Optimization Techniques for Solving Complex Problems
, by Alba, Enrique; Blum, Christian; Asasi, Pedro; Leon, Coromoto; Gomez, Juan Antonio- ISBN: 9780470293324 | 0470293322
- Cover: Hardcover
- Copyright: 3/23/2009
Enrique Alba is a Professor of Data Communications and Evolutionary Algorithms at the University of Málaga, Spain. Christian Blum is a Research Fellow at the ALBCOM research group of the Universitat Politècnica de Catalunya, Spain.
Pedro Isasi?is a Professor of Artificial Intelligence at the University Carlos III of Madrid, Spain. Coromoto León is a Professor of Language Processors and Distributed Programming at the University of La Laguna, Spain. Juan Antonio?Gómez is a Professor of Computer Architecture and Reconfigurable Computing at the University of Extremadura, Spain.
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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