MARC ayrıntıları
| 000 -LEADER |
| fixed length control field |
08394nam a2200301 i 4500 |
| 001 - CONTROL NUMBER |
| control field |
28746 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
| fixed length control field |
101021s2009 njudf b 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
| International Standard Book Number |
9780470293324 |
|
| International Standard Book Number |
0470293322 |
| 035 ## - SYSTEM CONTROL NUMBER |
| System control number |
(OCoLC) |
| 040 ## - CATALOGING SOURCE |
| Original cataloging agency |
BAUN |
| Language of cataloging |
eng |
| Transcribing agency |
BAUN |
| Description conventions |
rda |
| 049 ## - LOCAL HOLDINGS (OCLC) |
| Holding library |
BAUN_MERKEZ |
| 050 04 - LIBRARY OF CONGRESS CALL NUMBER |
| Classification number |
QA76.9.M35 |
| Item number |
O68 2009 |
| 245 00 - TITLE STATEMENT |
| Title |
Optimization techniques for solving complex problems / |
| Statement of responsibility, etc |
editör, Enrique Alba [and others] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
| Place of production, publication, distribution, manufacture |
Hoboken, N.J. : |
| Name of producer, publisher, distributor, manufacturer |
Wiley, |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2009. |
| 300 ## - PHYSICAL DESCRIPTION |
| Extent |
xxi, 476 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
25 cm. |
| 336 ## - CONTENT TYPE |
| Source |
rdacontent |
| Content Type Term |
text |
| Content Type Code |
txt |
| 337 ## - MEDIA TYPE |
| Source |
rdamedia |
| Media Type Term |
unmediated |
| Media Type Code |
unmediated |
| 338 ## - CARRIER TYPE |
| Source |
rdacarrier |
| Carrier Type Term |
volume |
| Carrier Type Code |
volume |
| 490 0# - SERIES STATEMENT |
| Series statement |
Wiley series on parallel and distributed computing. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 00 - FORMATTED CONTENTS NOTE |
| Title |
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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
| Topical term or geographic name as entry element |
Computer science |
| General subdivision |
Mathematics. |
|
| Topical term or geographic name as entry element |
Mathematical optimization. |
|
| Topical term or geographic name as entry element |
Problem solving. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
| Personal name |
Alba, Enrique. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Kitap |