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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000004809376 | QA76.9.S63 E54 2002 | Open Access Book | Book | Searching... |
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Summary
Summary
Computational intelligence is the study of adaptive mechanisms to enable or facilitate intelligent behaviour in complex and changing environments. As such, computational intelligence combines artificial neural networks, evolutionary computing, swarm intelligence and fuzzy systems.
This book presents a highly readable and systematic introduction to the fundamentals of computational intelligence. In-depth treatments of the more important and most frequently used techniques are also given. The book provides treatment of computational intelligence in a manner which allows the reader to easily implement the different techniques, and to apply these techniques to solve real-world, complex problems.
Key features include:
* A balanced treatment of the different computational intelligence paradigms
* Inclusion of swarm intelligence
* Coverage of the most recent developments in computational intelligence
* Complete algorithms presented in pseudo-code to allow implementation in any language
* Includes numerous exercises to involve and stimulate the reader further
The careful and considered approach to this key subject makes this book appropriate for both the first-time reader, as well as individuals already active in the field.
Table of Contents
Figures |
Tables |
Algorithms |
Preface |
Part I Introduction |
1 Introduction to Computational Intelligence |
1.1 Computational Intelligence Paradigms |
1.2 Short History |
1.3 Assignments |
Part II Artificial Neural Networks |
2 The Artificial Neuron |
2.1 Calculating the Net Input Signal |
2.2 Activation Functions |
2.3 Artificial Neuron Geometry |
2.4 Artificial Neuron Learning |
2.5 Assignments |
3 Supervised Learning Neural Networks |
3.1 Neural Network Types |
3.2 Supervised Learning Rules |
3.3 Functioning of Hidden Units |
3.4 Ensemble Neural Networks |
3.5 Assignments |
4 Unsupervised Learning Neural Networks |
4.1 Background |
4.2 Hebbian Learning Rule |
4.3 Principal Component Learning Rule |
4.4 Learning Vector Quantizer-I |
4.5 Self-Organizing Feature Maps |
4.6 Assignments |
5 Radial Basis Function Networks |
5.1 Learning Vector Quantizer-II |
5.2 Radial Basis Function Neural Networks |
5.3 Assignments |
6 Reinforcement Learning |
6.1 Learning through Awards |
6.2 Model-Free Reinforcement LearningModel |
6.3 Neural Networks and Reinforcement Learning |
6.4 Assignments |
7 Performance Issues (Supervised Learning) |
7.1 PerformanceMeasures |
7.2 Analysis of Performance |
7.3 Performance Factors |
7.4 Assignments |
Part III Evolutionary Computation |
8 Introduction to Evolutionary Computation |
8.1 Generic Evolutionary Algorithm |
8.2 Representation - The Chromosome |
8.3 Initial Population |
8.4 Fitness Function |
8.5 Selection |
8.6 Reproduction Operators |
8.7 Stopping Conditions |
8.8 Evolutionary Computation versus Classical Optimization |
8.9 Assignments |
9 Genetic Algorithms |
9.1 Canonical Genetic Algorithm |
9.2 Crossover |
9.3 Mutation |
9.4 Control Parameters |
9.5 Genetic Algorithm Variants |
9.6 Advanced Topics |
9.7 Applications |
9.8 Assignments |
10 Genetic Programming |
10.1 Tree-Based Representation |
10.2 Initial Population |
10.3 Fitness Function |
10.4 Crossover Operators |
10.5 Mutation Operators |
10.6 Building Block Genetic Programming |
10.7 Applications |
10.8 Assignments |
11 Evolutionary Programming |
11.1 Basic Evolutionary Programming |
11.2 Evolutionary Programming Operators |
11.3 Strategy Parameters |
11.4 Evolutionary Programming Implementations |
11.5 Advanced Topics |
11.6 Applications |
11.7 Assignments |
12 Evolution Strategies |
12.1 (1+1)-ES |
12.2 Generic Evolution Strategy Algorithm |
12.3 Strategy Parameters and Self-Adaptation |
12.4 Evolution Strategy Operators |
12.5 Evolution Strategy Variants |
12.6 Advanced Topics |
12.7 Applications of Evolution Strategies |
12.8 Assignments |
13 Differential Evolution |
13.1 Basic Differential Evolution |
13.2 DE/x/y/z |
13.3 Variations to Basic Differential Evolution |
13.4 Differential Evolution for Discrete-Valued Problems |
13.5 Advanced Topics |
13.6 Applications |
13.7 Assignments |
14 Cultural Algorithms |
14.1 Culture and Artificial Culture |
14.2 Basic Cultural Algorithm |
14.3 Belief Space |
14.4 Fuzzy Cultural Algorithm |
14.5 Advanced Topics |
14.6 Applications |
14.7 Assignments |
15 Coevolution |
15.1 Coevolution Types |
15.2 Competitive Coevolution |
15.3 Cooperative Coevolution |
15.4 Assignments |
Part IV Computational Swarm Intelligence |
16 Particle Swarm Optimization |
16.1 Basic Particle Swarm Optimization |
16.2 Social Network Structures |
16.3 Basic Variations |
16.4 Basic PSO Parameters |
16.5 Single-Solution Particle SwarmOptimization |
16.6 Advanced Topics |
16.7 Applications |
16.8 Assignments |
17 Ant Algorithms |
17.1 Ant Colony OptimizationMeta-Heuristic |
17.2 Cemetery Organization and Brood Care |
17.3 Division of Labor |
17.4 Advanced Topics |
17.5 Applications |
17.6 Assignments |
Part V Artificial Immune Systems |
18 Natural Immune System |
18.1 Classical View |
18.2 Antibodies and Antigens |
18.3 TheWhite Cells |
18.4 Immunity Types |
18.5 Learning the Antigen Structure |
18.6 The Network Theory |
18.7 The Danger Theory |
18.8 Assignments |
19 Artificial Immune Models |
19.1 Artificial Immune System Algorithm |
19.2 Classical ViewModels |
19.3 Clonal Selection TheoryModels |
19.4 Network TheoryModels |
19.5 Danger TheoryModels |
19.6 Applications and Other AIS models |
19.7 Assignments |
Part VI Fuzzy Systems |
20 Fuzzy Sets |
20.1 Formal Definitions |
20.2 Membership Functions |
20.3 Fuzzy Operators |
20.4 Fuzzy Set Characteristics |
20.5 Fuzziness and Probability |
20.6 Assignments |
21 Fuzzy Logic and Reasoning |
21.1 Fuzzy Logic |
21.2 Fuzzy Inferencing |
21.3 Assignments |
22 Fuzzy Controllers |
22.1 Components of Fuzzy Controllers |
22.2 Fuzzy Controller Types |
22.3 Assignments |
23 Rough Sets |
23.1 Concept of Discernibility |
23.2 Vagueness in Rough Sets |
23.3 Uncertainty in Rough Sets |
23.4 Assignments |
References |
A Optimization Theory |
A.1 Basic Ingredients of Optimization Problems |
A.2 Optimization ProblemClassifications |
A.3 Optima Types |
A.4 OptimizationMethod Classes |
A.5 Unconstrained Optimization |
A.6 Constrained Optimization |
A.7 Multi-Solution Problems |
A.8 Multi-Objective Optimization |
A.9 Dynamic Optimization Problems |
Index |