Optimization Techniques for Solving Complex Problems

Optimization Techniques for Solving Complex Problems

Released March 2009
Publisher(s): Wiley
ISBN: 9780470293324

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Book description

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.

Table of contents

  1. Cover Page
  2. WILEY SERIES ON PARALLEL AND DISTRIBUTED COMPUTING
  3. Title Page
  4. Copyright
  5. Dedication
  6. CONTENTS
  7. CONTRIBUTORS
  8. FOREWORD
  9. PREFACE
  10. PART I: METHODOLOGIES FOR COMPLEX PROBLEM SOLVING
    1. CHAPTER 1: Generating Automatic Projections by Means of Genetic Programming
      1. 1.1 INTRODUCTION
      2. 1.2 BACKGROUND
      3. 1.3 DOMAINS
      4. 1.4 ALGORITHMIC PROPOSAL
      5. 1.5 EXPERIMENTAL ANALYSIS
      6. 1.6 CONCLUSIONS
      7. REFERENCES
    2. CHAPTER 2: Neural Lazy Local Learning
      1. 2.1 INTRODUCTION
      2. 2.2 LAZY RADIAL BASIS NEURAL NETWORKS
      3. 2.3 EXPERIMENTAL ANALYSIS
      4. 2.4 CONCLUSIONS
      5. REFERENCES
    3. CHAPTER 3: Optimization Using Genetic Algorithms with Micropopulations
      1. 3.1 INTRODUCTION
      2. 3.2 ALGORITHMIC PROPOSAL
      3. 3.3 EXPERIMENTAL ANALYSIS: THE RASTRIGIN FUNCTION
      4. 3.4 CONCLUSIONS
      5. REFERENCES
    4. CHAPTER 4: Analyzing Parallel Cellular Genetic Algorithms
      1. 4.1 INTRODUCTION
      2. 4.2 CELLULAR GENETIC ALGORITHMS
      3. 4.3 PARALLEL MODELS FOR cGAs
      4. 4.4 BRIEF SURVEY OF PARALLEL cGAs
      5. 4.5 EXPERIMENTAL ANALYSIS
      6. 4.6 CONCLUSIONS
      7. REFERENCES
    5. CHAPTER 5: Evaluating New Advanced Multiobjective Metaheuristics
      1. 5.1 INTRODUCTION
      2. 5.2 BACKGROUND
      3. 5.3 DESCRIPTION OF THE METAHEURISTICS
      4. 5.4 EXPERIMENTAL METHODOLOGY
      5. 5.5 EXPERIMENTAL ANALYSIS
      6. 5.6 CONCLUSIONS
      7. REFERENCES
    6. CHAPTER 6: Canonical Metaheuristics for Dynamic Optimization Problems
      1. 6.1 INTRODUCTION
      2. 6.2 DYNAMIC OPTIMIZATION PROBLEMS
      3. 6.3 CANONICAL MHs FOR DOPs
      4. 6.4 BENCHMARKS
      5. 6.5 METRICS
      6. 6.6 CONCLUSIONS
      7. REFERENCES
    7. CHAPTER 7: Solving Constrained Optimization Problems with Hybrid Evolutionary Algorithms
      1. 7.1 INTRODUCTION
      2. 7.2 STRATEGIES FOR SOLVING CCOPs WITH HEAs
      3. 7.3 STUDY CASES
      4. 7.4 CONCLUSIONS
      5. REFERENCES
    8. CHAPTER 8: Optimization of Time Series Using Parallel, Adaptive, and Neural Techniques
      1. 8.1 INTRODUCTION
      2. 8.2 TIME SERIES IDENTIFICATION
      3. 8.3 OPTIMIZATION PROBLEM
      4. 8.4 ALGORITHMIC PROPOSAL
      5. 8.5 EXPERIMENTAL ANALYSIS
      6. 8.6 CONCLUSIONS
      7. REFERENCES
    9. CHAPTER 9: Using Reconfigurable Computing for the Optimization of Cryptographic Algorithms
      1. 9.1 INTRODUCTION
      2. 9.2 DESCRIPTION OF THE CRYPTOGRAPHIC ALGORITHMS
      3. 9.3 IMPLEMENTATION PROPOSAL
      4. 9.4 EXPERMENTAL ANALYSIS
      5. 9.5 CONCLUSIONS
      6. REFERENCES
    10. CHAPTER 10: Genetic Algorithms, Parallelism, and Reconfigurable Hardware
      1. 10.1 INTRODUCTION
      2. 10.2 STATE OF THE ART
      3. 10.3 FPGA PROBLEM DESCRIPTION AND SOLUTION
      4. 10.4 ALGORITHMIC PROPOSAL
      5. 10.5 EXPERIMENTAL ANALYSIS
      6. 10.6 CONCLUSIONS
      7. REFERENCES
    11. CHAPTER 11: Divide and Conquer: Advanced Techniques
      1. 11.1 INTRODUCTION
      2. 11.2 ALGORITHM OF THE SKELETON
      3. 11.3 EXPERIMENTAL ANALYSIS
      4. 11.4 CONCLUSIONS
      5. REFERENCES
    12. CHAPTER 12: Tools for Tree Searches: Branch-and-Bound and A* Algorithms
      1. 12.1 INTRODUCTION
      2. 12.2 BACKGROUND
      3. 12.3 ALGORITHMIC SKELETON FOR TREE SEARCHES
      4. 12.4 EXPERIMENTATION METHODOLOGY
      5. 12.5 EXPERIMENTAL RESULTS
      6. 12.6 CONCLUSIONS
      7. REFERENCES
    13. CHAPTER 13: Tools for Tree Searches: Dynamic Programming
      1. 13.1 INTRODUCTION
      2. 13.2 TOP-DOWN APPROACH
      3. 13.3 BOTTOM-UP APPROACH
      4. 13.4 AUTOMATA THEORY AND DYNAMIC PROGRAMMING
      5. 13.5 PARALLEL ALGORITHMS
      6. 13.6 DYNAMIC PROGRAMMING HEURISTICS
      7. 13.7 CONCLUSIONS
      8. REFERENCES
  11. PART II: APPLICATIONS
    1. CHAPTER 14: Automatic Search of Behavior Strategies in Auctions
      1. 14.1 INTRODUCTION
      2. 14.2 EVOLUTIONARY TECHNIQUES IN AUCTIONS
      3. 14.3 THEORETICAL FRAMEWORK: THE AUSUBEL AUCTION
      4. 14.4 ALGORITHMIC PROPOSAL
      5. 14.5 EXPERIMENTAL ANALYSIS
      6. 14.6 CONCLUSIONS
      7. REFERENCES
    2. CHAPTER 15: Evolving Rules for Local Time Series Prediction
      1. 15.1 INTRODUCTION
      2. 15.2 EVOLUTIONARY ALGORITHMS FOR GENERATING PREDICTION RULES
      3. 15.3 EXPERIMENTAL METHODOLOGY
      4. 15.4 EXPERIMENTS
      5. 15.5 CONCLUSIONS
      6. REFERENCES
    3. CHAPTER 16: Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction
      1. 16.1 INTRODUCTION
      2. 16.2 METAHEURISTICS AND BIOINFORMATICS
      3. 16.3 DNA FRAGMENT ASSEMBLY PROBLEM
      4. 16.4 SHORTEST COMMON SUPERSEQUENCE PROBLEM
      5. 16.5 CONCLUSIONS
      6. REFERENCES
    4. CHAPTER 17: Optimal Location of Antennas in Telecommunication Networks
      1. 17.1 INTRODUCTION
      2. 17.2 STATE OF THE ART
      3. 17.3 RADIO NETWORK DESIGN PROBLEM
      4. 17.4 OPTIMIZATION ALGORITHMS
      5. 17.5 BASIC PROBLEMS
      6. 17.6 ADVANCED PROBLEM
      7. 17.7 CONCLUSIONS
      8. REFERENCES
    5. CHAPTER 18: Optimization of Image-Processing Algorithms Using FPGAs
      1. 18.1 INTRODUCTION
      2. 18.2 BACKGROUND
      3. 18.3 MAIN FEATURES OF FPGA-BASED IMAGE PROCESSING
      4. 18.4 ADVANCED DETAILS
      5. 18.5 EXPERIMENTAL ANALYSIS: SOFTWARE VERSUS FPGA
      6. 18.6 CONCLUSIONS
      7. REFERENCES
    6. CHAPTER 19: Application of Cellular Automata Algorithms to the Parallel Simulation of Laser Dynamics
      1. 19.1 INTRODUCTION
      2. 19.2 BACKGROUND
      3. 19.3 LASER DYNAMICS PROBLEM
      4. 19.4 ALGORITHMIC PROPOSAL
      5. 19.5 EXPERIMENTAL ANALYSIS
      6. 19.6 PARALLEL IMPLEMENTATION OF THE ALGORITHM
      7. 19.7 CONCLUSIONS
      8. REFERENCES
    7. CHAPTER 20: Dense Stereo Disparity from an Artificial Life Standpoint
      1. 20.1 INTRODUCTION
      2. 20.2 INFECTION ALGORITHM WITH AN EVOLUTIONARY APPROACH
      3. 20.3 EXPERIMENTAL ANALYSIS
      4. 20.4 CONCLUSIONS
      5. REFERENCES
    8. CHAPTER 21: Exact, Metaheuristic, and Hybrid Approaches to Multidimensional Knapsack Problems
      1. 21.1 INTRODUCTION
      2. 21.2 MULTIDIMENSIONAL KNAPSACK PROBLEM
      3. 21.3 HYBRID MODELS
      4. 21.4 EXPERIMENTAL ANALYSIS
      5. 21.5 CONCLUSIONS
      6. REFERENCES
    9. CHAPTER 22: Greedy Seeding and Problem-Specific Operators for GAs Solution of Strip Packing Problems
      1. 22.1 INTRODUCTION
      2. 22.2 BACKGROUND
      3. 22.3 HYBRID GA FOR THE 2SPP
      4. 22.4 GENETIC OPERATORS FOR SOLVING THE 2SPP
      5. 22.5 INITIAL SEEDING
      6. 22.6 IMPLEMENTATION OF THE ALGORITHMS
      7. 22.7 EXPERIMENTAL ANALYSIS
      8. 22.8 CONCLUSIONS
      9. REFERENCES
    10. CHAPTER 23: Solving the KCT Problem: Large-Scale Neighborhood Search and Solution Merging
      1. 23.1 INTRODUCTION
      2. 23.2 HYBRID ALGORITHMS FOR THE KCT PROBLEM
      3. 23.3 EXPERIMENTAL ANALYSIS
      4. 23.4 CONCLUSIONS
      5. REFERENCES
    11. CHAPTER 24: Experimental Study of GA-Based Schedulers in Dynamic Distributed Computing Environments
      1. 24.1 INTRODUCTION
      2. 24.2 RELATED WORK
      3. 24.3 INDEPENDENT JOB SCHEDULING PROBLEM
      4. 24.4 GENETIC ALGORITHMS FOR SCHEDULING IN GRID SYSTEMS
      5. 24.5 GRID SIMULATOR
      6. 24.6 INTERFACE FOR USING A GA-BASED SCHEDULER WITH THE GRID SIMULATOR
      7. 24.7 EXPERIMENTAL ANALYSIS
      8. 24.8 CONCLUSIONS
      9. REFERENCES
    12. CHAPTER 25: Remote Optimization Service
      1. 25.1 INTRODUCTION
      2. 25.2 BACKGROUND AND STATE OF THE ART
      3. 25.3 ROS ARCHITECTURE
      4. 25.4 INFORMATION EXCHANGE IN ROS
      5. 25.5 XML IN ROS
      6. 25.6 WRAPPERS
      7. 25.7 EVALUATION OF ROS
      8. 25.8 CONCLUSIONS
      9. REFERENCES
    13. CHAPTER 26: Remote Services for Advanced Problem Optimization
      1. 26.1 INTRODUCTION
      2. 26.2 SIRVA
      3. 26.3 MOSET AND TIDESI
      4. 26.4 ABACUS
      5. REFERENCES
  12. INDEX
  13. WILEY SERIES ON PARALLEL AND DISTRIBUTED COMPUTING Series Editor: Albert Y. Zomaya

Product information

  • Title: Optimization Techniques for Solving Complex Problems
  • Author(s):
  • Release date: March 2009
  • Publisher(s): Wiley
  • ISBN: 9780470293324