You are previewing Principal Concepts in Applied Evolutionary Computation.
O'Reilly logo
Principal Concepts in Applied Evolutionary Computation

Book Description

Increasingly powerful and diverse computing technologies have the potential to tackle ever greater and more complex problems and dilemmas in engineering and science disciplines. Principal Concepts in Applied Evolutionary Computation: Emerging Trends provides an introduction to the important interdisciplinary discipline of evolutionary computation, an artificial intelligence field that combines the principles of computational intelligence with the mechanisms of the theory of evolution. Academics and practicing field professionals will find this reference useful as they break into the emerging and complex world of evolutionary computation, learning to harness and utilize this exciting new interdisciplinary field.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Associate Editors
    2. List of Reviewers
  5. Preface
  6. Chapter 1: Ant Clustering Algorithms
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. METHODOLOGIES
    4. 3. PERFORMANCE EVALUATION AMONG THREE ACAS
    5. 4. COMPARISON OF ACA-2 WITH OTHER ALGORITHMS
    6. 5. CONCLUSION
  7. Chapter 2: Three Novel Methods to Predict Traffic Time Series in Reconstructed State Spaces
    1. ABSTRACT
    2. INTRODUCTION
    3. STATE SPACE RECONSTRUCTION
    4. PROPOSED METHODS
    5. CRITERIA TO EVALUATE PREDICTION PERFORMANCE
    6. PREDICTION RESULTS
    7. DISCUSSION
    8. CONCLUSION
  8. Chapter 3: Self-Evolvable Protocol Design Using Genetic Algorithms
    1. ABSTRACT
    2. INTRODUCTION
    3. SELF-MODIFYING PROTOCOLS AND OUR EVOLUTIONARY APPROACH
    4. SELF EVOLVABLE TRANSACTION PROTOCOL
    5. SIMULATION
    6. SIMULATION RESULTS AND DISCUSSION
    7. CONCLUSION
  9. Chapter 4: Facial Feature Tracking via Evolutionary Multiobjective Optimization
    1. ABSTRACT
    2. INTRODUCTION
    3. PARAMETERIZED FACIAL ANALYSIS
    4. AN APPLICATION SPECIFIC NSGA-II
    5. NUMERICAL RESULTS
    6. CONCLUSION
    7. APPENDIX
  10. Chapter 5: Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms
    1. ABSTRACT
    2. INTRODUCTION
    3. INCREMENTAL MULTI-OBJECTIVE GENETIC ALGORITHM
    4. OBJECTIVE ORDERING FOR IMOGA
    5. EXPERIMENTAL RESULTS AND ANALYSIS
    6. DISCUSSION
    7. CONCLUSION
  11. Chapter 6: Comparative Study of Evolutionary Computing Methods for Parameter Estimation of Power Quality Signals
    1. ABSTRACT
    2. INTRODUCTION
    3. AN OVERVIEW OF EVOLUTIONARY ALGORITHMS
    4. MATHEMATICAL FORMULATION
    5. SIMULATION RESULTS
    6. STATISTICAL ANALYSIS
    7. CONCLUSION
  12. Chapter 7: A Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. PROBLEM FORMULATION
    4. 3. THE MODEL OF MULTI-OBJECTIVE FUNCTION
    5. 4. SOLVING THE OPTIMIZATION PROBLEM USING GENETIC ALGORITHM
    6. 5. PROPOSED METHOD FLOWCHART
    7. 6. NUMERICAL RESULTS
    8. 7. CONCLUSION
  13. Chapter 8: A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II
    1. ABSTRACT
    2. INTRODUCTION
    3. MULTI OBJECTIVE OPTIMIZATION USING GA
    4. THE PROPOSED MODEL
    5. SIMULATION STUDY
    6. CONCLUDING REMARKS
  14. Chapter 9: Extrapolated Biogeography-Based Optimization (eBBO) for Global Numerical Optimization and Microstrip Patch Antenna Design
    1. ABSTRACT
    2. INTRODUCTION
    3. BASIC CONCEPT
    4. EXTRAPOLATED BBO (EBBO)
    5. COMPARISON OF EXPERIENTIAL RESULTS OF EBBO AND BBO
    6. CALCULATION OF OPTIMIZED PARAMETERS OF RECTANGULAR MICROSTRIP PATCH ANTENNA
    7. CONCLUSION
  15. Chapter 10: Posterior Sampling using Particle Swarm Optimizers and Model Reduction Techniques
    1. ABSTRACT
    2. PARTICLE SWARM OPTIMIZATION (PSO) APPLIED TO INVERSE PROBLEMS
    3. WHY EXPLORATION IS NEEDED TO EVALUATE UNCERTAINTY
    4. HOW TO ACHIEVE EXPLORATION USING PSO
    5. APPLICATION TO ENVIRONMENTAL GEOPHYSICS
    6. HOW TO EXPLORE HIGH DIMENSIONAL SPACES? APPLICATION TO RESERVOIR ENGINEERING
    7. CONCLUSION
  16. Chapter 11: Reserve Constrained Multi-Area Economic Dispatch Employing Evolutionary Approach
    1. ABSTRACT
    2. INTRODUCTION
    3. RESERVE CONSTRAINED MULTI-AREA ECONOMIC DISPATCH
    4. EVOLUTIONARY OPTIMIZATION STRATEGIES
    5. PSO WITH TIME-VARYING ACCELERATION COEFFICIENTS (PSO_TVAC)
    6. SOLUTION OF RESERVE CONSTRAINED MAED PROBLEM
    7. RESULTS AND DISCUSSIONS
    8. CONCLUSION
    9. APPENDIX
  17. Chapter 12: Application of Machine Learning Techniques to Predict Software Reliability
    1. ABSTRACT
    2. INTRODUCTION
    3. LITERATURE SURVEY
    4. OVERVIEW OF THE TECHNIQUES APPLIED
    5. EXPERIMENTAL DESIGN
    6. RESULTS AND DISCUSSIONS
    7. CONCLUSION
  18. Chapter 13: Buffer Management in Cellular IP Networks using Evolutionary Algorithms
    1. ABSTRACT
    2. INTRODUCTION
    3. PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM
    4. RELATED WORK
    5. BUFFER IN CELLULAR IP NETWORKS
    6. THE PROPOSED MODEL
    7. EXPERIMENTS
    8. OBSERVATIONS AND CONCLUSION
  19. Chapter 14: Using Evolution Strategies to Perform Stellar Population Synthesis for Galaxy Spectra from SDSS
    1. ABSTRACT
    2. INTRODUCTION
    3. GALAXY SPECTRA
    4. FITTING
    5. RESULTS
    6. CONCLUSION AND FUTURE WORK
  20. Chapter 15: A Novel Puzzle Based Compaction (PBC) Strategy for Enhancing the Utilization of Reconfigurable Resources
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. RELATED WORK
    5. SYSTEM MODEL AND BASIC DEFINITIONS
    6. THE PROPOSED PUZZLE BASED COMPACTION (PBC) TECHNIQUE
    7. EXCREMENTAL RESULTS
    8. FUTURE WORK
    9. CONCLUSION
  21. Chapter 16: A Comparative Study of Metaheuristic Methods for Transmission Network Expansion Planning
    1. ABSTRACT
    2. INTRODUCTION
    3. PROBLEM FORMULATION
    4. OVERVIEW OF GA, PSO AND BF
    5. RESULTS AND DISCUSSIONS
    6. CONCLUSION
    7. APPENDIX A
  22. Compilation of References
  23. About the Contributors