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Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation

Book Description

Evolutionary computation has emerged as a major topic in the scientific community as many of its techniques have successfully been applied to solve problems in a wide variety of fields. provides comprehensive research on emerging theories and its aspects on intelligent computation. Particularly focusing on breaking trends in evolutionary computing, algorithms, and programming, this publication serves to support professionals, government employees, policy and decision makers, as well as students in this scientific field. Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Associate Editors and List of Reviewers
    1. Associate Editors
    2. List of Reviewers
  5. Preface
  6. Chapter 1: Reliability Allocation Problem in Series-Parallel Systems
    1. ABSTRACT
    2. INTRODUCTION
    3. ANT COLONY OPTIMIZATION
    4. PROBLEM DESCRIPTION
    5. THE YCC-SP METHOD
    6. ANT COLONY FOR RELIABILITY OPTIMIZATION
    7. RESULTS
    8. CONCLUSION
  7. Chapter 2: Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. DETECTION ERROR TRADEOFF CURVE
    4. 3. PARTICLE SWARM OPTIMIZATION
    5. 5. CONCLUSION
  8. Chapter 3: Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem
    1. ABSTRACT
    2. INTRODUCTION
    3. ONE-DIMENSIONAL CUTTING STOCK PROBLEM
    4. CONCLUSIONS AND PERSPECTIVES
  9. Chapter 4: A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets
    1. ABSTRACT
    2. INTRODUCTION
    3. PREVIOUS WORK
    4. PROPOSED METHOD
    5. SIMULATION
    6. CONCLUSION
  10. Chapter 5: Simultaneous Tolerance Synthesis for Manufacturing and Quality using Evolutionary Algorithms
    1. ABSTRACT
    2. INTRODUCTION
    3. MATHEMATICAL FORMULATIONS: THE STS METHOD
    4. OVERVIEW OF EVOLUTIONARY ALGORITHMS
    5. CASE STUDY
    6. RESULTS AND DISCUSSION
    7. CONCLUSION
  11. Chapter 6: Parallel Single and Multiple Objectives Genetic Algorithms
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. GENETIC ALGORITHMS AND THEIR VARIANTS
    4. 3. PARALLEL GENETIC ALGORITHMS
    5. 4. SOME ISSUES WITH PGAS
    6. 5. MULTI-OBJECTIVE GENETIC ALGORITHMS
    7. 6. PARALLEL MULTI-OBJECTIVE GENETIC ALGORITHMS
    8. 7. APPLICATIONS
    9. 8. CONCLUSION
  12. Chapter 7: Experimental Study on Recent Advances in Differential Evolution Algorithm
    1. ABSTRACT
    2. INTRODUCTION
    3. RECENT ADVANCES AND IDEAS IN DE
    4. RELATED WORKS
    5. DESIGN OF EXPERIMENT
    6. RESULTS AND DISCUSSION
    7. CONCLUSION
  13. Chapter 8: The Volatility for Pre and Post Global Financial Crisis
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. DATA AND METHODOLOGY
    4. 3. TEMPORARY AND PERMANENT VOLATILITY MODEL
    5. 4. EMPIRICAL RESULTS
    6. 4.2 THE EFFECTS OF TEMPORARY AND PERMANENT
    7. 5. SUMMARY AND CONCLUSION
  14. Chapter 9: Differential Operators Embedded Artificial Bee Colony Algorithm
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. DIFFERENTIAL EVOLUTION
    4. 3. ARTIFICIAL BEE COLONY (ABC) OPTIMIZATION ALGORITHM
    5. 5. PARAMETER SETTINGS, BENCHMARK PROBLEMS AND COMPARISON CRITERIA
    6. 6. SIMULATION RESULTS
    7. 7. CONCLUSION
    8. APPENDIX
  15. Chapter 10: Appropriate Evolutionary Algorithm for Scheduling in FMS
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. MODEL FORMULATION
    4. 3. METHODOLOGY
    5. 4. PROBLEM STATEMENT
    6. 5. RESULTS AND CONCLUSIONS
  16. Chapter 11: GBF Trained Neuro-Fuzzy Equalizer for Time Varying Channels
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. SYSTEM MODEL
    4. 3. OBJECTIVE FUNCTIONS
    5. 4. PROPOSED HYBRID GBF
    6. 5. SIMULATIONS
    7. 6. CONCLUSION
  17. Chapter 12: An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. ARCHITECTURE OF PROPOSED MODELS
    4. 3. BACK PROPAGATION LEARNING ALGORITHM
    5. 4. EVOLUTIONARY LEARNING ALGORITHMS
    6. 5. ANALYSIS OF DATASETS AND INPUT SELECTION
    7. 6. EXPERIMENTAL RESULTS
    8. 7. ANALYSIS OF RESULTS
    9. 8. CONCLUSION
  18. Chapter 13: Firm Size Transmission Effect and Price-Volume Relationship Analysis During Financial Tsunami Periods
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. DATA SOURCE AND METHODOLOGY
    4. 4. SUMMARY AND CONCLUSION
  19. Chapter 14: Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set
    1. ABSTRACT
    2. INTRODUCTION
    3. TRADITIONAL GA-BASED CLASSIFIER
    4. RECURSIVE GA LEARNING WITH TASK DECOMPOSITION AND VARIED RULE NUMBER
    5. EXPERIMENTAL RESULTS AND ANALYSIS
    6. DISCUSSION
    7. CONCLUSION
    8. APPENDIX
  20. Chapter 15: LZW Encoding in Genetic Algorithm
    1. ABSTRACT
    2. INTRODUCTION
    3. LZWGA
    4. TEST PROBLEMS
    5. CROSSOVER OPERATORS
    6. MUTATION OPERATORS
    7. SHIFT OPERATOR
    8. DISCUSSION
    9. CONCLUSION
  21. Chapter 16: Usage Profile Generation from Web Usage Data Using Hybrid Biclustering Algorithm
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. METHODS AND MATERIALS
    4. 3. USER PROFILING USING BPSO WITH MUTATION BASED BICLUSTERING ALGORITHM
    5. 4. EXPERIMENTAL ANALYSIS AND DISCUSSION
    6. 5. CONCLUSION
  22. Chapter 17: An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. LINEAR AND NONLINEAR REGRESSION METHODS
    4. 3. THE BUILDING PROCESS OF THE SEMI-PARAMETRIC REGRESSION MODEL
    5. 5. CONCLUSION
  23. Compilation of References
  24. About the Contributors