TY  - BOOK
AU  - Alba,Enrique
TI  - Optimization techniques for solving complex problems
T2  - Wiley series on parallel and distributed computing
SN  - 9780470293324
AV  - QA76.9.M35 O68 2009
PY  - 2009///
CY  - Hoboken, N.J.
PB  - Wiley
KW  - Computer science
KW  - Mathematics
KW  - Mathematical optimization
KW  - Problem solving
N1  - Includes bibliographical references and index; PART I: METHODOLOGIES FOR COMPLEX PROBLEM SOLVING.	; 1. Generating Automatic Projections by Means of GP (C. Estébanez,and R. Aler); 1.1 Introduction; 1.2 Background; 1.3 Domains; 1.4 Algorithmic Proposal; 1.5 Experimental Analysis; 1.6 Conclusions and Future Work; References; 2. Neural Lazy Local Learning (J. M. Valls, I. M. Galván, and P. Isasi); 2.1 Introduction; 2.2 LRBNN: Lazy Radial Basis Neural Networks; 2.3 Experimental Framework; 2.4 Conclusions; References; 3. Optimization by Using GAs with Micropopulations (Y. Sáez); 3.1 Introduction; 3.2 Algorithmic Proposal; 3.3 Experimental Analysis: the Rastrigin Function; 3.4 Conclusions; References; 4. Analyzing Parallel Cellular Genetic Algorithms (G. Luque, E. Alba, and B. Dorronsoro); 4.1 Introduction; 4.2 Cellular Genetic Algorithms; 4.3 Parallel Models for cGAs; 4.4 Brief Survey on Parallel cGAs; 4.5 Experimental Results; 4.6 Conclusions; References; 5. Evaluating New Advanced Multiobjective Metaheuristics (A. J. Nebro, J.J. Durillo, F. Luna, and E. Alba); 5.1 Introduction; 5.2 Background; 5.3 Description of the Metaheuristics; 5.4 Experimentation Methodology; 5.5 Computational Results; 5.6 Conclusions and Future Work; References; 6. Canonical Metaheuristics for DOPs (G. Leguizamón, G. Ordóñez, S. Molina, and E. Alba); 6.1 Introduction; 6.2 Dynamic Optimization Problems; 6.3 Canonical MHs for DOPs; 6.4 Benchmarks; 6.5 Metrics; 6.6 Conclusions; References; 7. Solving Constrained Optimization Problems with HEAs (C. Cotta, and A. J. Fernández); 7.1 Introduction; 7.2 Strategies for Solving CCOPs with HEAs; 7.3 Study Cases; 7.4 Conclusions; 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); 8.1 Introduction; 8.2 Time Series Identification; 8.3 Optimization Problem; 8.4 Algorithmic Proposal; 8.5 Experimental Analysis; 8.6 Conclusions and Future Work; References; 9. Using Reconfigurable Computing to Optimization of Cryptographic Algorithms (J. M. Granado, M. A. Vega, J. M. Sanchez, and J. A. Gomez); 9.1 Introduction; 9.2 Description of the Cryptographic Algorithms; 9.3 Implementation Proposal; 9.4 Results; 9.5 Conclusions; References; 10. Genetic Algorithms, Parallelism and Reconfigurable Hardware (J. M. Sanchez, M. Rubio, M. A. Vega, and J. A. Gomez); 10.1 Introduction; 10.2 State of the Art; 10.3 FPGA Problem Description and Solution; 10.4 Algorithmic Proposal; 10.5 Experiments and Results; 10.6 Conclusions and Future Work; References; 11. Divide and Conquer, Advanced Techniques (C. Lóon, G. Miranda, and C. Rodriguez); 11.1 Introduction; 11.2 The Algorithm of the Skeleton; 11.3 Computational Results; 11.4 Conclusions; References; 12. Tools for Tree Searches: Branch and Bound and A* Algorithms (C. León, G. Miranda, and C. Rodriguez); 12.1 Introduction; 12.2 Background; 12.3 Algorithmic Skeleton for Tree Searches; 12.4 Experimentation Methodology; 12.5 Computational Results; 12.6 Conclusions and Future Work; References; 13. Tools for Tree Searches: Dynamic Programming (C. León, G. Miranda, and C. Rodriguez); 13.1 Introduction; 13.2 The TopDown; Approach; 13.3 The BottomUp Approach; 13.4 Automata Theory and Dynamic Programming; 13.5 Parallel Algorithms; 13.6 Dynamic Programming Heuristics; 13.7 Conclusions; References; PART II: APPLICATIONS.	; 14. Automatic Search of Behavior Strategies in Auctions (D. Quintana, and A. Mochón); 14.1 Introduction; 14.2 Evolutionary Techniques in Auctions; 14.3 Theoretical Framework: the Ausubel Auction; 14.4 Algorithmic Proposal; 14.5 Experimental analysis; 14.6 Conclusions and Future Work; References; 15. Evolving Rules For Local Time Series Prediction (C. Luque, J. M. Valls, and P. Isasi); 15.1 Introduction; 15.2 Evolutionary Algorithms for Generating Prediction Rules; 15.3 Description of the Method; 15.4 Experiments; 15.5 Conclusions; References; 16. Metaheuristics in Bioinformatics (C. Cotta, A. J. Fernández, J. E. Gallardo, G. Luque, and E. Alba); 16.1 Introduction; 16.2 Metaheuristics and Bioinformatics; 16.3 The DNA Fragment Assembly Problem; 16.4 The Shortest Common Supersequence Problem; 16.5 Conclusions; References; 17. Optimal Location of Antennae in Telecommunication Networks (G. Molina, F. Chicano, and E. Alba); 17.1 Introduction; 17.2 State of the Art; 17.3 Radio Network Design Problem; 17.4 Optimization Algorithms; 17.5 Basic Problem Instances; 17.6 Advanced Problem Instance; 17.7 Conclusions; References; 18. Optimization of Image Processing Algorithms Using FPGAs (M. A. Vega, A. Gomez, J. A. Gomez, and J. M. Sanchez); 18.1 Introduction; 18.2 Background; 18.3 Main Features of the FPGAbased Image Processing; 18.4 Advanced Details; 18.5 Experimental Analysis: Software vs. FPGA; 18.6 Conclusions; 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); 19.1 Introduction; 19.2 Background; 19.3 The Problem: Laser Dynamics; 19.4 Algorithmic Proposal; 19.5 Experimental Analysis; 19.6 Parallel Implementation of the Algorithm; 19.7 Conclusions and Future Work; References; 20. Dense Stereo Disparity from an ALife Standpoint (G. Olague, F. Fernandez, C. B. Perez, and E. Lutton); 20.1 Introduction; 20.2 Infection Algorithm with an Evolutionary Approach; 20.3 Experimental Results; 20.4 Conclusion; References; 21. Approaches to Multidimensional Knapsack Problems (J. E. Gallardo, C. Cotta, and A. J. Fernández); 21.1 Introduction; 21.2 The Multidimensional Knapsack Problem; 21.3 Hybrid Models; 21.4 Experimental Results; 21.5 Conclusions and Future Work; References; 22. Greedy Seeding and ProblemSpecific Operators for GAs Solving Strip Packing Problems (C. Salto, J. M. Molina, and E. Alba); 22.1 Introduction; 22.2 Background; 22.3 A Hybrid GA for the 2SPP; 22.4 Genetic Operators for Solving the 2SPP; 22.5 Initial Seeding; 22.6 Implementation; 22.7 Computational Analysis; 22.8 Conclusions; References; 23. Solving the KCT Problem: Large Scale Neighborhood Search and Solution Merging (C. Blum, and M. Blesa); 23.1 Introduction; 23.2 Hybrid Algorithms for the KCT Problem; 23.3 Experimental Evaluation; 23.4 Summary and Conclusions; References; 24. Experimental Study of Gabased Schedulers in Dynamic Distributed Computing Environments (F. Xhafa, and J. Carretero); 24.1 Introduction; 24.2 Related Work; 24.3 Independent Job Scheduling Problem; 24.4 Genetic Algorithms for Scheduling in Grid Systems; 24.5 Grid Simulator; 24.6 The Interface for Using Gabased Scheduler with the Grid Simulator; 24.7 Experimental Analysis; 24.8 Conclusions; References; 25. ROS: Remote Optimization Service (J. GarcíaNieto, F. Chicano, and E. Alba); 25.1 Introduction; 25.2 Background and State of the Art; 25.3 ROS Architecture; 25.4 Information Exchange in ROS; 25.5 XML in ROS; 25.6 Wrappers; 25.7 Evaluation of ROS; 25.8 Conclusions and Future Work; 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); 26.1 Introduction; 26.2 SIRVA.	; 26.3 MOSET and TIDESI; 26.4 ABACUS.	; References; Index
ER  -