Preface. Preface to First Edition. List of Symbols. 1. Introduction to Optimization. 1.1 Finding the Best Solution. 1.

2 Minimum-Seeking Algorithms. 1.3 Natural Optimization Methods. 1.4 Biological Optimization: Natural Selection. 1.5 The Genetic Algorithm. 2.

The Binary Genetic Algorithm. 2.1 Genetic Algorithms: Natural Selection on a Computer. 2.2 Components of a Binary Genetic Algorithm. 2.3 A Parting Look. 3.

The Continuous Genetic Algorithm. 3.1 Components of a Continuous Genetic Algorithm. 3.2 A Parting Look. 4. Basic Applications. 4.

1 "Mary Had a Little Lamb". 4.2 Algorithmic Creativity-Genetic Art. 4.3 Word Guess. 4.4 Locating an Emergency Response Unit. 4.

5 Antenna Array Design. 4.6 The Evolution of Horses. 4.7 Summary. 5. An Added Level of Sophistication. 5.

1 Handling Expensive Cost Functions. 5.2 Multiple Objective Optimization. 5.3 Hybrid GA. 5.4 Gray Codes. 5.

5 Gene Size. 5.6 Convergence. 5.7 Alternative Crossovers for Binary GAs. 5.8 Population. 5.

9 Mutation. 5.10 Permutation Problems. 5.11 Selling GA Parameters. 5.12 Continuous versus Binary GA. 5.

13 Messy Genetic Algorithms. 5.14 Parallel Genetic Algorithms. 6. Advanced Applications. 6.1 Traveling Salespersons Problem. 6.

2 Locating an Emergency Response Unit Revisited. 6.3 Decoding a Secret Message. 6.4 Robot Trajectory Planning. 6.5 Stealth Design. 6.

6 Building Dynamical Inverse Models-The Linear Case. 6.7 Building Dynamical Inverse Models-The Nonlinear Case. 6.8 Combining GAs with Simulations-Air Pollution Receptor Modeling. 6.9 Combining Methods Neural Nets with GAs. 6.

10 Solving High-Order Nonlinear Partial Differential Equations. 7. More Natural Optimization Algorithms. 7.1 Simulated Annealing. 7.2 Particle Swarm Optimization (PSO). 7.

3 Ant Colony Optimization (ACO). 7.4 Genetic Programming (GP). 7.5 Cultural Algorithms. 7.6 Evolutionary Strategies. 7.

7 The Future of Genetic Algorithms. Appendix I: Test Functions. Appendix II: MATLAB Code. Appendix III. High-Performance Fortran Code. Glossary. Index.