Details

Evolutionary Algorithms


Evolutionary Algorithms


1. Aufl.

von: Alain Petrowski, Sana Ben-Hamida

139,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 12.04.2017
ISBN/EAN: 9781119136385
Sprache: englisch
Anzahl Seiten: 256

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Beschreibungen

<p>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.</p> <p>In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms.</p> <p>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.</p>
<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 “Pareto ranking” 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>
<p>In general, Petrowski and Ben-Hamid display an in-depth understanding of several optimization classes and their corresponding evolutionary algorithms, along with an impressive ability to explain, illustrate, motivate, classify and codify. Although nobody can “do it all” in a field as deep and wide as evolutionary computation, they have chosen a pertinent subset and done a fine job with it. My own copy of “Evolutionary Algorithms” became an instant go-to reference as I prepare for another semester of teaching.<br />(<i>Genetic Programming and Evolvable Machines, December 2018)</i></p>
<p><b>Alain PÉTROWSKI</b> is Associate Professor in the Department of Networks and Mobile Multimedia Services at the Telecom-SudParis, Institut Mines-Télécom, Paris-Saclay University, France. His main research interests are related to optimization, metaheuristics and machine learning.</p>

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