Practical Genetic Algorithms
by Haupt, Randy L.; Haupt, Sue EllenEdition: 2nd
Format: Hardcover
Pub. Date: 2004-05-31
Publisher(s): Wiley-Interscience
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Summary
* This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science * Most significant update to the second edition is the MATLAB codes that accompany the text * Provides a thorough discussion of hybrid genetic algorithms * Features more examples than first edition
Author Biography
RANDY L. HAUPT, PhD, is Department Head and Senior Scientist at The Pennsylvania State University Applied Research Laboratory, State College, Pennsylvania.
SUE ELLEN HAUPT, PhD, is a Senior Research Associate in the Computational Mechanics Division of The Pennsylvania State University Applied Research Laboratory, State College, Pennsylvania.
Both Randy and Sue Ellen Haupt are renowned experts in the field of genetic algorithms in engineering and science applications.
Table of Contents
| Preface | p. xi |
| Preface to First Edition | p. xiii |
| List of Symbols | p. xv |
| Introduction to Optimization | p. 1 |
| Finding the Best Solution | p. 1 |
| What Is Optimization? | p. 2 |
| Root Finding versus Optimization | p. 3 |
| Categories of Optimization | p. 3 |
| Minimum-Seeking Algorithms | p. 5 |
| Exhaustive Search | p. 5 |
| Analytical Optimization | p. 7 |
| Nelder-Mead Downhill Simplex Method | p. 10 |
| Optimization Based on Line Minimization | p. 13 |
| Natural Optimization Methods | p. 18 |
| Biological Optimization: Natural Selection | p. 19 |
| The Genetic Algorithm | p. 22 |
| Bibliography | p. 24 |
| Exercises | p. 25 |
| The Binary Genetic Algorithm | p. 27 |
| Genetic Algorithms: Natural Selection on a Computer | p. 27 |
| Components of a Binary Genetic Algorithm | p. 28 |
| Selecting the Variables and the Cost Function | p. 30 |
| Variable Encoding and Decoding | p. 32 |
| The Population | p. 36 |
| Natural Selection | p. 36 |
| Selection | p. 38 |
| Mating | p. 41 |
| Mutations | p. 43 |
| The Next Generation | p. 44 |
| Convergence | p. 47 |
| A Parting Look | p. 47 |
| Bibliography | p. 49 |
| Exercises | p. 49 |
| The Continuous Genetic Algorithm | p. 51 |
| Components of a Continuous Genetic Algorithm | p. 52 |
| The Example Variables and Cost Function | p. 52 |
| Variable Encoding, Precision, and Bounds | p. 53 |
| Initial Population | p. 54 |
| Natural Selection | p. 54 |
| Pairing | p. 56 |
| Mating | p. 56 |
| Mutations | p. 60 |
| The Next Generation | p. 62 |
| Convergence | p. 64 |
| A Parting Look | p. 65 |
| Bibliography | p. 65 |
| Exercises | p. 65 |
| Basic Applications | p. 67 |
| "Mary Had a Little Lamb" | p. 67 |
| Algorithmic Creativity--Genetic Art | p. 71 |
| Word Guess | p. 75 |
| Locating an Emergency Response Unit | p. 77 |
| Antenna Array Design | p. 81 |
| The Evolution of Horses | p. 86 |
| Summary | p. 92 |
| Bibliography | p. 92 |
| An Added Level of Sophistication | p. 95 |
| Handling Expensive Cost Functions | p. 95 |
| Multiple Objective Optimization | p. 97 |
| Sum of Weighted Cost Functions | p. 99 |
| Pareto Optimization | p. 99 |
| Hybrid GA | p. 101 |
| Gray Codes | p. 104 |
| Gene Size | p. 106 |
| Convergence | p. 107 |
| Alternative Crossovers for Binary GAs | p. 110 |
| Population | p. 117 |
| Mutation | p. 121 |
| Permutation Problems | p. 124 |
| Selecting GA Parameters | p. 127 |
| Continuous versus Binary GA | p. 135 |
| Messy Genetic Algorithms | p. 136 |
| Parallel Genetic Algorithms | p. 137 |
| Advantages of Parallel GAs | p. 138 |
| Strategies for Parallel GAs | p. 138 |
| Expected Speedup | p. 141 |
| An Example Parallel GA | p. 144 |
| How Parallel GAs Are Being Used | p. 145 |
| Bibliography | p. 145 |
| Exercises | p. 148 |
| Advanced Applications | p. 151 |
| Traveling Salesperson Problem | p. 151 |
| Locating an Emergency Response Unit Revisited | p. 153 |
| Decoding a Secret Message | p. 155 |
| Robot Trajectory Planning | p. 156 |
| Stealth Design | p. 161 |
| Building Dynamic Inverse Models--The Linear Case | p. 165 |
| Building Dynamic Inverse Models--The Nonlinear Case | p. 170 |
| Combining GAs with Simulations--Air Pollution Receptor Modeling | p. 175 |
| Optimizing Artificial Neural Nets with GAs | p. 179 |
| Solving High-Order Nonlinear Partial Differential Equations | p. 182 |
| Bibliography | p. 184 |
| More Natural Optimization Algorithms | p. 187 |
| Simulated Annealing | p. 187 |
| Particle Swarm Optimization (PSO) | p. 189 |
| Ant Colony Optimization (ACO) | p. 190 |
| Genetic Programming (GP) | p. 195 |
| Cultural Algorithms | p. 199 |
| Evolutionary Strategies | p. 199 |
| The Future of Genetic Algorithms | p. 200 |
| Bibliography | p. 201 |
| Exercises | p. 202 |
| Test Functions | p. 205 |
| MATLAB Code | p. 211 |
| High-Performance Fortran Code | p. 233 |
| Glossary | p. 243 |
| Index | p. 251 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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