Coverart for item
The Resource Evolutionary algorithms, Alain Petrowski, Sana Ben-Hamida, (electronic book)

Evolutionary algorithms, Alain Petrowski, Sana Ben-Hamida, (electronic book)

Label
Evolutionary algorithms
Title
Evolutionary algorithms
Statement of responsibility
Alain Petrowski, Sana Ben-Hamida
Creator
Contributor
Author
Subject
Language
eng
Summary
Evolutionary algorithms are bio-inspired algorithms based on Darwin's theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms.
Member of
Cataloging source
  • StDuBDS
  • StDuBDS
http://library.link/vocab/creatorName
Petrowski, Alain
Dewey number
005.1
Index
no index present
LC call number
QA76.9.A43
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
Ben-Hamida, Sana
http://library.link/vocab/subjectName
  • Computer algorithms
  • Evolutionary programming (Computer science)
  • Machine learning
Summary expansion
Evolutionary algorithms are bio-inspired algorithms based on Darwin's theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning
Target audience
specialized
Label
Evolutionary algorithms, Alain Petrowski, Sana Ben-Hamida, (electronic book)
Instantiates
Publication
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
<p>Preface xi</p> <p><b>Chapter 1 Evolutionary Algorithms 1</b></p> <p>1.1 From natural evolution to engineering 1</p> <p>1.2 A generic evolutionary algorithm 3</p> <p>1.3 Selection operators 5</p> <p>1.4 Variation operators and representation 21</p> <p>1.5 Binary representation 25</p> <p>1.6 The simple genetic algorithm 30</p> <p>1.7 Conclusion 31</p> <p><b>Chapter 2 Continuous Optimization 33</b></p> <p>2.1 Introduction 33</p> <p>2.2 Real representation and variation operators for evolutionary algorithms 35</p> <p>2.3 Covariance Matrix Adaptation Evolution Strategy 46</p> <p>2.4 A restart CMA Evolution Strategy 55</p> <p>2.5 Differential Evolution (DE) 57</p> <p>2.6 Success-History based Adaptive Differential Evolution (SHADE) 65</p> <p>2.7 Particle Swarm Optimization 70</p> <p>2.8 Experiments and performance comparisons 77</p> <p>2.9 Conclusion 88</p> <p>2.10 Appendix: set of basic objective functions used for the experiments 89</p> <p><b>Chapter 3 Constrained Continuous Evolutionary Optimization 93</b></p> <p>3.1 Introduction 93</p> <p>3.2 Penalization 98</p> <p>3.3 Superiority of feasible solutions 112</p> <p>3.4 Evolving on the feasible region 117</p> <p>3.5 Multi-objective methods 123</p> <p>3.6 Parallel population approaches 130</p> <p>3.7 Hybrid methods 132</p> <p>3.8 Conclusion 132</p> <p><b>Chapter 4 Combinatorial Optimization 135</b></p> <p>4.1 Introduction 135</p> <p>4.2 The binary representation and variation operators 140</p> <p>4.3 Order-based Representation and variation operators 143</p> <p>4.4 Conclusion 163</p> <p><b>Chapter 5 Multi-objective Optimization 165</b></p> <p>5.1 Introduction 165</p> <p>5.2 Problem formalization 166</p> <p>5.3 The quality indicators 167</p> <p>5.4 Multi-objective evolutionary algorithms 169</p> <p>5.5 Methods using a &ldquo;Pareto ranking&rdquo; 169</p> <p>5.6 Many-objective problems 176</p> <p>5.7 Conclusion 181</p> <p><b>Chapter 6 Genetic Programming for Machine Learning 183</b></p> <p>6.1 Introduction 183</p> <p>6.2 Syntax tree representation 186</p> <p>6.3 Evolving the syntax trees 187</p> <p>6.4 GP in action: an introductory example 194</p> <p>6.5 Alternative Genetic Programming Representations 200</p> <p>6.6 Example of application: intrusion detection in a computer system 210</p> <p>6.7 Conclusion 215</p> <p>Bibliography 217</p> <p>Index 233</p>
Control code
AH30994275
Extent
256 pages
Form of item
electronic
Governing access note
After 5 minutes Preview, click on &#x32;Request Access&#x33;, fill in a form with your details. If triggered, the book will be loaned and tied to the one user for 1 week, during which time users can read or download as they choose. 4th user request triggers auto-purchase
Isbn
9781848218048
Media category
computer
Media MARC source
rdamedia
Specific material designation
remote
Label
Evolutionary algorithms, Alain Petrowski, Sana Ben-Hamida, (electronic book)
Publication
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
<p>Preface xi</p> <p><b>Chapter 1 Evolutionary Algorithms 1</b></p> <p>1.1 From natural evolution to engineering 1</p> <p>1.2 A generic evolutionary algorithm 3</p> <p>1.3 Selection operators 5</p> <p>1.4 Variation operators and representation 21</p> <p>1.5 Binary representation 25</p> <p>1.6 The simple genetic algorithm 30</p> <p>1.7 Conclusion 31</p> <p><b>Chapter 2 Continuous Optimization 33</b></p> <p>2.1 Introduction 33</p> <p>2.2 Real representation and variation operators for evolutionary algorithms 35</p> <p>2.3 Covariance Matrix Adaptation Evolution Strategy 46</p> <p>2.4 A restart CMA Evolution Strategy 55</p> <p>2.5 Differential Evolution (DE) 57</p> <p>2.6 Success-History based Adaptive Differential Evolution (SHADE) 65</p> <p>2.7 Particle Swarm Optimization 70</p> <p>2.8 Experiments and performance comparisons 77</p> <p>2.9 Conclusion 88</p> <p>2.10 Appendix: set of basic objective functions used for the experiments 89</p> <p><b>Chapter 3 Constrained Continuous Evolutionary Optimization 93</b></p> <p>3.1 Introduction 93</p> <p>3.2 Penalization 98</p> <p>3.3 Superiority of feasible solutions 112</p> <p>3.4 Evolving on the feasible region 117</p> <p>3.5 Multi-objective methods 123</p> <p>3.6 Parallel population approaches 130</p> <p>3.7 Hybrid methods 132</p> <p>3.8 Conclusion 132</p> <p><b>Chapter 4 Combinatorial Optimization 135</b></p> <p>4.1 Introduction 135</p> <p>4.2 The binary representation and variation operators 140</p> <p>4.3 Order-based Representation and variation operators 143</p> <p>4.4 Conclusion 163</p> <p><b>Chapter 5 Multi-objective Optimization 165</b></p> <p>5.1 Introduction 165</p> <p>5.2 Problem formalization 166</p> <p>5.3 The quality indicators 167</p> <p>5.4 Multi-objective evolutionary algorithms 169</p> <p>5.5 Methods using a &ldquo;Pareto ranking&rdquo; 169</p> <p>5.6 Many-objective problems 176</p> <p>5.7 Conclusion 181</p> <p><b>Chapter 6 Genetic Programming for Machine Learning 183</b></p> <p>6.1 Introduction 183</p> <p>6.2 Syntax tree representation 186</p> <p>6.3 Evolving the syntax trees 187</p> <p>6.4 GP in action: an introductory example 194</p> <p>6.5 Alternative Genetic Programming Representations 200</p> <p>6.6 Example of application: intrusion detection in a computer system 210</p> <p>6.7 Conclusion 215</p> <p>Bibliography 217</p> <p>Index 233</p>
Control code
AH30994275
Extent
256 pages
Form of item
electronic
Governing access note
After 5 minutes Preview, click on &#x32;Request Access&#x33;, fill in a form with your details. If triggered, the book will be loaned and tied to the one user for 1 week, during which time users can read or download as they choose. 4th user request triggers auto-purchase
Isbn
9781848218048
Media category
computer
Media MARC source
rdamedia
Specific material designation
remote

Library Locations

Processing Feedback ...