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| Figures | |
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| Tables | |
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| Algorithms | |
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| Preface | |
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| Introduction | |
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| Introduction to Computational Intelligence | |
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| Computational Intelligence Paradigms | |
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| Short History | |
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| Assignments | |
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| Artificial Neural Networks | |
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| The Artificial Neuron | |
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| Calculating the Net Input Signal | |
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| Activation Functions | |
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| Artificial Neuron Geometry | |
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| Artificial Neuron Learning | |
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| Assignments | |
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| Supervised Learning Neural Networks | |
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| Neural Network Types | |
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| Supervised Learning Rules | |
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| Functioning of Hidden Units | |
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| Ensemble Neural Networks | |
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| Assignments | |
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| Unsupervised Learning Neural Networks | |
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| Background | |
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| Hebbian Learning Rule | |
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| Principal Component Learning Rule | |
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| Learning Vector Quantizer-I | |
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| Self-Organizing Feature Maps | |
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| Assignments | |
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| Radial Basis Function Networks | |
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| Learning Vector Quantizer-II | |
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| Radial Basis Function Neural Networks | |
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| Assignments | |
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| Reinforcement Learning | |
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| Learning through Awards | |
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| Model-Free Reinforcement LearningModel | |
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| Neural Networks and Reinforcement Learning | |
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| Assignments | |
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| Performance Issues (Supervised Learning) | |
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| PerformanceMeasures | |
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| Analysis of Performance | |
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| Performance Factors | |
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| Assignments | |
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| Evolutionary Computation | |
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| Introduction to Evolutionary Computation | |
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| Generic Evolutionary Algorithm | |
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| Representation - The Chromosome | |
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| Initial Population | |
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| Fitness Function | |
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| Selection | |
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| Reproduction Operators | |
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| Stopping Conditions | |
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| Evolutionary Computation versus Classical Optimization | |
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| Assignments | |
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| Genetic Algorithms | |
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| Canonical Genetic Algorithm | |
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| Crossover | |
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| Mutation | |
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| Control Parameters | |
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| Genetic Algorithm Variants | |
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| Advanced Topics | |
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| Applications | |
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| Assignments | |
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| Genetic Programming | |
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| Tree-Based Representation | |
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| Initial Population | |
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| Fitness Function | |
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| Crossover Operators | |
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| Mutation Operators | |
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| Building Block Genetic Programming | |
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| Applications | |
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| Assignments | |
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| Evolutionary Programming | |
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| Basic Evolutionary Programming | |
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| Evolutionary Programming Operators | |
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| Strategy Parameters | |
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| Evolutionary Programming Implementations | |
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| Advanced Topics | |
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| Applications | |
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| Assignments | |
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| Evolution Strategies | |
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| (1+1)-ES | |
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| Generic Evolution Strategy Algorithm | |
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| Strategy Parameters and Self-Adaptation | |
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| Evolution Strategy Operators | |
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| Evolution Strategy Variants | |
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| Advanced Topics | |
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| Applications of Evolution Strategies | |
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| Assignments | |
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| Differential Evolution | |
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| Basic Differential Evolution | |
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| DE/x/y/z | |
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| Variations to Basic Differential Evolution | |
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| Differential Evolution for Discrete-Valued Problems | |
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| Advanced Topics | |
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| Applications | |
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| Assignments | |
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| Cultural Algorithms | |
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| Culture and Artificial Culture | |
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| Basic Cultural Algorithm | |
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| Belief Space | |
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| Fuzzy Cultural Algorithm | |
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| Advanced Topics | |
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| Applications | |
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| Assignments | |
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| Coevolution | |
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| Coevolution Types | |
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| Competitive Coevolution | |
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| Cooperative Coevolution | |
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| Assignments | |
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| Computational Swarm Intelligence | |
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| Particle Swarm Optimization | |
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| Basic Particle Swarm Optimization | |
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| Social Network Structures | |
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| Basic Variations | |
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| Basic PSO Parameters | |
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| Single-Solution Particle SwarmOptimization | |
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| Advanced Topics | |
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| Applications | |
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| Assignments | |
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| Ant Algorithms | |
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| Ant Colony OptimizationMeta-Heuristic | |
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| Cemetery Organization and Brood Care | |
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| Division of Labor | |
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| Advanced Topics | |
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| Applications | |
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| Assignments | |
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| Artificial Immune Systems | |
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| Natural Immune System | |
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| Classical View | |
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| Antibodies and Antigens | |
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| TheWhite Cells | |
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| Immunity Types | |
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| Learning the Antigen Structure | |
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| The Network Theory | |
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| The Danger Theory | |
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| Assignments | |
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| Artificial Immune Models | |
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| Artificial Immune System Algorithm | |
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| Classical ViewModels | |
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| Clonal Selection TheoryModels | |
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| Network TheoryModels | |
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| Danger TheoryModels | |
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| Applications and Other AIS models | |
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| Assignments | |
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| Fuzzy Systems | |
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| Fuzzy Sets | |
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| Formal Definitions | |
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| Membership Functions | |
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| Fuzzy Operators | |
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| Fuzzy Set Characteristics | |
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| Fuzziness and Probability | |
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| Assignments | |
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| Fuzzy Logic and Reasoning | |
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| Fuzzy Logic | |
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| Fuzzy Inferencing | |
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| Assignments | |
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| Fuzzy Controllers | |
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| Components of Fuzzy Controllers | |
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| Fuzzy Controller Types | |
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| Assignments | |
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| Rough Sets | |
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| Concept of Discernibility | |
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| Vagueness in Rough Sets | |
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| Uncertainty in Rough Sets | |
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| Assignments | |
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| References | |
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| Optimization Theory | |
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| Basic Ingredients of Optimization Problems | |
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| Optimization ProblemClassifications | |
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| Optima Types | |
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| OptimizationMethod Classes | |
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| Unconstrained Optimization | |
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| Constrained Optimization | |
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| Multi-Solution Problems | |
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| Multi-Objective Optimization | |
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| Dynamic Optimization Problems | |
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| Index | |