Practical Genetic Algorithms

PRACTICAL GENETIC ALGORITHMS......Page 4
CONTENTS......Page 10
Preface......Page 14
Preface to First Edition......Page 16
List of Symbols......Page 18
1.1 Finding the Best Solution......Page 22
1.1.1 What Is Optimization?......Page 23
1.1.3 Categories of Optimization......Page 24
1.2.1 Exhaustive Search......Page 26
1.2.2 Analytical Optimization......Page 28
1.2.3 Nelder-Mead Downhill Simplex Method......Page 31
1.2.4 Optimization Based on Line Minimization......Page 34
1.3 Natural Optimization Methods......Page 39
1.4 Biological Optimization: Natural Selection......Page 40
1.5 The Genetic Algorithm......Page 43
Bibliography......Page 45
Exercises......Page 46
2.1 Genetic Algorithms: Natural Selection on a Computer......Page 48
2.2 Components of a Binary Genetic Algorithm......Page 49
2.2.1 Selecting the Variables and the Cost Function......Page 51
2.2.2 Variable Encoding and Decoding......Page 53
2.2.4 Natural Selection......Page 57
2.2.5 Selection......Page 59
2.2.6 Mating......Page 62
2.2.7 Mutations......Page 64
2.2.8 The Next Generation......Page 65
2.3 A Parting Look......Page 68
Exercises......Page 70
3 The Continuous Genetic Algorithm......Page 72
3.1.1 The Example Variables and Cost Function......Page 73
3.1.2 Variable Encoding, Precision, and Bounds......Page 74
3.1.4 Natural Selection......Page 75
3.1.6 Mating......Page 77
3.1.7 Mutations......Page 81
3.1.8 The Next Generation......Page 83
3.1.9 Convergence......Page 85
Exercises......Page 86
4.1 “Mary Had a Little Lamb”......Page 88
4.2 Algorithmic Creativity—Genetic Art......Page 92
4.3 Word Guess......Page 96
4.4 Locating an Emergency Response Unit......Page 98
4.5 Antenna Array Design......Page 102
4.6 The Evolution of Horses......Page 107
Bibliography......Page 113
5.1 Handling Expensive Cost Functions......Page 116
5.2 Multiple Objective Optimization......Page 118
5.2.2 Pareto Optimization......Page 120
5.3 Hybrid GA......Page 122
5.4 Gray Codes......Page 125
5.5 Gene Size......Page 127
5.6 Convergence......Page 128
5.7 Alternative Crossovers for Binary GAs......Page 131
5.8 Population......Page 138
5.9 Mutation......Page 142
5.10 Permutation Problems......Page 145
5.11 Selecting GA Parameters......Page 148
5.12 Continuous versus Binary GA......Page 156
5.13 Messy Genetic Algorithms......Page 157
5.14 Parallel Genetic Algorithms......Page 158
5.14.2 Strategies for Parallel GAs......Page 159
5.14.3 Expected Speedup......Page 162
5.14.4 An Example Parallel GA......Page 165
Bibliography......Page 166
Exercises......Page 169
6.1 Traveling Salesperson Problem......Page 172
6.2 Locating an Emergency Response Unit Revisited......Page 174
6.3 Decoding a Secret Message......Page 176
6.4 Robot Trajectory Planning......Page 177
6.5 Stealth Design......Page 182
6.6 Building Dynamic Inverse Models—The Linear Case......Page 186
6.7 Building Dynamic Inverse Models—The Nonlinear Case......Page 191
6.8 Combining GAs with Simulations—Air Pollution Receptor Modeling......Page 196
6.9 Optimizing Artificial Neural Nets with GAs......Page 200
6.10 Solving High-Order Nonlinear Partial Differential Equations......Page 203
Bibliography......Page 205
7.1 Simulated Annealing......Page 208
7.2 Particle Swarm Optimization (PSO)......Page 210
7.3 Ant Colony Optimization (ACO)......Page 211
7.4 Genetic Programming (GP)......Page 216
7.6 Evolutionary Strategies......Page 220
7.7 The Future of Genetic Algorithms......Page 221
Bibliography......Page 222
Exercises......Page 223
Appendix I Test Functions......Page 226
Appendix II MATLAB Code......Page 232
Appendix III High-Performance Fortran Code......Page 254
Glossary......Page 264
Index......Page 272