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Handbook of Research on Natural Computing for Optimization Problems

Book Description

Nature-inspired computation is an interdisciplinary topic area that connects the natural sciences to computer science. Since natural computing is utilized in a variety of disciplines, it is imperative to research its capabilities in solving optimization issues. The Handbook of Research on Natural Computing for Optimization Problems discusses nascent optimization procedures in nature-inspired computation and the innovative tools and techniques being utilized in the field. Highlighting empirical research and best practices concerning various optimization issues, this publication is a comprehensive reference for researchers, academicians, students, scientists, and technology developers interested in a multidisciplinary perspective on natural computational systems.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
    2. List of Reviewers
  6. Foreword
  7. Preface
  8. Acknowledgment
  9. Section 1: Evolutionary Computing and Swarm Intelligence
    1. Chapter 1: Realizing the Need for Intelligent Optimization Tool
      1. ABSTRACT
      2. INTRODUCTION TO OPTIMIZATION
      3. TRADITIONAL TOOLS FOR OPTIMIZATION
      4. NATURAL COMPUTING
      5. NON-TRADITIONAL TOOLS FOR OPTIMIZATION
      6. WORKING PRINCIPLE OF A GA
      7. WORKING PRINCIPLE OF PARTICLE SWARM OPTIMIZATION (PSO)
      8. WORKING PRINCIPLE OF SIMULATED ANNEALING (SA)
      9. NEED FOR INTELLIGENT OPTIMIZATION TOOL
      10. WORK DONE
      11. SUMMARY AND SCOPE FOR FUTURE WORK
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
    2. Chapter 2: MRI Brain Image Segmentation Using Interactive Multiobjective Evolutionary Approach
      1. ABSTRACT
      2. INTRODUCTION
      3. MULTIOBJECTIVE FUZZY CLUSTERING
      4. PROPOSED IMOVGAC ALGORITHM
      5. EXPERIMENTAL RESULTS
      6. CONCLUSION
      7. REFERENCES
    3. Chapter 3: A Genetic Algorithm to Goal Programming Model for Crop Production with Interval Data Uncertainty
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CHANCE CONSTRAINED IVGP PROBLEM FORMULATION
      5. FORMULATION OF EGP MODEL
      6. GA SCHEME FOR EGP MODEL
      7. DEFINITIONS OF VARIABLES AND PARAMETERS
      8. PLANED-INTERVAL GOALS
      9. CASE EXAMPLE
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. ACKNOWLEDGMENT
      13. REFERENCES
      14. ADDITIONAL READING
      15. KEY TERMS AND DEFINITIONS
    4. Chapter 4: A System on Chip Development of Customizable GA Architecture for Real Parameter Optimization Problem
      1. ABSTRACT
      2. INTRODUCTION
      3. GENETIC ALGORITHM
      4. INITIAL POPULATION GENERATION
      5. FITNESS EVALUATION
      6. PARENT SELECTION
      7. GENETIC OPERATION
      8. BACKGROUND AND LITERATURE REVIEW OF RELATED WORKS
      9. GENETIC ALGORITHM BASED HARDWARE DEVELOPMENT AND ITS OPERATION
      10. BASIC HARDWARE STRUCTURE
      11. FIXED POINT HARDWARE IMPLEMENTATION ISSUES
      12. HARDWARE OPERATION
      13. FPGA BASED PROTOTYPE DEVELOPMENT OF GA BASED ARCHITECTURE
      14. BENCHMARK PROBLEMS
      15. EXPERIMENTAL STUDY
      16. SIMULATION AND FUNCTIONAL VERIFICATION
      17. SYNTHESIS AND FPGA IMPLEMENTATION
      18. PERFORMANCE ANALYSIS OF THE PROPOSED GA STRUCTURE: OPTIMIZATION OF STANDARD BENCHMARK FUNCTIONS
      19. COMPARISON WITH SOFTWARE IMPLEMENTATION AND PREVIOUS GENERAL PURPOSE HARDWARE GAs
      20. CONCLUSION
      21. ACKNOWLEDGMENT
      22. REFERENCES
      23. KEY TERMS AND DEFINITIONS
    5. Chapter 5: Genetic-Algorithm-Based Optimization of Fragile Watermarking in Discrete Hartley Transform Domain
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. 1-D DISCRETE HARTLEY TRANSFORMATION
      5. PROPOSED TECHNIQUE
      6. RESULTS AND DISCUSSIONS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    6. Chapter 6: Genetic-Algorithm-Based Optimization of Clustering in Mobile Ad Hoc Network
      1. ABSTRACT
      2. INTRODUCTION
      3. CLUSTERING IN MOBILE AD HOC NETWORK
      4. TRUST BASED CLUSTERING IN MOBILE AD HOC NETWORK
      5. OPTIMIZATION TECHNIQUES USING GENETIC ALGORITHM
      6. ISSUES ADDRESSED BY THE GENETIC ALGORITHM BASED APPROACHES
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    7. Chapter 7: Optimizing Solution for Storage Space Allocation Problem in Container Terminal Using Genetic Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. COMBINATORIAL OPTIMIZATION PROBLEMS
      5. STORAGE SPACE ALLOCATION PROBLEM (SSAP)
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    8. Chapter 8: Evolutionary Computing to Examine Variation in Proteins with Evolution
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ISSUES, CONTROVERSIES AND PROBLEMS
      5. RECOMMENDATIONS AND SOLUTIONS
      6. SUMMARIZING DIFFERENT ALGORITHMS IN BRIEF
      7. FUTURE PROSPECTS
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    9. Chapter 9: Evolutionary Algorithms for Economic Load Dispatch Having Multiple Types of Cost Functions
      1. ABSTRACT
      2. INTRODUCTION
      3. MATHEMATICAL PROBLEM FORMULATION
      4. COST FUNCTION
      5. QUADRATIC COST FUNCTION
      6. COST FUNCTION CONSIDERING VALVE POINT NONLINEARITY
      7. COST FUNCTION CONSIDERING MULTIPLE FUELS
      8. COST FUNCTION CONSIDERING BOTH VALVE POINT LOADING AND MULTIPLE FUELS
      9. COST FUNCTION CONSIDERING VALVE POINT LOADING AND RAMP RATE
      10. OBJECTIVE FUNCTION
      11. ALGORITHMS
      12. INPUT PARAMETERS
      13. SIMULATION AND RESULTS
      14. TEST SYSTEM 1
      15. TEST SYSTEM 2
      16. TEST SYSTEM 3
      17. TEST SYSTEM 4
      18. TEST SYSTEM 5
      19. ROBUSTNESS TEST
      20. CONCLUSION
      21. REFERENCES
      22. APPENDIX: LIST OF SYMBOLS
    10. Chapter 10: Source Location Privacy Using Ant Colony Optimization in Wireless Sensor Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ANT COLONY OPTIMIZATION ALGORITHM
      5. SYSTEM MODEL
      6. OUR PROPOSAL
      7. CONCLUSION
      8. REFERENCES
    11. Chapter 11: Evolutionary Computing Approaches for Clustering and Routing in Wireless Sensor Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. AN OVERVIEW OF EVOLUTIONARY ALGORITHMS
      4. ENERGY MODEL AND USED TERMINOLOGIES
      5. EVOLUTIONARY COMPUTING BASED CLUSTERING AND ROUTING
      6. SUMMARY AND RESEARCH DIRECTIONS
      7. ACKNOWLEDGMENT
      8. REFERENCES
    12. Chapter 12: Balanced Energy Consumption Approach Based on Ant Colony in Wireless Sensor Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. WIRELESS SENSOR NETWORK ARCHITECTURE
      4. ENERGY EFFICIENCY IS PRIMARY GOAL FOR CONSTRUCTING WSNS
      5. ENERGY EFFECTIVE ROUTING TECHNIQUES IN WSN
      6. BIOLOGICAL INSPIRATION FOR NETWORKING
      7. NEED FOR COMPUTATIONAL INTELLIGENCE TECHNIQUES IN WIRELESS SENSOR NETWORK
      8. OPTIMIZATION IN BIOLOGICAL SYSTEMS
      9. COMPARISON OF VARIOUS COMPUTATIONAL INTELLIGENCE TECHNIQUES
      10. APPLICATION OF BIOLOGICAL SYSTEMS IN WIRELESS SENSOR NETWORK
      11. ANT COLONY BASED ENERGY EFFECTIVE APPROACHES IN WSNS
      12. OVERVIEW OF ANT COLONY OPTIMIZATION
      13. HOW FUNDAMENTAL ANT COLONY ALGORITHM WORKS IN WSN
      14. ANT COLONY ALGORITHM PRINCIPLE
      15. NEED ACO TECHNIQUES IN WSNS TO BUILD ENERGY EFFECTIVE ROUTING PROTOCOLS
      16. MAIN ANT COLONY OPTIMIZATION ALGORITHM
      17. PROPERTY OF ACO FOR ROUTING
      18. APPLICATION OF ACO ALGORITHM
      19. NEED FOR ENERGY BALANCING IN WSNS
      20. COMPARISON OF ANT BASED BALANCED ENERGY EFFICIENT APPROACHES IN WSNS
      21. COMPARATIVE SIMULATION ANAYSIS OF ANT BASED BALANCED ENERGY EFFICIENT APPROACHES IN WSNS
      22. CONCLUSION
      23. FUTURE SCOPE
      24. ACKNOWLEDGMENT
      25. REFERENCES
      26. KEY TERMS AND DEFINITIONS
    13. Chapter 13: Variable Length PSO-Based Image Clustering for Image Denoising
      1. ABSTRACT
      2. INTRODUCTION
      3. NOISE MODELS
      4. PARTICLE SWARM OPTIMIZATION
      5. PROPOSED ALGORITHM FOR CLUSTERING, NOISE DETECTION AND FILTERING
      6. VARIABLE LENGTH PARTICLE SWARM OPTIMIZATION (VPSO) FOR CLUSTERING
      7. RESULTS AND DISCUSSION
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
    14. Chapter 14: An Intelligent Approach for Tracking and Monitoring Objects in a Departmental Store Using PSO
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. RFID SYSTEMS: AN OVERVIEW
      5. 4. COMPONENTS OF AN RFID SYSTEM
      6. 5. SYSTEM SPECIFICATION
      7. 6. GOALS OF THE SYSTEM
      8. 7. FUNCTIONAL DESCRIPTION OF THE SYSTEM
      9. 8. PSO: A BRIEF DESCRIPTION
      10. 9. APPLICATION OF PSO IN THE CURRENT SCENARIO
      11. 10. EXPERIMENTAL RESULTS
      12. 11. CONCLUSION
      13. ACKNOWLEDGMENT
      14. REFERENCES
    15. Chapter 15: Particle Swarm Optimization (PSO) for Optimization in Video Steganography
      1. ABSTRACT
      2. INTRODUCTION
      3. VIDEO STEGANOGRAPHY
      4. NATURAL COMPUTING
      5. PARTICLE SWARM OPTIMIZATION
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
    16. Chapter 16: A Secure Data-Hiding Approach Using Particle Swarm Optimization and Pixel Value Difference
      1. ABSTRACT
      2. INTRODUCTION
      3. 2. LITERATURE REVIEW
      4. 3. THE PROPOSED METHOD
      5. 4. EXPERIMENTED RESULTS
      6. 5. DISCUSSION AND ANALYSIS
      7. 6. CONCLUSION
      8. REFERENCES
    17. Chapter 17: Natural Computing in Mobile Network Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. NATURE INSPIRED COMPUTING BASED WIRLESS SENSOR NETWORK
      5. NATURE INSPIRED COMPUTING BASED MOBILE CLOUD COMPUTING
      6. NATURE INSPIRED COMPUTING BASED MOBILE AD HOC NETWORK
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    18. Chapter 18: A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System
      1. ABSTRACT
      2. INTRODUCTION
      3. MULTI-AGENT STOCHASTIC OPTIMIZATION (MASO) TECHNIQUES
      4. PROBLEM STATEMENT
      5. RESULTS AND DISCUSSIONS
      6. CONCLUSION
      7. ACKNOWLEDGMENT
      8. REFERENCES
    19. Chapter 19: Optimized Energy Aware VM Provisioning in Green Cloud Based on Cuckoo Search with Levy Flight
      1. ABSTRACT
      2. INTRODUCTION
      3. SURVEY
      4. CHALLENGES OF CLOUD COMPUTING
      5. MOTIVATION OF GREEN CLOUD COMUTING
      6. ARCHITECTURE OF GREEN CLOUD
      7. POWER MODEL FOR GREEN CLOUD
      8. PRELIMINARIES
      9. PROPOSED TECHNIQUE
      10. SIMULATION AND PERFORMANCE ANALYSIS
      11. FUTURE RESEARCH DIRECTION
      12. CONCLUSION
      13. ACKNOWLEDGMENT
      14. REFERENCES
    20. Chapter 20: Adaptive Simulated Annealing Algorithm to Solve Bio-Molecular Optimization
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ISSUES, CONTROVERSIES, AND PROBLEMS
      5. RECOMMENDATIONS AND SOLUTIONS
      6. APPLICATIONS
      7. FUTURE PROSPECT
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
  10. Section 2: Neural Computing
    1. Chapter 21: A Hybrid Model of FLANN and Firefly Algorithm for Classification
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE SURVEY
      4. PRELIMINARIES
      5. PROPOSED METHOD
      6. EXPERIMENTAL SETUP
      7. SIMULATION RESULTS AND COMPARISONS
      8. PROOF OF STATISTICAL SIGNIFICANCE
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
    2. Chapter 22: Applicability of ANN in Adsorptive Removal of Cd(II) from Aqueous Solution
      1. ABSTRACT
      2. INTRODUCTION
      3. MATERIALS AND METHODS
      4. RESULTS AND DISCUSSION
      5. ANN PROCEDURE AND PERFORMANCE
      6. CONCLUSION
      7. REFERENCES
      8. APPENDIX: NOMENCLATURE
    3. Chapter 23: Learning from Unbalanced Stream Data in Non-Stationary Environments Using Logistic Regression Model
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. SYSTEM DESCRIPTION
      5. ARCHITECTURAL VIEW
      6. RELEVANT MATHEMATICS
      7. EXPERIMENTS AND RESULTS
      8. MINE FRAMEWORK: SYSTEM ANALYSIS
      9. CONCLUSION
      10. FUTURE RESEARCH DIRECTIONS
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    4. Chapter 24: Soft-Computing-Based Optimization of Low Return Loss Multiband Microstrip Patch Antenna
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. BASIC CONSIDERATIONS
      5. ENHANCEMENT OF RADIATION PROPERTIES OF ANTENNA USING METAMATERIAL
      6. DESIGN OF METAMATERIAL BASED MULTIBAND ANTENNA
      7. DESIGN OPTIMIZATION USING ANN AND GA
      8. CONCLUSION
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
  11. Section 3: Cellular Automata and Cellular Computing
    1. Chapter 25: Cellular Automata-Basics
      1. ABSTRACT
      2. INTRODUCTION
      3. BASIC CELLULAR AUTOMATA STRUCTURE
      4. BASIC ALGORITHMS USED TO DRAW THE DECISION RULES FOR CA
      5. EXAMPLE TO DEMONSTRATE CA
      6. APPLICATION OF CELLULAR AUTOMATA
      7. LIMITATIONS OF RESEARCH WITH CA MODELS
      8. APPLICATION OF CELLULAR AUTOMATA MODELS TO UNDERSTAND THE ONSET OF MUSCULAR DYSTROPHIES: A NEW ERA TO BE DISCLOSED
      9. CONCLUSION
      10. ACKNOWLEDGMENT
      11. REFERENCES
      12. KEY TERMS AND DEFINITION
    2. Chapter 26: Overview of Cellular Computing-Basic Principles and Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. DIFFERENCE WITH MOLECULAR COMPUTING
      5. INHERENT CHARACTERISTICS OF CELLULAR SYSTEMS
      6. ALGORITHMS INFLUENCED BY BIOLOGICAL ENTITIES
      7. CC AND SYSTEMS BIOLOGY
      8. CC AND BIOINFORMATICS – IMPLEMENTATION AND OPPORTUNITES: A CASE REPORT
      9. FUTURE CHALLENGES OF CELLULAR COMPUTING
      10. CONCLUSION
      11. ACKNOWLEDGMENT
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
  12. Section 4: Quantum Computing
    1. Chapter 27: Sustainability of Public Key Cryptosystem in Quantum Computing Paradigm
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. QUANTUM COMPUTING PARADIGM
      5. SUSTAINABILITY OF PUBLIC CRYPTOSYSTEM IN QUANTUM COMPUTING PARADIGM
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    2. Chapter 28: Image Representation, Filtering, and Natural Computing in a Multivalued Quantum System
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. QUANTUM IMAGE PROCESSING
      5. QUANTUM NATURAL COMPUTING
      6. QUANTUM IMAGE FILTERATION
      7. CHRESTENSON GATES
      8. S-GATE
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
  13. Section 5: DNA Computing
    1. Chapter 29: Introduction to Molecular Computation
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MOLECULAR COMPUTATION: FACTSHEET
      5. MOLECULAR COMPUTATION: FACTSHEET CONTINUED
      6. SOLUTIONS AND RECOMMENDATIONS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    2. Chapter 30: DNA Computing Using Carbon Nanotube-DNA Hybrid Nanostructure
      1. ABSTRACT
      2. INTRODUCTION
      3. CARBON NANOTUBES: STRUCTURE, PHYSICAL AND CHEMICAL PROPERTIES
      4. STRUCTURE OF DNA
      5. PRIMARY STRUCTURE OF DNA
      6. SECONDARY STRUCTURE OF DNA
      7. TERTIARY STRUCTURE OF DNA
      8. QUATERNARY STRUCTURE OF DNA
      9. TYPES OF DNA
      10. CNT-DNA HYBRID FORMATION
      11. SIMULATION METHODS TO DESIGN DIFFERENT CNT-DNA MODELS
      12. SIMULATION OF CNT-DNA INTERACTION USING QUANTUMWISE SOFTWARE
      13. SIMULATION STEPS OF CNT-DNA / NUCLEOBASE INTERACTION USING QUANTUMWISE SOFTWARE
      14. SIGNIFICANCE OF PERFORMING SIMULATION
      15. CNT-DNA INTERACTION MODELS
      16. INSERTION OF DNA THROUGH CNT
      17. WRAPPING OF DNA OVER CNT
      18. FABRICATION OF DNA NANOTWEEZERS
      19. FABRICATION OF DNA NANOTUBE USING DNA-ORIGAMI
      20. APPLICATIONS OF CNT-DNA IN DNA-COMPUTING
      21. BIOSENSOR
      22. CNT-DNA LOGIC DESIGN
      23. CNT-DNA CRYPTOGRAPHY
      24. ADVANTAGES OF CNT-DNA COMPUTING
      25. CONCLUSION
      26. ACKNOWLEDGMENT
      27. REFERENCES
      28. KEY TERMS AND DEFINITIONS
    3. Chapter 31: DNA Cryptography
      1. ABSTRACT
      2. INTRODUCTION
      3. CRYPTOGRAPHY BASICS
      4. DNA CRYPTOGRAPHY
      5. ADVANTAGES OF DNA CRYPTOGRAPHY
      6. LIMITATION OF DNA CRYPTOGRAPHY
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 32: An Optimized In Silico Neuroinformatics Approach
      1. ABSTRACT
      2. INTRODUCTION
      3. MATERIALS AND METHODS
      4. RESULTS
      5. DISCUSSION
      6. CONCLUSION
      7. CONFLICTS OF INTEREST
      8. LIST OF ABBREVIATIONS
      9. ACKNOWLEDGMENT
      10. REFERENCES
  14. Section 6: Fuzzy Logic
    1. Chapter 33: Application of Fuzzy Logic and Fuzzy Optimization Techniques in Medical Image Processing
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. FUZZY LOGIC AND OPTIMIZATION METHOD
      5. FUZZY OPTIMIZATION MODEL (FOP)
      6. FUZZY OPTIMIZATION PROBLEM
      7. FUZZY OPTIMAL SOLUTION ACHIEVED BY GENETIC ALGORITHM
      8. FUZZY LOGIC IN IMAGE PROCESSING
      9. FUZZY PENALTY BASED OPTIMIZATION
      10. CURRENT APPLICATIONS OF FUZZY LOGIC
      11. FUTURE RESEARCH DIRECTIONS
      12. CONCLUSION
      13. ACKNOWLEDGMENT
      14. REFERENCES
    2. Chapter 34: Using Fuzzy Goal Programming with Penalty Functions for Solving EEPGD Problem via Genetic Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. EEPGD PROBLEM FORMULATION
      5. FGP FORMULATION
      6. MINSUM FGP MODEL
      7. PENALTY FUNCTION DESCRIPTION
      8. DESCRIPTION OF GA SCHEME
      9. AN ILLUSTRATIVE CASE EXAMPLE
      10. FUTURE RESEARCH DIRECTIONS
      11. CONCLUSION
      12. ACKNOWLEDGMENT
      13. REFERENCES
      14. ADDITIONAL READING
      15. KEY TERMS AND DEFINITIONS
    3. Chapter 35: Genetic Algorithm for FGP Model of a Multiobjective Bilevel Programming Problem in Uncertain Environment
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PROBLEM FORMULATION
      5. GA SCHEME
      6. FGP PROBLEM FORMULATION
      7. FGP MODEL
      8. NUMERICAL ILLUSTRATION
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. ACKNOWLEDGMENT
      12. REFERENCES
      13. ADDITIONAL READING
      14. KEY TERMS AND DEFINITIONS
  15. Section 7: Others
    1. Chapter 36: Vedic Sutras
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. VEDIC MULTIPLICATION
      6. REFERENCES
    2. Chapter 37: A Web-Based Collaborative Learning System Using Concept Maps
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORKS
      4. COLLABORATION ARCHITECTURE
      5. CONCEPT MAP AND LEARNING SYSTEM IMPLEMENTATION
      6. EVALUATION
      7. COMPARATIVE STUDY WITH OTHER COLLABORATIVE SYSTEMS
      8. CONCLUSION
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
    3. Chapter 38: Active Contour Model for Medical Applications
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. ACTIVE CONTOUR MODEL
      5. ALGORITHM ANALYSIS AND FLOWCHART
      6. MATHEMATICAL MODEL OF ACTIVE CONTOUR EVOLUTION USING GREEDY APPROACH ON AN IMAGE MATRIX
      7. TRADITIONAL CHALLENGES
      8. INVESTIGATION RESULTS IN TOWARDS IMPROVEMENTS
      9. FUTURE DIRECTIONS
      10. CONCLUSION
      11. ACKNOWLEDGMENT
      12. REFERENCES
      13. KEY TERMS AND DEFINITIONS
    4. Chapter 39: Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND: BASIC TERMINOLOGY AND CLASSIFICATION
      4. MAIN FOCUS OF THE CHAPTER: INTERPRETATION OF BLOODSTAIN PATTERNS: A SCIENTIFIC APPROACH
      5. ISSUES: CRIME SCENE RECONSTRUCTION-HOW EFFECTIVE ARE BLOODSTAIN PATTERNS?
      6. SOLUTION
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    5. Chapter 40: From Cochlear Implants and Neurotology to Brain Computer Interfaces
      1. ABSTRACT
      2. INTRODUCTION
      3. BRAIN: COMPUTER INTERFACES AND MUSIC PERCEPTION
      4. THE SUBSTRATE FOR MUSICAL UNDERSTANDING AND REASONING
      5. TRIGGERING, INTERFACING AND COMMUNICATING
      6. INTERFACING WITH THE COCHLEAR IMPLANT
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. KEY TERMS AND DEFINITIONS
  16. Compilation of References
  17. About the Contributors