that two processors have as input the same random number generator seed. The best solution found among all processors is identified and used as solution of the problem.", } @incollection{ResFeo96a, author = "M.G.C. Resende and T.A. Feo", title = "A {GRASP} for Satisfiability", booktitle = "{C}liques, {C}oloring, and {S}atisfiability: {T}he {S}econd {DIMACS} {I}mplementation {C}hallenge", editor = "D.S. Johnson and M.A. Trick", series = "{DIMACS} Series on Discrete Mathematics and Theoretical Computer Science", publisher = "American Mathematical Society", volume = "26", year = "1996", pages = "499--520", annote = "This paper describes a {GRASP} for the satisfiability problem that can be also directly applied to both the weighted and unweighted versions of the maximum satisfiability problem. The adaptive greedy function is a hybrid combination of two functions. One function seeks to maximize the number of yet-unsatisfied clauses that become satisfied after the assignment of each construction iteration, while the other maximizes the number of yet-unassigned literals in yet-unsatisfied clauses that become satisfied if opposite assignments were to be made. The local search flips the assignment of each variable, one at a time, checking if the new truth assignment increases the number of satisfied clauses." } @incollection{ResPitPar97a, author = "M.G.C. Resende and L.S. Pitsoulis and P.M. Pardalos", title = "Approximate solution of weighted {MAX-SAT} problems using {GRASP}", booktitle = "Satisfiability problems", editor = "J. Gu and P.M. Pardalos", series = "{DIMACS} Series on Discrete Mathematics and Theoretical Computer Science", publisher = "American Mathematical Society", volume = "35", pages = "393--405", year = "1997", annote = "This article proposes a {GRASP} for finding approximate solutions of weighted {MAX-SAT} problems. The greedy adaptive function is to maximize the total weight of yet-unsatisfied clauses that become satisfied after the assignment of each construction phase iteration. The local search uses the $1$-flip neighborhood of a vector $x$, defined as the set of all binary vectors that differ from $x$ in exactly one literal." } @Article{ResPitPar00a, author = "M.G.C. Resende and L.S. Pitsoulis and P.M. Pardalos", title = "{Fortran} subroutines for computing approximate solutions of {MAX-SAT} problems using {GRASP}", journal = "Discrete Applied Mathematics", volume = "100", pages = "95--113", year = "2000", annote = "A set of {Fortran} subroutines for computing approximate solutions of {MAX-SAT} problems is described. The algorithm implemented was proposed by Resende, Pitsoulis, and Pardalos (1997). Two versions of the subroutines are distributed. One version uses a neighborhood data structure in order to speed up the local search phase, while the second version, since it does not make use of this data structure, is more memory efficient but less time efficient. Computational results improve upon those in Resende, Pitsoulis, and Pardalos (1997) using an RCL parameter $\alpha$ randomly chosen each {GRASP} iteration from the interval $[0,1]$.", } @Article{YilTriCheLia03a, author = {E. Yilmaz and E. Triantaphyllou and J. Chen and T.W. Liao}, title = {A heuristic for mining association rules in polynomial time}, journal = {Mathematical and Computer Modelling}, volume = {37}, pages = {219--233}, annote = {The problem consisting in mining association rules in a database needs to be efficiently solved, expecially nowadays when modern databases have very large sizes. The authors propose a heuristic algorithm that incorporates the randomized idea of the {GRASP} construction phase.}, year = {2003} }