| Introduction | p. 1 |
| Motivation | p. 1 |
| A Survey of This Book | p. 2 |
| Foundations of Evolutionary Computation | |
| Evolutionary Algorithms | p. 9 |
| Introduction to Evolutionary Computation | p. 9 |
| Computational Intelligence | p. 9 |
| Optimization Problems and Stochastic Convergence | p. 11 |
| Classic Optimization Methods | p. 12 |
| A Short Excursus to Molecular Biology and Genetics | p. 13 |
| Concepts of the Evolutionary Computation Framework | p. 13 |
| Types of Evolutionary Algorithms | p. 17 |
| Evolution Strategies | p. 21 |
| The ([mu]/[rho superscript +], [lambda])-ES | p. 21 |
| The ([mu], [kappa], [lambda], [rho])-ES | p. 22 |
| Practical Guidelines for Evolutionary Algorithms | p. 22 |
| Theories of Evolutionary Algorithms | p. 24 |
| Summary | p. 27 |
| Self-Adaptation | p. 29 |
| History of Parameter Adaptation | p. 29 |
| An Extended Taxonomy of Parameter Setting Techniques | p. 30 |
| Preliminary Work | p. 30 |
| Parameter Tuning | p. 31 |
| Parameter Control | p. 32 |
| Typical Adapted Parameters | p. 35 |
| The Concept of Self-Adaptation | p. 37 |
| Self-Adaptation of Global Parameters | p. 40 |
| Theoretical Approaches Toward Self-Adaptation | p. 40 |
| A Self-Adaptive Estimation of Distribution View on EAs | p. 42 |
| Preliminary Views on Self-Adaptation | p. 42 |
| Estimation of Distribution Algorithms | p. 43 |
| Self-Adaptation: Evolving the Estimation of Distribution | p. 43 |
| Views on the Proposed Operators and Problems of This Work | p. 44 |
| Premature Convergence | p. 45 |
| Summary | p. 46 |
| Self-Adaptive Operators | |
| Biased Mutation for Evolution Strategies | p. 51 |
| Mutation Operators for Evolution Strategies | p. 51 |
| Uncorrelated Isotropic Gaussian Mutation with One Step Size | p. 53 |
| Uncorrelated Gaussian Mutation with N Step Sizes | p. 54 |
| Correlated Mutation | p. 54 |
| Asymmetric Density Functions - Directed Mutation | p. 55 |
| Cauchy Mutation | p. 56 |
| Covariance Matrix Adaptation (CMA) | p. 56 |
| Self-Adaptive Mutation for Binary Representations | p. 58 |
| Mutation Operators for Strategy Parameters | p. 58 |
| The Biased Mutation Operator | p. 58 |
| BMO Concept | p. 59 |
| Sphere Biased Mutation Operator (sBMO) | p. 60 |
| Cube Biased Mutation Operator (cBMO) | p. 61 |
| Comparison of Computational Effort of the Randomized Operators | p. 61 |
| The Descent Direction Mutation Operator (DMO) | p. 62 |
| Success Rate on Monotone Functions | p. 63 |
| Experimental Analysis | p. 64 |
| Unconstrained Real Parameter Optimization | p. 65 |
| Climbing Ridges with Biased Mutation | p. 71 |
| Handling Constraints with Biased Mutation | p. 74 |
| Excursus: Self-Adaptive Mutation Operator Selection | p. 79 |
| Summary | p. 80 |
| Self-Adaptive Inversion Mutation | p. 81 |
| Introduction | p. 81 |
| The Traveling Salesman Problem | p. 81 |
| Evolutionary Combinatorial Optimization | p. 82 |
| Self-Adaptation for Discrete Strategy Variables | p. 82 |
| Self-Adaptive Inversion Mutation | p. 83 |
| Convergence Properties | p. 84 |
| Experimental Analysis | p. 86 |
| TSP Instance Berlin52 | p. 87 |
| TSP Instance Bier127 | p. 88 |
| TSP Instance Gr666 | p. 89 |
| Small TSP Instances | p. 92 |
| Statistical Test | p. 92 |
| The Strategy Bound Problem and SA-INV-c | p. 92 |
| Summary | p. 94 |
| Self-Adaptive Crossover | p. 97 |
| The Self-Adaptive Crossover Concept | p. 97 |
| The Role of Crossover - Building Blocks or Genetic Repair? | p. 98 |
| Preliminary Work | p. 99 |
| Adaptation of the Crossover Structure | p. 99 |
| Self-Adaptive n-Point Crossover | p. 101 |
| SA-1-Point | p. 101 |
| SA-n-Point | p. 103 |
| Self-Adaptive Uniform and Multi Parent Crossover | p. 103 |
| Self-Adaptive Partially Mapped Crossover | p. 105 |
| Self-Adaptive Recombination for Evolution Strategies (SAR) | p. 107 |
| Intermediate and Dominant Recombination | p. 108 |
| Self-Adaptive Recombination | p. 108 |
| SAR Variants | p. 109 |
| Experimental Analysis | p. 109 |
| Crossover Point Optimization | p. 111 |
| Summary | p. 112 |
| Constraint Handling | |
| Constraint Handling Heuristics for Evolution Strategies | p. 117 |
| The NLP Problem | p. 117 |
| A Short Survey of Constraint-Handling Methods | p. 118 |
| Penalty Functions | p. 118 |
| Penalty-Related Methods | p. 119 |
| Repair Algorithms | p. 119 |
| Decoder Functions | p. 119 |
| Multiobjective Optimization | p. 120 |
| Constraint Preserving Operators and Representations | p. 120 |
| Recent Developments | p. 120 |
| Premature Step Size Reduction | p. 120 |
| Experimental Analysis | p. 121 |
| Theoretical Analysis | p. 121 |
| The Death Penalty Step Control Approach | p. 127 |
| Basic Minimum Step Size Reduction Mechanism | p. 127 |
| Experimental Analysis | p. 128 |
| Constraint-Handling with Two Sexes | p. 131 |
| Biologically Inspired Constraint-Handling | p. 131 |
| Modifications of the Basic Two Sexes Evolution Strategy | p. 132 |
| Experimental Analysis | p. 133 |
| The Nested Angle Evolution Strategy | p. 137 |
| Meta-evolution for Mutation Ellipsoid Rotation | p. 137 |
| Experimental Analysis | p. 138 |
| Outlook: Covariance Matrix Adaptation by Feasibility Classification | p. 139 |
| Summary | p. 139 |
| Summary | |
| Summary and Conclusion | p. 143 |
| Contributions of This Book | p. 143 |
| Conclusion | p. 146 |
| Appendix | |
| Continuous Benchmark Functions | p. 149 |
| Unimodal Numerical Functions | p. 149 |
| Multimodal Numerical Functions | p. 150 |
| Ridge Functions | p. 152 |
| Numerical Functions in Bit String Representations | p. 153 |
| Constrained Numerical Functions | p. 154 |
| Discrete Benchmark Functions | p. 159 |
| Traveling Salesman Problems | p. 159 |
| SAT-Problem | p. 161 |
| References | p. 163 |
| List of Figures | p. 173 |
| List of Tables | p. 175 |
| Index | p. 179 |
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