Evolutionary Algorithms (eBook)

Alain PÉTROWSKI 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.

Preface xi

Chapter 1 Evolutionary Algorithms 1

1.1 From natural evolution to engineering 1

1.2 A generic evolutionary algorithm 3

1.3 Selection operators 5

1.4 Variation operators and representation 21

1.5 Binary representation 25

1.6 The simple genetic algorithm 30

1.7 Conclusion 31

Chapter 2 Continuous Optimization 33

2.1 Introduction 33

2.2 Real representation and variation operators for evolutionary algorithms 35

2.3 Covariance Matrix Adaptation Evolution Strategy 46

2.4 A restart CMA Evolution Strategy 55

2.5 Differential Evolution (DE) 57

2.6 Success-History based Adaptive Differential Evolution (SHADE) 65

2.7 Particle Swarm Optimization 70

2.8 Experiments and performance comparisons 77

2.9 Conclusion 88

2.10 Appendix: set of basic objective functions used for the experiments 89

Chapter 3 Constrained Continuous Evolutionary Optimization 93

3.1 Introduction 93

3.2 Penalization 98

3.3 Superiority of feasible solutions 112

3.4 Evolving on the feasible region 117

3.5 Multi-objective methods 123

3.6 Parallel population approaches 130

3.7 Hybrid methods 132

3.8 Conclusion 132

Chapter 4 Combinatorial Optimization 135

4.1 Introduction 135

4.2 The binary representation and variation operators 140

4.3 Order-based Representation and variation operators 143

4.4 Conclusion 163

Chapter 5 Multi-objective Optimization 165

5.1 Introduction 165

5.2 Problem formalization 166

5.3 The quality indicators 167

5.4 Multi-objective evolutionary algorithms 169

5.5 Methods using a "Pareto ranking" 169

5.6 Many-objective problems 176

5.7 Conclusion 181

Chapter 6 Genetic Programming for Machine Learning 183

6.1 Introduction 183

6.2 Syntax tree representation 186

6.3 Evolving the syntax trees 187

6.4 GP in action: an introductory example 194

6.5 Alternative Genetic Programming Representations 200

6.6 Example of application: intrusion detection in a computer system 210

6.7 Conclusion 215

Bibliography 217

Index 233

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.
(Genetic Programming and Evolvable Machines, December 2018)