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

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

by David B. Fogel
Pub. Date:
SPIE Press

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

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.

Product Details

ISBN-13: 9780819437259
Publisher: SPIE Press
Publication date: 07/28/2000
Series: Tutorial Texts in Optical Engineering Series
Pages: 184
Product dimensions: 7.34(w) x 9.66(h) x 0.45(d)

Table of Contents

Chapter 1An Overview of Evolutionary Algorithms and Their Advantages1
1.2Basics of Evolutionary Algorithms1
1.3Examples of Evolutionary Algorithm Applications3
1.4Advantages of Evolutionary Algorithms9
1.4.1Conceptual Simplicity9
1.4.2Broad Applicability11
1.4.3Outperform Classic Methods on Real Problems11
1.4.4Potential to Use Knowledge and Hybridize with Other Methods12
1.4.6Robust to Dynamic Changes13
1.4.7Capability for Self-Optimization14
1.4.8Able to Solve Problems with No Known Solutions15
1.5Principles and Practice of Evolutionary Algorithms16
Chapter 2Evolving Models of Time Series19
2.1Introduction to Time Series Prediction and Modeling19
2.2Linear Models with Payoffs Other Than Least Squares22
2.2.1Examples of Evolving Polynomials for Arbitrary Criteria23
2.3Generating ARMA Models in One Step27
2.3.1The Traditional Two-Step Approach27
2.3.2Information Criteria for Model Building29
2.3.3Evolutionary Modeling31
2.4Neural Models36
2.4.1Evolving a Neural Predictor for a Chaotic Time Series40
2.4.2Evolving a Predictor of a Financial Time Series42
2.5Multiple Interactive Programs48
Chapter 3Evolutionary Clustering and Classification57
3.1Concepts of Clustering and Classification57
3.2Evolutionary Clustering57
3.2.2Evaluating Solutions62
3.2.3Problems Investigated63
3.2.4Results and Discussion63
3.3Evolutionary Classification69
3.3.1Neural Networks for Classifying Sonar Data69 Propagation70 Annealing71 Algorithms72 and Materials72
3.3.2Neural Networks for Classifying Breast Cancer Data82 Features and Neural Network Architecture83 and Evaluation84
Chapter 4Evolving Control Systems95
4.1Introduction to Control95
4.2Evolutionary Control as Function Optimization95
4.2.1Three Textbook Problems95 Linear-Quadratic Problem96 Harvest Problem97 Push-Cart Problem98 Results98
4.2.2Traffic Ramp Control98 Control Rules105 I: High Mainline and Ramp Demand109 II: Moderate Mainline and Ramp Demand with an Incident112 III: High Mainline and Ramp Demand with an Incident116
4.3Evolutionary Control through Model Identification118
4.3.1Steps Toward Controlling Blood Pressure during Surgery118, Materials, and Results119
4.3.2Controlling a Pole Balanced on a Cart128 Dynamics128
Chapter 5Theory and Tools for Improving Evolutionary Algorithms139
5.1.1Binary Representations and Maximizing Implicit Parallelism139
5.1.2Crossover and Building Blocks140
5.1.3The Schema Theorem: The Fundamental Theorem of Genetic Algorithms143
5.1.4Proportional Selection and the K-Armed Bandit144
5.1.5A New Direction145
5.2Methods to Relate Parent and Offspring Fitness146
5.3Experiments with Fitness Distributions148

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