LEADER 04798nam 22007335 450 001 9910254092003321 005 20200630025352.0 010 $a1-4939-6530-1 024 7 $a10.1007/978-1-4939-6530-4 035 $a(CKB)3710000000926145 035 $a(DE-He213)978-1-4939-6530-4 035 $a(MiAaPQ)EBC6314116 035 $a(MiAaPQ)EBC5576263 035 $a(Au-PeEL)EBL5576263 035 $a(OCoLC)961912749 035 $a(PPN)196320852 035 $a(EXLCZ)993710000000926145 100 $a20161026d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOptimization by GRASP $eGreedy Randomized Adaptive Search Procedures /$fby Mauricio G.C. Resende, Celso C. Ribeiro 205 $a1st ed. 2016. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2016. 215 $a1 online resource (XX, 312 p. 173 illus., 117 illus. in color.) 311 $a1-4939-6528-X 327 $aForeword -- Preface -- 1. Introduction -- 2. A short tour of combinatorial optimization and computational complexity -- 3. Solution construction and greedy algorithms -- 4. Local search -- 5. GRASP: The basic heuristic -- 6. Runtime distributions -- 7. GRASP: extended construction heuristics -- 8. Path-relinking -- 9. GRASP with Path-relinking -- 10. Parallel GRASP heuristics -- 11. GRASP for continuous optimization -- 12. Case studies -- References -- Index. 330 $aThis is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general. The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASP and combinatorial optimization. For the practitioner who needs to solve combinatorial optimization problems, the book provides a chapter with four case studies and implementable templates for all algorithms covered in the text. This book, with its excellent overview of GRASP, will appeal to researchers and practitioners of combinatorial optimization who have a need to find optimal or near optimal solutions to hard combinatorial optimization problems. 606 $aComputer mathematics 606 $aComputer science?Mathematics 606 $aOperations research 606 $aDecision making 606 $aIndustrial engineering 606 $aProduction engineering 606 $aArtificial intelligence 606 $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X 606 $aDiscrete Mathematics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17028 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 606 $aIndustrial and Production Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T22008 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputer mathematics. 615 0$aComputer science?Mathematics. 615 0$aOperations research. 615 0$aDecision making. 615 0$aIndustrial engineering. 615 0$aProduction engineering. 615 0$aArtificial intelligence. 615 14$aComputational Mathematics and Numerical Analysis. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aOperations Research/Decision Theory. 615 24$aIndustrial and Production Engineering. 615 24$aArtificial Intelligence. 676 $a519.3 700 $aResende$b Mauricio G.C$4aut$4http://id.loc.gov/vocabulary/relators/aut$0282953 702 $aRibeiro$b Celso C$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254092003321 996 $aOptimization by GRASP$92169026 997 $aUNINA