{"product_id":"optimization-techniques-for-solving-complex-problems-hardback-9780470293324","title":"Optimization Techniques for Solving Complex Problems (Hardback) 9780470293324","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOptimization Techniques for Solving Complex Problems\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eEnrique Alba (Edited by), E Alba (Author), Christian Blum (Edited by), Pedro Asasi (Edited by), Coromoto Leon (Edited by), Juan Antonio Gomez (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470293324, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 9 April 2009\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e500 pages\u003cbr\u003e23.8 x 16.4 x 3.1 cm, 0.78 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eReal-world problems and modern optimization techniques to solve them  \u003cp\u003eHere, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics.\u003c\/p\u003e \u003cp\u003ePart One—covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more.\u003c\/p\u003e \u003cp\u003ePart Two—delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more.\u003c\/p\u003e \u003cp\u003eAll chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eContributors xv\u003c\/p\u003e \u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Methodologies for Complex Problem Solving 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Generating Automatic Projections by Means of Genetic Programming 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Estébanez and R. Aler\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Background 4\u003c\/p\u003e \u003cp\u003e1.3 Domains 6\u003c\/p\u003e \u003cp\u003e1.4 Algorithmic Proposal 6\u003c\/p\u003e \u003cp\u003e1.5 Experimental Analysis 9\u003c\/p\u003e \u003cp\u003e1.6 Conclusions 11\u003c\/p\u003e \u003cp\u003eReferences 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Neural Lazy Local Learning 15\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. M. Valls, I. M. Galván, and P. Isasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 15\u003c\/p\u003e \u003cp\u003e2.2 Lazy Radial Basis Neural Networks 17\u003c\/p\u003e \u003cp\u003e2.3 Experimental Analysis 22\u003c\/p\u003e \u003cp\u003e2.4 Conclusions 28\u003c\/p\u003e \u003cp\u003eReferences 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Optimization Using Genetic Algorithms with Micropopulations 31\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eY. Sáez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 31\u003c\/p\u003e \u003cp\u003e3.2 Algorithmic Proposal 33\u003c\/p\u003e \u003cp\u003e3.3 Experimental Analysis: The Rastrigin Function 40\u003c\/p\u003e \u003cp\u003e3.4 Conclusions 44\u003c\/p\u003e \u003cp\u003eReferences 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Analyzing Parallel Cellular Genetic Algorithms 49\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Luque, E. Alba, and B. Dorronsoro\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 49\u003c\/p\u003e \u003cp\u003e4.2 Cellular Genetic Algorithms 50\u003c\/p\u003e \u003cp\u003e4.3 Parallel Models for cGAs 51\u003c\/p\u003e \u003cp\u003e4.4 Brief Survey of Parallel cGAs 52\u003c\/p\u003e \u003cp\u003e4.5 Experimental Analysis 55\u003c\/p\u003e \u003cp\u003e4.6 Conclusions 59\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Evaluating New Advanced Multiobjective Metaheuristics 63\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eA. J. Nebro, J. J. Durillo, F. Luna, and E. Alba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 63\u003c\/p\u003e \u003cp\u003e5.2 Background 65\u003c\/p\u003e \u003cp\u003e5.3 Description of the Metaheuristics 67\u003c\/p\u003e \u003cp\u003e5.4 Experimental Methodology 69\u003c\/p\u003e \u003cp\u003e5.5 Experimental Analysis 72\u003c\/p\u003e \u003cp\u003e5.6 Conclusions 79\u003c\/p\u003e \u003cp\u003eReferences 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Canonical Metaheuristics for Dynamic Optimization Problems 83\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Leguizam\u003c\/i\u003e\u003ci\u003eón, G. Ord\u003c\/i\u003e\u003ci\u003eó\u003c\/i\u003e\u003ci\u003eñez, S. Molina, and E. Alba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 83\u003c\/p\u003e \u003cp\u003e6.2 Dynamic Optimization Problems 84\u003c\/p\u003e \u003cp\u003e6.3 Canonical MHs for DOPs 88\u003c\/p\u003e \u003cp\u003e6.4 Benchmarks 92\u003c\/p\u003e \u003cp\u003e6.5 Metrics 93\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 95\u003c\/p\u003e \u003cp\u003eReferences 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms 101\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Cotta and A. J. Fernández\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 101\u003c\/p\u003e \u003cp\u003e7.2 Strategies for Solving CCOPs with HEAs 103\u003c\/p\u003e \u003cp\u003e7.3 Study Cases 105\u003c\/p\u003e \u003cp\u003e7.4 Conclusions 114\u003c\/p\u003e \u003cp\u003eReferences 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques 123\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. A. G\u003c\/i\u003e\u003ci\u003eómez, M. D. Jaraiz, M. A. Vega, and J. M. Sánchez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 123\u003c\/p\u003e \u003cp\u003e8.2 Time Series Identification 124\u003c\/p\u003e \u003cp\u003e8.3 Optimization Problem 125\u003c\/p\u003e \u003cp\u003e8.4 Algorithmic Proposal 130\u003c\/p\u003e \u003cp\u003e8.5 Experimental Analysis 132\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 136\u003c\/p\u003e \u003cp\u003eReferences 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Using Reconfigurable Computing for the Optimization of Cryptographic Algorithms 139\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. M. Granado, M. A. Vega, J. M. Sánchez, and J. A. G\u003c\/i\u003e\u003ci\u003eómez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 139\u003c\/p\u003e \u003cp\u003e9.2 Description of the Cryptographic Algorithms 140\u003c\/p\u003e \u003cp\u003e9.3 Implementation Proposal 144\u003c\/p\u003e \u003cp\u003e9.4 Expermental Analysis 153\u003c\/p\u003e \u003cp\u003e9.5 Conclusions 154\u003c\/p\u003e \u003cp\u003eReferences 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Genetic Algorithms, Parallelism, and Reconfigurable Hardware 159\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. M. Sánchez, M. Rubio, M. A. Vega, and J. A. G\u003c\/i\u003e\u003ci\u003eómez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 159\u003c\/p\u003e \u003cp\u003e10.2 State of the Art 161\u003c\/p\u003e \u003cp\u003e10.3 FPGA Problem Description and Solution 162\u003c\/p\u003e \u003cp\u003e10.4 Algorithmic Proposal 169\u003c\/p\u003e \u003cp\u003e10.5 Experimental Analysis 172\u003c\/p\u003e \u003cp\u003e10.6 Conclusions 177\u003c\/p\u003e \u003cp\u003eReferences 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Divide and Conquer: Advanced Techniques 179\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Le\u003c\/i\u003e\u003ci\u003eón, G. Miranda, and C. Rodríguez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 179\u003c\/p\u003e \u003cp\u003e11.2 Algorithm of the Skeleton 180\u003c\/p\u003e \u003cp\u003e11.3 Experimental Analysis 185\u003c\/p\u003e \u003cp\u003e11.4 Conclusions 189\u003c\/p\u003e \u003cp\u003eReferences 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Tools for Tree Searches: Branch-and-Bound and A\u003c\/b\u003e\u003cb\u003e\u003csup\u003e∗\u003c\/sup\u003e Algorithms 193\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Le\u003c\/i\u003e\u003ci\u003eón, G. Miranda, and C. Rodríguez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 193\u003c\/p\u003e \u003cp\u003e12.2 Background 195\u003c\/p\u003e \u003cp\u003e12.3 Algorithmic Skeleton for Tree Searches 196\u003c\/p\u003e \u003cp\u003e12.4 Experimentation Methodology 199\u003c\/p\u003e \u003cp\u003e12.5 Experimental Results 202\u003c\/p\u003e \u003cp\u003e12.6 Conclusions 205\u003c\/p\u003e \u003cp\u003eReferences 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Tools for Tree Searches: Dynamic Programming 209\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Le\u003c\/i\u003e\u003ci\u003eón, G. Miranda, and C. Rodríguez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 209\u003c\/p\u003e \u003cp\u003e13.2 Top-Down Approach 210\u003c\/p\u003e \u003cp\u003e13.3 Bottom-Up Approach 212\u003c\/p\u003e \u003cp\u003e13.4 Automata Theory and Dynamic Programming 215\u003c\/p\u003e \u003cp\u003e13.5 Parallel Algorithms 223\u003c\/p\u003e \u003cp\u003e13.6 Dynamic Programming Heuristics 225\u003c\/p\u003e \u003cp\u003e13.7 Conclusions 228\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Applications 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Automatic Search of Behavior Strategies in Auctions 233\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eD. Quintana and A. Moch\u003c\/i\u003e\u003ci\u003eón\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 233\u003c\/p\u003e \u003cp\u003e14.2 Evolutionary Techniques in Auctions 234\u003c\/p\u003e \u003cp\u003e14.3 Theoretical Framework: The Ausubel Auction 238\u003c\/p\u003e \u003cp\u003e14.4 Algorithmic Proposal 241\u003c\/p\u003e \u003cp\u003e14.5 Experimental Analysis 243\u003c\/p\u003e \u003cp\u003e14.6 Conclusions 246\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Evolving Rules for Local Time Series Prediction 249\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Luque, J. M. Valls, and P. Isasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 249\u003c\/p\u003e \u003cp\u003e15.2 Evolutionary Algorithms for Generating Prediction Rules 250\u003c\/p\u003e \u003cp\u003e15.3 Experimental Methodology 250\u003c\/p\u003e \u003cp\u003e15.4 Experiments 256\u003c\/p\u003e \u003cp\u003e15.5 Conclusions 262\u003c\/p\u003e \u003cp\u003eReferences 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction 265\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Cotta, A. J. Fernández, J. E. Gallardo, G. Luque, and E. Alba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 265\u003c\/p\u003e \u003cp\u003e16.2 Metaheuristics and Bioinformatics 266\u003c\/p\u003e \u003cp\u003e16.3 DNA Fragment Assembly Problem 270\u003c\/p\u003e \u003cp\u003e16.4 Shortest Common Supersequence Problem 278\u003c\/p\u003e \u003cp\u003e16.5 Conclusions 282\u003c\/p\u003e \u003cp\u003eReferences 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Optimal Location of Antennas in Telecommunication Networks 287\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Molina, F. Chicano, and E. Alba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 287\u003c\/p\u003e \u003cp\u003e17.2 State of the Art 288\u003c\/p\u003e \u003cp\u003e17.3 Radio Network Design Problem 292\u003c\/p\u003e \u003cp\u003e17.4 Optimization Algorithms 294\u003c\/p\u003e \u003cp\u003e17.5 Basic Problems 297\u003c\/p\u003e \u003cp\u003e17.6 Advanced Problem 303\u003c\/p\u003e \u003cp\u003e17.7 Conclusions 305\u003c\/p\u003e \u003cp\u003eReferences 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Optimization of Image-Processing Algorithms Using FPGAs 309\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eM. A. Vega, A. G\u003c\/i\u003e\u003ci\u003eómez, J. A. G\u003c\/i\u003e\u003ci\u003eómez, and J. M. Sánchez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 309\u003c\/p\u003e \u003cp\u003e18.2 Background 310\u003c\/p\u003e \u003cp\u003e18.3 Main Features of FPGA-Based Image Processing 311\u003c\/p\u003e \u003cp\u003e18.4 Advanced Details 312\u003c\/p\u003e \u003cp\u003e18.5 Experimental Analysis: Software Versus FPGA 321\u003c\/p\u003e \u003cp\u003e18.6 Conclusions 322\u003c\/p\u003e \u003cp\u003eReferences 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics 325\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. L. Guisado, F. Jiménez-Morales, J. M. Guerra, and F. Fernández\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 325\u003c\/p\u003e \u003cp\u003e19.2 Background 326\u003c\/p\u003e \u003cp\u003e19.3 Laser Dynamics Problem 328\u003c\/p\u003e \u003cp\u003e19.4 Algorithmic Proposal 329\u003c\/p\u003e \u003cp\u003e19.5 Experimental Analysis 331\u003c\/p\u003e \u003cp\u003e19.6 Parallel Implementation of the Algorithm 336\u003c\/p\u003e \u003cp\u003e19.7 Conclusions 344\u003c\/p\u003e \u003cp\u003eReferences 344\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Dense Stereo Disparity from an Artificial Life Standpoint 347\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Olague, F. Fernández, C. B. Pérez, and E. Lutton\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 347\u003c\/p\u003e \u003cp\u003e20.2 Infection Algorithm with an Evolutionary Approach 351\u003c\/p\u003e \u003cp\u003e20.3 Experimental Analysis 360\u003c\/p\u003e \u003cp\u003e20.4 Conclusions 363\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems 365\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. E. Gallardo, C. Cotta, and A. J. Fernández\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 365\u003c\/p\u003e \u003cp\u003e21.2 Multidimensional Knapsack Problem 370\u003c\/p\u003e \u003cp\u003e21.3 Hybrid Models 372\u003c\/p\u003e \u003cp\u003e21.4 Experimental Analysis 377\u003c\/p\u003e \u003cp\u003e21.5 Conclusions 379\u003c\/p\u003e \u003cp\u003eReferences 380\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Greedy Seeding and Problem-Specific Operators for Gas Solution of Strip Packing Problems 385\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Salto, J. M. Molina, and E. Alba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 385\u003c\/p\u003e \u003cp\u003e22.2 Background 386\u003c\/p\u003e \u003cp\u003e22.3 Hybrid GA for the 2SPP 387\u003c\/p\u003e \u003cp\u003e22.4 Genetic Operators for Solving the 2SPP 388\u003c\/p\u003e \u003cp\u003e22.5 Initial Seeding 390\u003c\/p\u003e \u003cp\u003e22.6 Implementation of the Algorithms 391\u003c\/p\u003e \u003cp\u003e22.7 Experimental Analysis 392\u003c\/p\u003e \u003cp\u003e22.8 Conclusions 403\u003c\/p\u003e \u003cp\u003eReferences 404\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Solving the KCT Problem: Large-Scale Neighborhood Search and Solution Merging 407\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Blum and M. J. Blesa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 407\u003c\/p\u003e \u003cp\u003e23.2 Hybrid Algorithms for the KCT Problem 409\u003c\/p\u003e \u003cp\u003e23.3 Experimental Analysis 415\u003c\/p\u003e \u003cp\u003e23.4 Conclusions 416\u003c\/p\u003e \u003cp\u003eReferences 419\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Experimental Study of GA-Based Schedulers in Dynamic Distributed Computing Environments 423\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eF. Xhafa and J. Carretero\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 423\u003c\/p\u003e \u003cp\u003e24.2 Related Work 425\u003c\/p\u003e \u003cp\u003e24.3 Independent Job Scheduling Problem 426\u003c\/p\u003e \u003cp\u003e24.4 Genetic Algorithms for Scheduling in Grid Systems 428\u003c\/p\u003e \u003cp\u003e24.5 Grid Simulator 429\u003c\/p\u003e \u003cp\u003e24.6 Interface for Using a GA-Based Scheduler with the Grid Simulator 432\u003c\/p\u003e \u003cp\u003e24.7 Experimental Analysis 433\u003c\/p\u003e \u003cp\u003e24.8 Conclusions 438\u003c\/p\u003e \u003cp\u003eReferences 439\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Remote Optimization Service 443\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. García-Nieto, F. Chicano, and E. Alba\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 443\u003c\/p\u003e \u003cp\u003e25.2 Background and State of the Art 444\u003c\/p\u003e \u003cp\u003e25.3 ROS Architecture 446\u003c\/p\u003e \u003cp\u003e25.4 Information Exchange in ROS 448\u003c\/p\u003e \u003cp\u003e25.5 XML in ROS 449\u003c\/p\u003e \u003cp\u003e25.6 Wrappers 450\u003c\/p\u003e \u003cp\u003e25.7 Evaluation of ROS 451\u003c\/p\u003e \u003cp\u003e25.8 Conclusions 454\u003c\/p\u003e \u003cp\u003eReferences 455\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Remote Services for Advanced Problem Optimization 457\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. A. G\u003c\/i\u003e\u003ci\u003eómez, M. A. Vega, J. M. Sánchez, J. L. Guisado, D. Lombra\u003c\/i\u003e\u003ci\u003eña, and F. Fernández\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 457\u003c\/p\u003e \u003cp\u003e26.2 SIRVA 458\u003c\/p\u003e \u003cp\u003e26.3 MOSET and TIDESI 462\u003c\/p\u003e \u003cp\u003e26.4 ABACUS 465\u003c\/p\u003e \u003cp\u003eReferences 470\u003c\/p\u003e \u003cp\u003eIndex 473\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52276178944280,"sku":"9780470293324","price":115.98,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470293324.jpg?v=1781364912","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/optimization-techniques-for-solving-complex-problems-hardback-9780470293324","provider":"Freshly Printed Books","version":"1.0","type":"link"}