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Cover image for Computational intelligence :  an introduction
Title:
Computational intelligence : an introduction
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Publication Information:
Haboken, NJ : John Wiley & Sons, 2002
ISBN:
9780470848708

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30000004809376 QA76.9.S63 E54 2002 Open Access Book Book
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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
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