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.
Cuprins
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
Despre autor
Alain PÉTROWSKI is Associate Professor in the Department of Networks and Mobile Multimedia Services at the Telecom-Sud Paris, Institut Mines-Télécom, Paris-Saclay University, France. His main research interests are related to optimization, metaheuristics and machine learning.
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