Optimization Techniques for Solving Complex Problems
(Sprache: Englisch)
Real world problems and modern optimization techniques to solve them Here, 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,...
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Real world problems and modern optimization techniques to solve them Here, 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.
Klappentext zu „Optimization Techniques for Solving Complex Problems “
Real-world problems and modern optimization techniques to solve themHere, 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.
Part One--covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more.
Part 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.
All 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.
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.
Inhaltsverzeichnis zu „Optimization Techniques for Solving Complex Problems “
PART I: METHODOLOGIES FOR COMPLEX PROBLEM SOLVING1. Generating Automatic Projections by Means of GP (C. Estébanez,and R. Aler)
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2. Neural Lazy Local Learning (J. M. Valls, I. M. Galván, and P. Isasi)
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3. Optimization by Using GAs with Micropopulations (Y. Sáez)
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4. Analyzing Parallel Cellular Genetic Algorithms (G. Luque, E. Alba, and B. Dorronsoro)
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5. Evaluating New Advanced Multiobjective Metaheuristics (A. J. Nebro, J.J. Durillo, F. Luna, and E. Alba)
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6. Canonical Metaheuristics for DOPs (G. Leguizamón, G. Ordóñez, S. Molina, and E. Alba)
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7. Solving Constrained Optimization Problems with HEAs (C. Cotta, and A. J. Fernández)
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8. Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques (J. A. Gomez, M. D. Jaraiz, M. A. Vega, and J. M. Sanchez)
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9. Using Reconfigurable Computing to Optimization of Cryptographic Algorithms (J. M. Granado, M. A. Vega, J. M. Sanchez, and J. A. Gomez)
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10. Genetic Algorithms, Parallelism and Reconfigurable Hardware (J. M. Sanchez, M. Rubio, M. A. Vega, and J. A. Gomez)
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11. Divide and Conquer, Advanced Techniques (C. Lóon, G. Miranda, and C. Rodriguez)
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12. Tools for Tree Searches: Branch and Bound and A* Algorithms (C. León, G. Miranda, and C. Rodriguez)
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13. Tools for Tree Searches: Dynamic Programming (C. León, G. Miranda, and C. Rodriguez)
Approach
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PART II: APPLICATIONS
14. Automatic Search of Behavior Strategies in Auctions (D. Quintana, and A. Mochón)
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15. Evolving Rules For Local Time Series Prediction (C. Luque, J. M. Valls, and P. Isasi)
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16. Metaheuristics in Bioinformatics (C. Cotta, A. J. Fernández, J. E. Gallardo, G. Luque, and E. Alba)
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17. Optimal Location of Antennae in Telecommunication Networks (G. Molina, F. Chicano, and E.
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Alba)
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18. Optimization of Image Processing Algorithms Using FPGAs (M. A. Vega, A. Gomez, J. A. Gomez, and J. M. Sanchez)
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19. Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics (J. L. Guisado, F. Jiménez Morales, J. M. Guerra, F. Fernández de Vega)
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20. Dense Stereo Disparity from an ALife Standpoint (G. Olague, F. Fernandez, C. B. Perez, and E. Lutton)
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21. Approaches to Multidimensional Knapsack Problems (J. E. Gallardo, C. Cotta, and A. J. Fernández)
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22. Greedy Seeding and ProblemSpecific Operators for GAs Solving Strip Packing Problems (C. Salto, J. M. Molina, and E. Alba)
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23. Solving the KCT Problem: Large Scale Neighborhood Search and Solution Merging (C. Blum, and M. Blesa)
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24. Experimental Study of Gabased Schedulers in Dynamic Distributed Computing Environments (F. Xhafa, and J. Carretero)
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25. ROS: Remote Optimization Service (J. GarcíaNieto, F. Chicano, and E. Alba)
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26. SIRVA, MOSET, TIDESI, ABACUS: Remote Services for Advanced
Problem Optimization (J. A. Gomez, M. A. Vega, J. M. Sanchez, J. L. Guisado, D. Lombrana, and F. Fernandez)
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Index
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18. Optimization of Image Processing Algorithms Using FPGAs (M. A. Vega, A. Gomez, J. A. Gomez, and J. M. Sanchez)
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19. Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics (J. L. Guisado, F. Jiménez Morales, J. M. Guerra, F. Fernández de Vega)
References
20. Dense Stereo Disparity from an ALife Standpoint (G. Olague, F. Fernandez, C. B. Perez, and E. Lutton)
References
21. Approaches to Multidimensional Knapsack Problems (J. E. Gallardo, C. Cotta, and A. J. Fernández)
References
22. Greedy Seeding and ProblemSpecific Operators for GAs Solving Strip Packing Problems (C. Salto, J. M. Molina, and E. Alba)
References
23. Solving the KCT Problem: Large Scale Neighborhood Search and Solution Merging (C. Blum, and M. Blesa)
References
24. Experimental Study of Gabased Schedulers in Dynamic Distributed Computing Environments (F. Xhafa, and J. Carretero)
References
25. ROS: Remote Optimization Service (J. GarcíaNieto, F. Chicano, and E. Alba)
References
26. SIRVA, MOSET, TIDESI, ABACUS: Remote Services for Advanced
Problem Optimization (J. A. Gomez, M. A. Vega, J. M. Sanchez, J. L. Guisado, D. Lombrana, and F. Fernandez)
References
Index
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Autoren-Porträt
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.
Bibliographische Angaben
- 2009, 1. Auflage, 504 Seiten, Maße: 12,8 x 28,4 cm, Gebunden, Englisch
- Herausgegeben: Enrique Alba, Christian Blum, Pedro Asasi, Coromoto Leon, Juan Antonio Gomez
- Verlag: Wiley & Sons
- ISBN-10: 0470293322
- ISBN-13: 9780470293324
- Erscheinungsdatum: 20.04.2009
Sprache:
Englisch
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