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|a UAMI
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|a Kristensen, Terje.
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|a Computational Intelligence, Evolutionary Computing, Evolutionary Clustering Algorithms.
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| 260 |
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|a Sharjah :
|b Bentham Science Publishers,
|c 2016.
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| 300 |
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|a 1 online resource (135 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
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|a Print version record.
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|a PREFACE ; ACKNOWLEDGEMENTS; CONFILICT OF INTEREST; Introduction ; 1.1. OVERVIEW; 1.2. GOAL; 1.3. OUTLINE; Chapter 1 (Introduction); Chapter 2 (Background); Chapter 3 (Evolutionary Algorithms); Chapter 4 (System Specification); Chapter 5 (Design and Implementation); Chapter 6 (Data Visualization); Chapter 7 (User Interface); Chapter 8 (Case Study); Chapter 9 (Discussion); Chapter 10 (Summary and Future); Background ; 2.1. CLUSTERING; 2.1.1. Introduction; 2.1.2. General Definition; 2.1.3. Object Similarity; Proximity Measure for Continuous Values; Proximity Measure for Discrete Values.
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| 505 |
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|a Proximity Measure for Mixed Values2.1.4. Clustering Methods; Hierarchical Clustering; Partitional Clustering; Fuzzy Clustering; 2.1.5. Cluster Membership; 2.1.6. Cluster Validation; Evolutionary Algorithms ; 3.1. INTRODUCTION; 3.1.1. Data Representation Chromosome; 3.1.2. Initial Population; 3.1.3. Fitness Function; 3.1.4. Selection; 3.1.5. Reproduction; 3.1.6. Stopping conditions; 3.2. MATHEMATICAL OPTIMIZATION; 3.2.1. Maxima and Mimima; 3.2.2. Optimization Problems; 3.3. GENETIC ALGORITHMS; 3.3.1. Crossover; 3.3.2. Mutation; 3.3.3. Control Parameters; 3.4. GENETIC PROGRAMMING.
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|a 3.4.1. Tree Based Representation3.4.2. Fitness Function; 3.4.3. Crossover Operators; 3.4.4. Mutation Operators; 3.5. EVOLUTIONARY PROGRAMMING; 3.5.1. Representation; 3.5.2. Mutation Operators; 3.5.3. Selection Operators; 3.6. EVOLUTION STRATEGIES; 3.6.1. Generic Evolution Strategies Algorithm; 3.6.2. Strategy Parameter; 3.6.3. Selection Operator; 3.6.4. Crossover Operators; 3.6.5. Mutation Operator; 3.7. DIFFERENTIAL EVOLUTION; 3.7.1. Mutation Operator; 3.7.2. Crossover Operator; 3.7.3. Selection; 3.7.4. Control Parameters; 3.8. CULTURAL ALGORITHMS; 3.8.1. Belief Space.
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| 505 |
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|a 3.8.2. Acceptance Function3.8.3. Influence Function; System Specification ; 4.1. INTRODUCTION; 4.2. SYSTEM OBJECTIVE; 4.3. FUNCTIONAL REQUIREMENTS; 4.3.1. System Input; 4.3.2. Cluster Analysis; 4.3.3. Visualization; 4.4. NON-FUNCTIONAL REQUIREMENTS; 4.4.1. Functional Correctness; 4.4.2. Extensibility; 4.4.3. Maintainability; 4.4.4. Portability; 4.4.1. Usability; Design and Implementation ; 5.1. INTRODUCTION; 5.2. SYSTEM ARCHITECTURE; 5.2.1. Dependency Injection; 5.2.2. Open-Closed Principle; 5.3. TOOLS AND TECHNOLOGIES; 5.3.1. Java; 5.3.2. JavaFX; 5.3.3. Netbeans; 5.3.4. Maven.
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| 505 |
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|a 5.3.5. Git and GitHub5.3.6. JUnit; 5.4. DATA STRUCTURE AND CLUSTERING; 5.4.1. Import Data and Data Structure; 5.4.2. K-means Algorithm; Complexity of K-Means Operations; 5.5. EVOLUTIONARY ALGORITHMS; 5.5.1. Genetic Clustering Algorithm; Population Initialization; Fitness Evaluation; Evolve Population; Termination Criteria; Time-Complexity; 5.5.2. Differential Evolution Based Clustering Algorithm; Population Initialization; Mutation; Crossover; Termination Criteria; Time-complexity; 5.5.3. Selection Operators; Random Selection; Proportional Selection; 5.5.4. Mutation Operators.
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| 500 |
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|a Floating-Point Mutation.
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| 504 |
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|a Includes bibliographical references and index.
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| 590 |
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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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| 650 |
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|a Computational intelligence.
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| 650 |
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|a Intelligence informatique.
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|i Print version:
|a Kristensen, Terje.
|t Computational Intelligence, Evolutionary Computing, Evolutionary Clustering Algorithms.
|d Sharjah : Bentham Science Publishers, ©2016
|z 9781681082301
|
| 856 |
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