Optimization Techniques for Solving Complex Problems

, by Alba, Enrique; Blum, Christian; Asasi, Pedro; Leon, Coromoto; Gomez, Juan Antonio

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ISBN: 9780470293324 | 0470293322
Cover: Hardcover
Copyright: 3/23/2009

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Solving Complex Problems addresses real problems and the modern optimization techniques used to solve them. Thorough examples illustrate the applications themselves, as well as the actual performance of the algorithms. Application areas include computer science, engineering, transportation, telecommunications, and bioinformatics, making the book especially useful to practitioners in those areas.

Contributors	p. xv
Foreword	p. xix
Preface	p. xxi
Methodologies for Complex Problem Solving	p. 1
Generating Automatic Projections by Means of Genetic Programming	p. 3
Introduction	p. 3
Background	p. 4
Domains	p. 6
Algorithmic Proposal	p. 6
Experimental Analysis	p. 9
Conclusions	p. 11
References	p. 13
Neural Lazy Local Learning	p. 15
Introduction	p. 15
Lazy Radial Basis Neural Networks	p. 17
Experimental Analysis	p. 22
Conclusions	p. 28
References	p. 30
Optimization Using Genetic Algorithms with Micropopulations	p. 31
Introduction	p. 31
Algorithmic Proposal	p. 33
Experimental Analysis: The Rastrigin Function	p. 40
Conclusions	p. 44
References	p. 45
Analyzing Parallel Cellular Genetic Algorithms	p. 49
Introduction	p. 49
Cellular Genetic Algorithms	p. 50
Parallel Models for cGAs	p. 51
Brief Survey of Parallel cGAs	p. 52
Experimental Analysis	p. 55
Conclusions	p. 59
References	p. 59
Evaluating New Advanced Multiobjective Metaheuristics	p. 63
Introduction	p. 63
Background	p. 65
Description of the Metaheuristics	p. 67
Experimental Methodology	p. 69
Experimental Analysis	p. 72
Conclusions	p. 79
References	p. 80
Canonical Metaheuristics for Dynamic Optimization Problems	p. 83
Introduction	p. 83
Dynamic Optimization Problems	p. 84
Canonical MHs for DOPs	p. 88
Benchmarks	p. 92
Metrics	p. 93
Conclusions	p. 95
References	p. 96
Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms	p. 101
Introduction	p. 101
Strategies for Solving CCOPs with HEAs	p. 103
Study Cases	p. 105
Conclusions	p. 114
References	p. 115
Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques	p. 123
Introduction	p. 123
Time Series Identification	p. 124
Optimization Problem	p. 125
Algorithmic Proposal	p. 130
Experimental Analysis	p. 132
Conclusions	p. 136
References	p. 136
Using Reconfigurable Computing for the Optimization of Cryptographic Algorithms	p. 139
Introduction	p. 139
Description of the Cryptographic Algorithms	p. 140
Implementation Proposal	p. 144
Expermental Analysis	p. 153
Conclusions	p. 154
References	p. 155
Genetic Algorithms, Parallelism, and Reconfigurable Hardware	p. 159
Introduction	p. 159
State of the Art	p. 161
FPGA Problem Description and Solution	p. 162
Algorithmic Proposal	p. 169
Experimental Analysis	p. 172
Conclusions	p. 177
References	p. 177
Divide and Conquer: Advanced Techniques	p. 179
Introduction	p. 179
Algorithm of the Skeleton	p. 180
Experimental Analysis	p. 185
Conclusions	p. 189
References	p. 190
Tools for Tree Searches: Branch-and-Bound and A* Algorithms	p. 193
Introduction	p. 193
Background	p. 195
Algorithmic Skeleton for Tree Searches	p. 196
Experimentation Methodology	p. 199
Experimental Results	p. 202
Conclusions	p. 205
References	p. 206
Tools for Tree Searches: Dynamic Programming	p. 209
Introduction	p. 209
Top-Down Approach	p. 210
Bottom-Up Approach	p. 212
Automata Theory and Dynamic Programming	p. 215
Parallel Algorithms	p. 223
Dynamic Programming Heuristics	p. 225
Conclusions	p. 228
References	p. 229
Applications	p. 231
Automatic Search of Behavior Strategies in Auctions	p. 233
Introduction	p. 233
Evolutionary Techniques in Auctions	p. 234
Theoretical Framework: The Ausubel Auction	p. 238
Algorithmic Proposal	p. 241
Experimental Analysis	p. 243
Conclusions	p. 246
References	p. 247
Evolving Rules for Local Time Series Prediction	p. 249
Introduction	p. 249
Evolutionary Algorithms for Generating Prediction Rules	p. 250
Experimental Methodology	p. 250
Experiments	p. 256
Conclusions	p. 262
References	p. 263
Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction	p. 265
Introduction	p. 265
Metaheuristics and Bioinformatics	p. 266
DNA Fragment Assembly Problem	p. 270
Shortest Common Supersequence Problem	p. 278
Conclusions	p. 282
References	p. 283
Optimal Location of Antennas in Telecommunication Networks	p. 287
Introduction	p. 287
State of the Art	p. 288
Radio Network Design Problem	p. 292
Optimization Algorithms	p. 294
Basic Problems	p. 297
Advanced Problem	p. 303
Conclusions	p. 305
References	p. 306
Optimization of Image-Processing Algorithms Using FPGAs	p. 309
Introduction	p. 309
Background	p. 310
Main Features of FPGA-Based Image Processing	p. 311
Advanced Details	p. 312
Experimental Analysis: Software Versus FPGA	p. 321
Conclusions	p. 322
References	p. 323
Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics	p. 325
Introduction	p. 325
Background	p. 326
Laser Dynamics Problem	p. 328
Algorithmic Proposal	p. 329
Experimental Analysis	p. 331
Parallel Implementation of the Algorithm	p. 336
Conclusions	p. 344
References	p. 344
Dense Stereo Disparity from an Artificial Life Standpoint	p. 347
Introduction	p. 347
Infection Algorithm with an Evolutionary Approach	p. 351
Experimental Analysis	p. 360
Conclusions	p. 363
References	p. 363
Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems	p. 365
Introduction	p. 365
Multidimensional Knapsack Problem	p. 370
Hybrid Models	p. 372
Experimental Analysis	p. 377
Conclusions	p. 379
References	p. 380
Greedy Seeding and Problem-Specific Operators for GAs Solution of Strip Packing Problems	p. 385
Introduction	p. 385
Background	p. 386
Hybrid GA for the 2SPP	p. 387
Genetic Operators for Solving the 2SPP	p. 388
Initial Seeding	p. 390
Implementation of the Algorithms	p. 391
Experimental Analysis	p. 392
Conclusions	p. 403
References	p. 404
Solving the KCT Problem: Large-Scale Neighborhood Search and Solution Merging	p. 407
Introduction	p. 407
Hybrid Algorithms for the KCT Problem	p. 409
Experimental Analysis	p. 415
Conclusions	p. 416
References	p. 419
Experimental Study of GA-Based Schedulers in Dynamic Distributed Computing Environments	p. 423
Introduction	p. 423
Related Work	p. 425
Independent Job Scheduling Problem	p. 426
Genetic Algorithms for Scheduling in Grid Systems	p. 428
Grid Simulator	p. 429
Interface for Using a GA-Based Scheduler with the Grid Simulator	p. 432
Experimental Analysis	p. 433
Conclusions	p. 438
References	p. 439
Remote Optimization Service	p. 443
Introduction	p. 443
Background and State of the Art	p. 444
ROS Architecture	p. 446
Information Exchange in ROS	p. 448
XML in ROS	p. 449
Wrappers	p. 450
Evaluation of ROS	p. 451
Conclusions	p. 454
References	p. 455
Remote Services for Advanced Problem Optimization	p. 457
Introduction	p. 457
SIRVA	p. 458
MOSET and TIDESI	p. 462
ABACUS	p. 465
References	p. 470
Index	p. 473
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Optimization Techniques for Solving Complex Problems

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