Optimization techniques for solving complex problems /

Optimization techniques for solving complex problems /

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Bibliographic Details
Imprint:Hoboken, N.J. : John Wiley & Sons, c2009.
Description:xxi, 476 p. : ill. ; 25 cm.
Language:English
Series:Wiley series on parallel and distributed computing
Subject:
Computer science -- Mathematics.
Mathematical optimization.
Problem solving.
Computer science -- Mathematics.
Mathematical optimization.
Problem solving.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/7691005
Hidden Bibliographic Details
Other authors / contributors:Alba, Enrique.
ISBN:9780470293324 (cloth)
0470293322 (cloth)
Notes:Includes bibliographical references and index.
Table of Contents:
  • Contributors
  • Foreword
  • Preface
  • Part I. Methodologies for Complex Problem Solving
  • 1. Generating Automatic Projections by Means of Genetic Programming
  • 1.1. Introduction
  • 1.2. Background
  • 1.3. Domains
  • 1.4. Algorithmic Proposal
  • 1.5. Experimental Analysis
  • 1.6. Conclusions
  • References
  • 2. Neural Lazy Local Learning
  • 2.1. Introduction
  • 2.2. Lazy Radial Basis Neural Networks
  • 2.3. Experimental Analysis
  • 2.4. Conclusions
  • References
  • 3. Optimization Using Genetic Algorithms with Micropopulations
  • 3.1. Introduction
  • 3.2. Algorithmic Proposal
  • 3.3. Experimental Analysis: The Rastrigin Function
  • 3.4. Conclusions
  • References
  • 4. Analyzing Parallel Cellular Genetic Algorithms
  • 4.1. Introduction
  • 4.2. Cellular Genetic Algorithms
  • 4.3. Parallel Models for cGAs
  • 4.4. Brief Survey of Parallel cGAs
  • 4.5. Experimental Analysis
  • 4.6. Conclusions
  • References
  • 5. Evaluating New Advanced Multiobjective Metaheuristics
  • 5.1. Introduction
  • 5.2. Background
  • 5.3. Description of the Metaheuristics
  • 5.4. Experimental Methodology
  • 5.5. Experimental Analysis
  • 5.6. Conclusions
  • References
  • 6. Canonical Metaheuristics for Dynamic Optimization Problems
  • 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 Hybrid Evolutionary Algorithms
  • 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
  • 8.1. Introduction
  • 8.2. Time Series Identification
  • 8.3. Optimization Problem
  • 8.4. Algorithmic Proposal
  • 8.5. Experimental Analysis
  • 8.6. Conclusions
  • References
  • 9. Using Reconfigurable Computing for the Optimization of Cryptographic Algorithms
  • 9.1. Introduction
  • 9.2. Description of the Cryptographic Algorithms
  • 9.3. Implementation Proposal
  • 9.4. Expermental Analysis
  • 9.5. Conclusions
  • References
  • 10. Genetic Algorithms, Parallelism, and Reconfigurable Hardware
  • 10.1. Introduction
  • 10.2. State of the Art
  • 10.3. FPGA Problem Description and Solution
  • 10.4. Algorithmic Proposal
  • 10.5. Experimental Analysis
  • 10.6. Conclusions
  • References
  • 11. Divide and Conquer: Advanced Techniques
  • 11.1. Introduction
  • 11.2. Algorithm of the Skeleton
  • 11.3. Experimental Analysis
  • 11.4. Conclusions
  • References
  • 12. Tools for Tree Searches: Branch-and-Bound and A* Algorithms
  • 12.1. Introduction
  • 12.2. Background
  • 12.3. Algorithmic Skeleton for Tree Searches
  • 12.4. Experimentation Methodology
  • 12.5. Experimental Results
  • 12.6. Conclusions
  • References
  • 13. Tools for Tree Searches: Dynamic Programming
  • 13.1. Introduction
  • 13.2. Top-Down Approach
  • 13.3. Bottom-Up 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
  • 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
  • References
  • 15. Evolving Rules for Local Time Series Prediction
  • 15.1. Introduction
  • 15.2. Evolutionary Algorithms for Generating Prediction Rules
  • 15.3. Experimental Methodology
  • 15.4. Experiments
  • 15.5. Conclusions
  • References
  • 16. Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction
  • 16.1. Introduction
  • 16.2. Metaheuristics and Bioinformatics
  • 16.3. DNA Fragment Assembly Problem
  • 16.4. Shortest Common Supersequence Problem
  • 16.5. Conclusions
  • References
  • 17. Optimal Location of Antennas in Telecommunication Networks
  • 17.1. Introduction
  • 17.2. State of the Art
  • 17.3. Radio Network Design Problem
  • 17.4. Optimization Algorithms
  • 17.5. Basic Problems
  • 17.6. Advanced Problem
  • 17.7. Conclusions
  • References
  • 18. Optimization of Image-Processing Algorithms Using FPGAs
  • 18.1. Introduction
  • 18.2. Background
  • 18.3. Main Features of FPGA-Based Image Processing
  • 18.4. Advanced Details
  • 18.5. Experimental Analysis: Software Versus FPGA
  • 18.6. Conclusions
  • References
  • 19. Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics
  • 19.1. Introduction
  • 19.2. Background
  • 19.3. Laser Dynamics Problem
  • 19.4. Algorithmic Proposal
  • 19.5. Experimental Analysis
  • 19.6. Parallel Implementation of the Algorithm
  • 19.7. Conclusions
  • References
  • 20. Dense Stereo Disparity from an Artificial Life Standpoint
  • 20.1. Introduction
  • 20.2. Infection Algorithm with an Evolutionary Approach
  • 20.3. Experimental Analysis
  • 20.4. Conclusions
  • References
  • 21. Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems
  • 21.1. Introduction
  • 21.2. Multidimensional Knapsack Problem
  • 21.3. Hybrid Models
  • 21.4. Experimental Analysis
  • 21.5. Conclusions
  • References
  • 22. Greedy Seeding and Problem-Specific Operators for GAs Solution of Strip Packing Problems
  • 22.1. Introduction
  • 22.2. Background
  • 22.3. Hybrid GA for the 2SPP
  • 22.4. Genetic Operators for Solving the 2SPP
  • 22.5. Initial Seeding
  • 22.6. Implementation of the Algorithms
  • 22.7. Experimental Analysis
  • 22.8. Conclusions
  • References
  • 23. Solving the KCT Problem: Large-Scale Neighborhood Search and Solution Merging
  • 23.1. Introduction
  • 23.2. Hybrid Algorithms for the KCT Problem
  • 23.3. Experimental Analysis
  • 23.4. Conclusions
  • References
  • 24. Experimental Study of GA-Based Schedulers in Dynamic Distributed Computing Environments
  • 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. Interface for Using a GA-Based Scheduler with the Grid Simulator
  • 24.7. Experimental Analysis
  • 24.8. Conclusions
  • References
  • 25. Remote Optimization Service
  • 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
  • References
  • 26. Remote Services for Advanced Problem Optimization
  • 26.1. Introduction
  • 26.2. SIRVA
  • 26.3. MOSET and TIDESI
  • 26.4. ABACUS
  • References
  • Index
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