Evolutionary Computation (Hardcover)

Kenneth A. de De Jong, Kenneth A. De Jong

  • 出版商: A Bradford Book
  • 出版日期: 2002-03-19
  • 定價: $2,120
  • 售價: 6.0$1,272
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Hardcover
  • ISBN: 0262041944
  • ISBN-13: 9780262041942

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Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artifacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, Evolutionary Computation is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.

Kenneth A. De Jong is Professor of Computer Science at George Mason University and the founding editor of the journal Evolutionary Computation (MIT Press).

 

Table of Contents

1 Introduction 1
 
1.1 Basic Evolutionary Processes 1
 
1.2 EV: A Simple Evolutionary System 3
 
1.3 EV on a Simple Fitness Landscape 6
 
1.4 EV on a More Complex Fitness Landscape 15
 
1.5 Evolutionary Systems as Problem Solvers 19
 
1.6 Exercises 21
 
2 A Historical Perspective 23
 
2.1 Early Algorithmic Views 23
 
2.2 The Catalytic 1960s 24
 
2.3 The Explorative 1970s 25
 
2.4 The Exploitative 1980s 27
 
2.5 The Unifying 1990s 29
 
2.6 The Twenty-first Century: Mature Expansion 29
 
2.7 Summary 31
 
3 Canonical Evolutionary Algorithms 33
 
3.1 Introduction 33
 
3.2 EV(m,n) 33
 
3.3 Evolutionary Programming 34
 
3.4 Evolution Strategies 36
 
3.5 Genetic Algorithms 40
 
3.6 Summary 47
 
4 A Unified View of Simple EAs 49
 
4.1 A Common Framework 49
 
4.2 Population Size 50
 
4.3 Selection 54
 
4.4 Reproductive Mechanisms 61
 
4.5 Summary 69
 
5 Evolutionary Algorithms as Problem Solvers 71
 
5.1 Simple EAs as Parallel Adaptive Search 71
 
5.2 EA-based Optimization 80
 
5.3 EA-Based Search 105
 
5.4 EA-Based Machine Learning 107
 
5.5 EA-Based Automated Programming 109
 
5.6 EA-Based Adaptation 112
 
5.7 Summary 113
 
6 Evolutionary Computation Theory 115
 
6.1 Introduction 115
 
6.2 Analyzing EA Dynamics 117
 
6.3 Selection-Only Models 120
 
6.4 Reproduction-Only Models 141
 
6.5 Selection and Reproduction Interactions 160
 
6.6 Representation 185
 
6.7 Landscape Analysis 188
 
6.8 Models of Canonical EAs 189
 
6.9 Application-Oriented Theories 205
 
6.10 Summary 209
 
7 Advanced EC Topics 211
 
7.1 Self-adapting EAs 211
 
7.2 Dynamic Landscapes 213
 
7.3 Exploiting Parallelism 219
 
7.4 Evolving Executable Objects 221
 
7.5 Multi-objective EAs 223
 
7.6 Hybrid EAs 224
 
7.7 Biologically Inspired Extensions 225
 
7.8 Summary 230
 
8 The Road Ahead 231
 
8.1 Modeling General Evolutionary Systems 231
 
8.2 More Unification 232
 
8.3 Summary 232
 
 Appendix A: Source Code Overview 
 
A.1 EC1: A Very Simple EC System 233
 
A.2 EC2: A More Interesting EC System 236
 
A.3 EC3: A More Flexible EC System 237
 
A.4 EC4: An EC Research System 240
 
 Bibliography 241
 
 Index 253