LEADER 05490nam 22007454a 450 001 9911020136803321 005 20241106133153.0 010 $a9786610277568 010 $a9781280277566 010 $a1280277564 010 $a9780470315408 010 $a0470315407 010 $a9780471739388 010 $a0471739383 010 $a9780471739371 010 $a0471739375 035 $a(CKB)1000000000355437 035 $a(EBL)239396 035 $a(OCoLC)77371883 035 $a(SSID)ssj0000217953 035 $a(PQKBManifestationID)11198569 035 $a(PQKBTitleCode)TC0000217953 035 $a(PQKBWorkID)10212807 035 $a(PQKB)10730171 035 $a(MiAaPQ)EBC239396 035 $a(PPN)243027850 035 $a(Perlego)2772809 035 $a(EXLCZ)991000000000355437 100 $a20050118d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aParallel metaheuristics $ea new class of algorithms /$fedited by Enrique Alba 210 $aHoboken, NJ $cJohn Wiley$d2005 215 $a1 online resource (574 p.) 225 1 $aWiley series on parallel and distributed computing 300 $aDescription based upon print version of record. 311 08$a9780471678069 311 08$a0471678066 320 $aIncludes bibliographical references and index. 327 $aPARALLEL METAHEURISTICS A New Class of Algorithms; Contents; Foreword; Preface; Contributors; Part I INTRODUCTION TO METAHEURISTICS AND PARALLELISM; 1 An Introduction to Metaheuristic Techniques; 1.1 Introduction; 1.2 Trajectory Methods; 1.3 Population-Based Methods; 1.4 Decentralized Metaheuristics; 1.5 Hybridization of Metaheuristics; 1.6 Conclusions; References; 2 Measuring the Performance of Parallel Metaheuristics; 2.1 Introduction; 2.2 Parallel Performance Measures; 2.3 How to Report Results; 2.4 Illustrating the Influence of Measures; 2.5 Conclusions; References 327 $a3 New Technologies in Parallelism3.1 Introduction; 3.2 Parallel Computer Architectures: An Overview; 3.3 Shared-Memory and Distributed-Memory Programming; 3.4 Shared-Memory Tools; 3.5 Distributed-Memory Tools; 3.6 Which of Them'?; 3.7 Summary; References; 4 Metaheuristics and Parallelism; 4.1 Introduction; 4.2 Parallel LSMs; 4.3 Case Studies of Parallel LSMs; 4.4 Parallel Evolutionary Algorithms; 4.5 Case Studies of Parallel EAs; 4.6 Other Models; 4.7 Conclusions; References; Part II PARALLEL METAHEURISTIC MODELS; 5 Parallel Genetic Algorithms; 5.1 Introduction 327 $a5.2 Panmictic Genetic Algorithms5.3 Structured Genetic Algorithms; 5.4 Parallel Genetic Algorithms; 5.5 Experimental Results; 5.6 Summary; References; 6 Parallel Genetic Programming; 6.1 Introduction to GP; 6.2 Models of Parallel and Distributed GP; 6.3 Problems; 6.4 Real-Life Applications; 6.5 Placement and Routing in FPGA; 6.6 Data Classification Using Cellular Genetic Programming; 6.7 Concluding Discussion; References; 7 Parallel Evolution Strategies; 7.1 Introduction; 7.2 Deployment Scenarios of Parallel Evolutionary Algorithms; 7.3 Sequential Evolutionary Algorithms 327 $a7.4 Parallel Evolutionary Algorithms7.5 Conclusions; References; 8 Parallel Ant Colony Algorithms; 8.1 Introduction; 8.2 Ant Colony Optimization; 8.3 Parallel ACO; 8.4 Hardware Parallelization of ACO; 8.5 Other Ant Colony Approaches; References; 9 Parallel Estimation of Distribution Algorithms; 9.1 Introduction; 9.2 Levels of Parallelism in EDA; 9.3 Parallel Models for EDAs; 9.4 A Classification of Parallel EDAs; 9.5 Conclusions; References; 10 Parallel Scatter Search; 10.1 Introduction; 10.2 Scatter Search; 10.3 Parallel Scatter Search 327 $a10.4 Application of Scatter Search to the p-Median Problem10.5 Application of Scatter Search to Feature Subset Selection; 10.6 Computational Experiments; 10.7 Conclusions; References; 11 Parallel Variable Neighborhood Search; 11.1 Introduction; 11.2 The VNS Metaheuristic; 11.3 The Parallelizations; 11.4 Application of VNS for the p-median; 11.5 Computational Experiments; 11.6 Conclusions; References; 12 Parallel Simulated Annealing; 12.1 Introduction; 12.2 Simulated Annealing; 12.3 Parallel Simulated Annealing; 12.4 A Case Study; 12.5 Summary; References; 13 Parallel Tabu Search 327 $a13.1 Introduction 330 $aSolving complex optimization problems with parallel metaheuristicsParallel Metaheuristics brings together an international group of experts in parallelism and metaheuristics to provide a much-needed synthesis of these two fields. Readers discover how metaheuristic techniques can provide useful and practical solutions for a wide range of problems and application domains, with an emphasis on the fields of telecommunications and bioinformatics. This volume fills a long-existing gap, allowing researchers and practitioners to develop efficient metaheuristic algorithms to find solutions. 410 0$aWiley series on parallel and distributed computing. 606 $aMathematical optimization 606 $aParallel algorithms 606 $aOperations research 615 0$aMathematical optimization. 615 0$aParallel algorithms. 615 0$aOperations research. 676 $a519.6 701 $aAlba$b Enrique$0853126 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020136803321 996 $aParallel metaheuristics$94417132 997 $aUNINA