Evolutionary Computation: Principles and Practice for Signal Processing / Edition 1

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Evolutionary computation is one of the fastest growing areas of computer science, partly because of its broad applicability to engineering problems. The methods can be applied to problems as diverse as supply-chain optimization, routing and planning, task assignment, pharmaceutical design, interactive gaming, and many others within the signal processing domain. The book is an outgrowth of successful SPIE short courses taught by the author. The examples span a range of applications and should be useful to a variety of readers with different backgrounds and expertise.
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Editorial Reviews

Provides explanations and examples of the application of evolutionary algorithms to problems in the area of signal processing, including system identification, time series prediction, clustering, and classification. The problem of evolving control systems in light of limited information about the target plant is also treated. The last chapter discusses the limitations of previous approaches to describe and analyze evolutionary algorithms in terms of mathematical theory. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Table of Contents

Preface xi
Chapter 1 An Overview of Evolutionary Algorithms and Their Advantages 1
1.1 Introduction 1
1.2 Basics of Evolutionary Algorithms 1
1.3 Examples of Evolutionary Algorithm Applications 3
1.4 Advantages of Evolutionary Algorithms 9
1.4.1 Conceptual Simplicity 9
1.4.2 Broad Applicability 11
1.4.3 Outperform Classic Methods on Real Problems 11
1.4.4 Potential to Use Knowledge and Hybridize with Other Methods 12
1.4.5 Parallelism 13
1.4.6 Robust to Dynamic Changes 13
1.4.7 Capability for Self-Optimization 14
1.4.8 Able to Solve Problems with No Known Solutions 15
1.5 Principles and Practice of Evolutionary Algorithms 16
References 16
Chapter 2 Evolving Models of Time Series 19
2.1 Introduction to Time Series Prediction and Modeling 19
2.2 Linear Models with Payoffs Other Than Least Squares 22
2.2.1 Examples of Evolving Polynomials for Arbitrary Criteria 23
2.3 Generating ARMA Models in One Step 27
2.3.1 The Traditional Two-Step Approach 27
2.3.2 Information Criteria for Model Building 29
2.3.3 Evolutionary Modeling 31
2.4 Neural Models 36
2.4.1 Evolving a Neural Predictor for a Chaotic Time Series 40
2.4.2 Evolving a Predictor of a Financial Time Series 42
2.5 Multiple Interactive Programs 48
2.6 Discussion 50
References 54
Chapter 3 Evolutionary Clustering and Classification 57
3.1 Concepts of Clustering and Classification 57
3.2 Evolutionary Clustering 57
3.2.1 Method 59
3.2.2 Evaluating Solutions 62
3.2.3 Problems Investigated 63
3.2.4 Results and Discussion 63
3.3 Evolutionary Classification 69
3.3.1 Neural Networks for Classifying Sonar Data 69 Back Propagation 70 Simulated Annealing 71 Evolutionary Algorithms 72 Method and Materials 72 Experiments 74 Results 78
3.3.2 Neural Networks for Classifying Breast Cancer Data 82 Methods 82 Input Features and Neural Network Architecture 83 Training and Evaluation 84 Results 85 Discussion 88
3.4 Discussion 90
References 90
Chapter 4 Evolving Control Systems 95
4.1 Introduction to Control 95
4.2 Evolutionary Control as Function Optimization 95
4.2.1 Three Textbook Problems 95 The Linear-Quadratic Problem 96 The Harvest Problem 97 The Push-Cart Problem 98 Experimental Results 98
4.2.2 Traffic Ramp Control 98 Background 98 Ramp-Metering Control Rules 105 Methods 108 Results 109 Scenario I: High Mainline and Ramp Demand 109 Scenario II: Moderate Mainline and Ramp Demand with an Incident 112 Scenario III: High Mainline and Ramp Demand with an Incident 116 Discussion 118
4.3 Evolutionary Control through Model Identification 118
4.3.1 Steps Toward Controlling Blood Pressure during Surgery 118 Introduction 118 Method, Materials, and Results 119 Discussion 124
4.3.2 Controlling a Pole Balanced on a Cart 128 System Dynamics 128 Method 129 Results 131 Discussion 134
4.4 Summary 134
References 136
Chapter 5 Theory and Tools for Improving Evolutionary Algorithms 139
5.1 Theory 139
5.1.1 Binary Representations and Maximizing Implicit Parallelism 139
5.1.2 Crossover and Building Blocks 140
5.1.3 The Schema Theorem: The Fundamental Theorem of Genetic Algorithms 143
5.1.4 Proportional Selection and the K-Armed Bandit 144
5.1.5 A New Direction 145
5.2 Methods to Relate Parent and Offspring Fitness 146
5.3 Experiments with Fitness Distributions 148
5.3.1 Methods 148
5.3.2 Results 152
5.4 Discussion 157
References 159
Index 163
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