Summary : Optimization Techniques for Solving Complex Problems (Wiley Series on Parallel and Distributed Computing) |
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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. 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.
Search : 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 C
Excerpts from Inner Flap (Front) |
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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. 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.