Optimization Techniques for Solving Complex Problems
Wiley Series on Parallel and Distributed Computing
1. Edition April 2009
504 Pages, Hardcover
Wiley & Sons Ltd
Short Description
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.
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.
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.
1. Generating Automatic Projections by Means of GP (C. Estébanez,and R. Aler).
References.
2. Neural Lazy Local Learning (J. M. Valls, I. M. Galván, and P. Isasi).
References.
3. Optimization by Using GAs with Micropopulations (Y. Sáez).
References.
4. Analyzing Parallel Cellular Genetic Algorithms (G. Luque, E. Alba, and B. Dorronsoro).
References.
5. Evaluating New Advanced Multiobjective Metaheuristics (A. J. Nebro, J.J. Durillo, F. Luna, and E. Alba).
References.
6. Canonical Metaheuristics for DOPs (G. Leguizamón, G. Ordóñez, S. Molina, and E. Alba).
References.
7. Solving Constrained Optimization Problems with HEAs (C. Cotta, and A. J. Fernández).
References.
8. Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques (J. A. Gomez, M. D. Jaraiz, M. A. Vega, and J. M. Sanchez).
References.
9. Using Reconfigurable Computing to Optimization of Cryptographic Algorithms (J. M. Granado, M. A. Vega, J. M. Sanchez, and J. A. Gomez).
References.
10. Genetic Algorithms, Parallelism and Reconfigurable Hardware (J. M. Sanchez, M. Rubio, M. A. Vega, and J. A. Gomez).
References.
11. Divide and Conquer, Advanced Techniques (C. Lóon, G. Miranda, and C. Rodriguez).
References.
12. Tools for Tree Searches: Branch and Bound and A* Algorithms (C. León, G. Miranda, and C. Rodriguez).
References.
13. Tools for Tree Searches: Dynamic Programming (C. León, G. Miranda, and C. Rodriguez).
Approach.
References.
PART II: APPLICATIONS.
14. Automatic Search of Behavior Strategies in Auctions (D. Quintana, and A. Mochón).
References.
15. Evolving Rules For Local Time Series Prediction (C. Luque, J. M. Valls, and P. Isasi).
References.
16. Metaheuristics in Bioinformatics (C. Cotta, A. J. Fernández, J. E. Gallardo, G. Luque, and E. Alba).
References.
17. Optimal Location of Antennae in Telecommunication Networks (G. Molina, F. Chicano, and E. Alba).
References.
18. Optimization of Image Processing Algorithms Using FPGAs (M. A. Vega, A. Gomez, J. A. Gomez, and J. M. Sanchez).
References.
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.
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.
