LEADER 01032nam--2200373---450 001 990005786030203316 005 20200921150203.0 010 $a978-887578291-7 035 $a000578603 035 $aUSA01000578603 035 $a(ALEPH)000578603USA01 035 $a000578603 100 $a20121203d2012----km-y0itay50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aEconomia senza natura$ela grande truffa$fFerdinando Boero 210 $aTorino$cCodice edizioni$d2012 215 $aXI, 240 p.$d22 cm 410 0$12001 454 1$12001 461 1$1001-------$12001 606 0 $aSviluppo sostenibile$2BNCF 676 $a363.7 700 1$aBOERO,$bFerdinando$0616120 801 0$aIT$bsalbc$gISBD 912 $a990005786030203316 951 $a363.7 BOE 1$b20506 G.$c363.7$d00317880 959 $aBK 969 $aSOS 979 $aFIORELLA$b90$c20121203$lUSA01$h1518 979 $aFIORELLA$b90$c20121203$lUSA01$h1525 996 $aEconomia senza natura$91081438 997 $aUNISA LEADER 05676nam 2200757Ia 450 001 9911019526403321 005 20200520144314.0 010 $a9786612188442 010 $a9781282188440 010 $a1282188445 010 $a9780470496916 010 $a0470496916 010 $a9780470496909 010 $a0470496908 035 $a(CKB)1000000000773898 035 $a(EBL)448946 035 $a(OCoLC)457179650 035 $a(SSID)ssj0000201616 035 $a(PQKBManifestationID)11203161 035 $a(PQKBTitleCode)TC0000201616 035 $a(PQKBWorkID)10245673 035 $a(PQKB)11664266 035 $a(MiAaPQ)EBC448946 035 $a(PPN)183428307 035 $a(Perlego)2764123 035 $a(EXLCZ)991000000000773898 100 $a20090429d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMetaheuristics $efrom design to implementation /$fEl-ghazali Talbi 210 $aHoboken, NJ $cJohn Wiley & Sons$d2009 215 $a1 online resource (625 p.) 225 1 $aWiley Series on Parallel and Distributed Computing ;$vv.74 300 $aDescription based upon print version of record. 311 08$a9780470278581 311 08$a0470278587 320 $aIncludes bibliographical references and index. 327 $aMETAHEURISTICS; CONTENTS; Preface; Acknowledgments; Glossary; 1 Common Concepts for Metaheuristics; 1.1 Optimization Models; 1.1.1 Classical Optimization Models; 1.1.2 Complexity Theory; 1.1.2.1 Complexity of Algorithms; 1.1.2.2 Complexity of Problems; 1.2 Other Models for Optimization; 1.2.1 Optimization Under Uncertainty; 1.2.2 Dynamic Optimization; 1.2.2.1 Multiperiodic Optimization; 1.2.3 Robust Optimization; 1.3 Optimization Methods; 1.3.1 Exact Methods; 1.3.2 Approximate Algorithms; 1.3.2.1 Approximation Algorithms; 1.3.3 Metaheuristics; 1.3.4 Greedy Algorithms 327 $a1.3.5 When Using Metaheuristics?1.4 Main Common Concepts for Metaheuristics; 1.4.1 Representation; 1.4.1.1 Linear Representations; 1.4.1.2 Nonlinear Representations; 1.4.1.3 Representation-Solution Mapping; 1.4.1.4 Direct Versus Indirect Encodings; 1.4.2 Objective Function; 1.4.2.1 Self-Sufficient Objective Functions; 1.4.2.2 Guiding Objective Functions; 1.4.2.3 Representation Decoding; 1.4.2.4 Interactive Optimization; 1.4.2.5 Relative and Competitive Objective Functions; 1.4.2.6 Meta-Modeling; 1.5 Constraint Handling; 1.5.1 Reject Strategies; 1.5.2 Penalizing Strategies 327 $a1.5.3 Repairing Strategies1.5.4 Decoding Strategies; 1.5.5 Preserving Strategies; 1.6 Parameter Tuning; 1.6.1 Off-Line Parameter Initialization; 1.6.2 Online Parameter Initialization; 1.7 Performance Analysis of Metaheuristics; 1.7.1 Experimental Design; 1.7.2 Measurement; 1.7.2.1 Quality of Solutions; 1.7.2.2 Computational Effort; 1.7.2.3 Robustness; 1.7.2.4 Statistical Analysis; 1.7.2.5 Ordinal Data Analysis; 1.7.3 Reporting; 1.8 Software Frameworks for Metaheuristics; 1.8.1 Why a Software Framework for Metaheuristics?; 1.8.2 Main Characteristics of Software Frameworks 327 $a1.8.3 ParadisEO Framework1.8.3.1 ParadisEO Architecture; 1.9 Conclusions; 1.10 Exercises; 2 Single-Solution Based Metaheuristics; 2.1 Common Concepts for Single-Solution Based Metaheuristics; 2.1.1 Neighborhood; 2.1.2 Very Large Neighborhoods; 2.1.2.1 Heuristic Search in Large Neighborhoods; 2.1.2.2 Exact Search in Large Neighborhoods; 2.1.2.3 Polynomial-Specific Neighborhoods; 2.1.3 Initial Solution; 2.1.4 Incremental Evaluation of the Neighborhood; 2.2 Fitness Landscape Analysis; 2.2.1 Distances in the Search Space; 2.2.2 Landscape Properties; 2.2.2.1 Distribution Measures 327 $a2.2.2.2 Correlation Measures2.2.3 Breaking Plateaus in a Flat Landscape; 2.3 Local Search; 2.3.1 Selection of the Neighbor; 2.3.2 Escaping from Local Optima; 2.4 Simulated Annealing; 2.4.1 Move Acceptance; 2.4.2 Cooling Schedule; 2.4.2.1 Initial Temperature; 2.4.2.2 Equilibrium State; 2.4.2.3 Cooling; 2.4.2.4 Stopping Condition; 2.4.3 Other Similar Methods; 2.4.3.1 Threshold Accepting; 2.4.3.2 Record-to-Record Travel; 2.4.3.3 Great Deluge Algorithm; 2.4.3.4 Demon Algorithms; 2.5 Tabu Search; 2.5.1 Short-Term Memory; 2.5.2 Medium-Term Memory; 2.5.3 Long-Term Memory; 2.6 Iterated Local Search 327 $a2.6.1 Perturbation Method 330 $aA unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a 410 0$aWiley Series on Parallel and Distributed Computing 606 $aMathematical optimization 606 $aHeuristic programming 606 $aProblem solving$xData processing 606 $aComputer algorithms 615 0$aMathematical optimization. 615 0$aHeuristic programming. 615 0$aProblem solving$xData processing. 615 0$aComputer algorithms. 676 $a519.6 700 $aTalbi$b El-Ghazali$f1965-$0786036 712 02$aWiley Online Library (Servicio en línea) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019526403321 996 $aMetaheuristics$91750106 997 $aUNINA