000			08394nam a2200301 i 4500
001			28746
008			101021s2009 njudf b 001 0 eng d
020			_a9780470293324
020			_a0470293322
035			_a(OCoLC)
040			_aBAUN _beng _cBAUN _erda
049			_aBAUN_MERKEZ
050	0	4	_aQA76.9.M35 _bO68 2009
245	0	0	_aOptimization techniques for solving complex problems / _ceditör, Enrique Alba [and others]
264		1	_aHoboken, N.J. : _bWiley, _cc2009.
300			_axxi, 476 pages : _billustrations ; _c25 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
490	0		_aWiley series on parallel and distributed computing.
504			_aIncludes bibliographical references and index.
505	0	0	_tPART I: METHODOLOGIES FOR COMPLEX PROBLEM SOLVING. _t1. Generating Automatic Projections by Means of GP (C. Estébanez,and R. Aler). _t1.1 Introduction. _t1.2 Background. _t1.3 Domains. _t1.4 Algorithmic Proposal. _t1.5 Experimental Analysis. _t1.6 Conclusions and Future Work. _tReferences. _t2. Neural Lazy Local Learning (J. M. Valls, I. M. Galván, and P. Isasi). _t2.1 Introduction. _t2.2 LRBNN: Lazy Radial Basis Neural Networks. _t2.3 Experimental Framework. _t2.4 Conclusions. _tReferences. _t3. Optimization by Using GAs with Micropopulations (Y. Sáez). _t3.1 Introduction. _t3.2 Algorithmic Proposal. _t3.3 Experimental Analysis: the Rastrigin Function. _t3.4 Conclusions. _tReferences. _t4. Analyzing Parallel Cellular Genetic Algorithms (G. Luque, E. Alba, and B. Dorronsoro). _t4.1 Introduction. _t4.2 Cellular Genetic Algorithms. _t4.3 Parallel Models for cGAs. _t4.4 Brief Survey on Parallel cGAs. _t4.5 Experimental Results. _t4.6 Conclusions. _tReferences. _t5. Evaluating New Advanced Multiobjective Metaheuristics (A. J. Nebro, J.J. Durillo, F. Luna, and E. Alba). _t5.1 Introduction. _t5.2 Background. _t5.3 Description of the Metaheuristics. _t5.4 Experimentation Methodology. _t5.5 Computational Results. _t5.6 Conclusions and Future Work. _tReferences. _t6. Canonical Metaheuristics for DOPs (G. Leguizamón, G. Ordóñez, S. Molina, and E. Alba). _t6.1 Introduction. _t6.2 Dynamic Optimization Problems. _t6.3 Canonical MHs for DOPs. _t6.4 Benchmarks. _t6.5 Metrics. _t6.6 Conclusions. _tReferences. _t7. Solving Constrained Optimization Problems with HEAs (C. Cotta, and A. J. Fernández). _t7.1 Introduction. _t7.2 Strategies for Solving CCOPs with HEAs. _t7.3 Study Cases. _t7.4 Conclusions. _tReferences. _t8. Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques (J. A. Gomez, M. D. Jaraiz, M. A. Vega, and J. M. Sanchez). _t8.1 Introduction. _t8.2 Time Series Identification. _t8.3 Optimization Problem. _t8.4 Algorithmic Proposal. _t8.5 Experimental Analysis. _t8.6 Conclusions and Future Work. _tReferences. _t9. Using Reconfigurable Computing to Optimization of Cryptographic Algorithms (J. M. Granado, M. A. Vega, J. M. Sanchez, and J. A. Gomez). _t9.1 Introduction. _t9.2 Description of the Cryptographic Algorithms. _t9.3 Implementation Proposal. _t9.4 Results. _t9.5 Conclusions. _tReferences. _t10. Genetic Algorithms, Parallelism and Reconfigurable Hardware (J. M. Sanchez, M. Rubio, M. A. Vega, and J. A. Gomez). _t10.1 Introduction. _t10.2 State of the Art. _t10.3 FPGA Problem Description and Solution. _t10.4 Algorithmic Proposal. _t10.5 Experiments and Results. _t10.6 Conclusions and Future Work. _tReferences. _t11. Divide and Conquer, Advanced Techniques (C. Lóon, G. Miranda, and C. Rodriguez). _t11.1 Introduction. _t11.2 The Algorithm of the Skeleton. _t11.3 Computational Results. _t11.4 Conclusions. _tReferences. _t12. Tools for Tree Searches: Branch and Bound and A* Algorithms (C. León, G. Miranda, and C. Rodriguez). _t12.1 Introduction. _t12.2 Background. _t12.3 Algorithmic Skeleton for Tree Searches. _t12.4 Experimentation Methodology. _t12.5 Computational Results. _t12.6 Conclusions and Future Work. _tReferences. _t13. Tools for Tree Searches: Dynamic Programming (C. León, G. Miranda, and C. Rodriguez). _t13.1 Introduction. _t13.2 The TopDown. _tApproach. _t13.3 The BottomUp Approach. _t13.4 Automata Theory and Dynamic Programming. _t13.5 Parallel Algorithms. _t13.6 Dynamic Programming Heuristics. _t13.7 Conclusions. _tReferences. _tPART II: APPLICATIONS. _t14. Automatic Search of Behavior Strategies in Auctions (D. Quintana, and A. Mochón). _t14.1 Introduction. _t14.2 Evolutionary Techniques in Auctions. _t14.3 Theoretical Framework: the Ausubel Auction. _t14.4 Algorithmic Proposal. _t14.5 Experimental analysis. _t14.6 Conclusions and Future Work. _tReferences. _t15. Evolving Rules For Local Time Series Prediction (C. Luque, J. M. Valls, and P. Isasi). _t15.1 Introduction. _t15.2 Evolutionary Algorithms for Generating Prediction Rules. _t15.3 Description of the Method. _t15.4 Experiments. _t15.5 Conclusions. _tReferences. _t16. Metaheuristics in Bioinformatics (C. Cotta, A. J. Fernández, J. E. Gallardo, G. Luque, and E. Alba). _t16.1 Introduction. _t16.2 Metaheuristics and Bioinformatics. _t16.3 The DNA Fragment Assembly Problem. _t16.4 The Shortest Common Supersequence Problem. _t16.5 Conclusions. _tReferences. _t17. Optimal Location of Antennae in Telecommunication Networks (G. Molina, F. Chicano, and E. Alba). _t17.1 Introduction. _t17.2 State of the Art. _t17.3 Radio Network Design Problem. _t17.4 Optimization Algorithms. _t17.5 Basic Problem Instances. _t17.6 Advanced Problem Instance. _t17.7 Conclusions. _tReferences. _t18. Optimization of Image Processing Algorithms Using FPGAs (M. A. Vega, A. Gomez, J. A. Gomez, and J. M. Sanchez). _t18.1 Introduction. _t18.2 Background. _t18.3 Main Features of the FPGAbased Image Processing. _t18.4 Advanced Details. _t18.5 Experimental Analysis: Software vs. FPGA. _t18.6 Conclusions. _tReferences. _t19. 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). _t19.1 Introduction. _t19.2 Background. _t19.3 The Problem: Laser Dynamics. _t19.4 Algorithmic Proposal. _t19.5 Experimental Analysis. _t19.6 Parallel Implementation of the Algorithm. _t19.7 Conclusions and Future Work. _tReferences. _t20. Dense Stereo Disparity from an ALife Standpoint (G. Olague, F. Fernandez, C. B. Perez, and E. Lutton). _t20.1 Introduction. _t20.2 Infection Algorithm with an Evolutionary Approach. _t20.3 Experimental Results. _t20.4 Conclusion. _tReferences. _t21. Approaches to Multidimensional Knapsack Problems (J. E. Gallardo, C. Cotta, and A. J. Fernández). _t21.1 Introduction. _t21.2 The Multidimensional Knapsack Problem. _t21.3 Hybrid Models. _t21.4 Experimental Results. _t21.5 Conclusions and Future Work. _tReferences. _t22. Greedy Seeding and ProblemSpecific Operators for GAs Solving Strip Packing Problems (C. Salto, J. M. Molina, and E. Alba). _t22.1 Introduction. _t22.2 Background. _t22.3 A Hybrid GA for the 2SPP. _t22.4 Genetic Operators for Solving the 2SPP. _t22.5 Initial Seeding. _t22.6 Implementation. _t22.7 Computational Analysis. _t22.8 Conclusions. _tReferences. _t23. Solving the KCT Problem: Large Scale Neighborhood Search and Solution Merging (C. Blum, and M. Blesa). _t23.1 Introduction. _t23.2 Hybrid Algorithms for the KCT Problem. _t23.3 Experimental Evaluation. _t23.4 Summary and Conclusions. _tReferences. _t24. Experimental Study of Gabased Schedulers in Dynamic Distributed Computing Environments (F. Xhafa, and J. Carretero). _t24.1 Introduction. _t24.2 Related Work. _t24.3 Independent Job Scheduling Problem. _t24.4 Genetic Algorithms for Scheduling in Grid Systems. _t24.5 Grid Simulator. _t24.6 The Interface for Using Gabased Scheduler with the Grid Simulator. _t24.7 Experimental Analysis. _t24.8 Conclusions. _tReferences. _t25. ROS: Remote Optimization Service (J. GarcíaNieto, F. Chicano, and E. Alba). _t25.1 Introduction. _t25.2 Background and State of the Art. _t25.3 ROS Architecture. _t25.4 Information Exchange in ROS. _t25.5 XML in ROS. _t25.6 Wrappers. _t25.7 Evaluation of ROS. _t25.8 Conclusions and Future Work. _tReferences. _t26. SIRVA, MOSET, TIDESI, ABACUS: Remote Services for Advanced. _tProblem Optimization (J. A. Gomez, M. A. Vega, J. M. Sanchez, J. L. Guisado, D. Lombrana, and F. Fernandez). _t26.1 Introduction. _t26.2 SIRVA. _t26.3 MOSET and TIDESI. _t26.4 ABACUS. _tReferences. _tIndex.
650		0	_aComputer science _xMathematics.
650		0	_aMathematical optimization.
650		0	_aProblem solving.
700	1		_aAlba, Enrique.
942			_2lcc _cKT
999			_c25079 _d25079