Practical Genetic Algorithms (Second Edition)

"The first introductory-level book to emphasize practical applications through the use of example problems." –– International Journal of General Systems, Vol. 31, No. 1, 2002, on the first edition The use of genetic algorithms (GAs) to solve large and often complex computational problems has given rise to many new applications in a variety of disciplines. Practical Genetic Algorithms was the first introductory-level book on genetic algorithms to emphasize practical applications rather than theory. Practical Genetic Algorithms, Second Edition reflects the significant evolution of the field since the book’s first edition. In an accessible style, the authors explain why the genetic algorithm is superior in many real-world applications, cover continuous parameter genetic algorithms, and provide in-depth trade-off analysis of genetic algorithm parameter selection. This Second Edition features: Numerous practical example problems A CD-ROM with MATLAB and High Performance Fortran codes A new, more complete picture of traditional optimization Revised examples reflecting recent research Coverage of pareto-genetic and hybrid genetic algorithms (GAs) New sections on hybrid GAs, parallel GAs, and messy GAs, with recommendations on improving their performance An all new chapter on simulated annealing, ant-colony optimization, evolutionary strategies, and other cutting-edge artificial intelligence methods of optimization Written for the practicing scientist, engineer, economist, artist, or anyone with an interest in the basics of GAs, the second edition continues to offer readers an up-to-date look at the evolving practical applications of GAs and how to manipulate them in order to get the best performance. Booknews Per the glossary of this introductory text, a genetic algorithm (GA) is a type of computation that models the biological genetic process by including crossover and mutation operators. R. Haupt (electrical engineering, U. of Nevada, Reno) and S. Haupt (atmospheric and oceanic science, U. of Colorado, Boulder) explain GA parameters, applications, and trends in computer modeling of natural processes. Includes a list of symbols, and rather than divorcing over which computer language codes to provide pseudocodes for customizing GAs. Annotation c. by Book News, Inc., Portland, Or. 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 Practical Genetic Algorithms is the first introductory-level book to emphasize practical applications through the use of example problems. In an accessible style, the authors explain why the genetic algorithm is superior in many real-world applications, cover continuous parameter genetic algorithms, and provide in-depth trade-off analysis of genetic algorithm parameter selection. Written for the end user in engineering, science, and computer programming, as well as upper-level undergraduate and graduate students, Practical Genetic Algorithms provides numerous practical example problems; contains over 80 illustrations; features many figures and tables; and includes three appendices: a glossary of terms, a list of genetic algorithm routines in pseudocode, and a list of symbols used in the book. Randy Haupt and Sue Ellen Haupt, both affiliated with The Pennsylvania State University Applied Research Laboratory, emphasize practical applications rather than theory in this second edition of a book/CD- ROM guide for practicing scientists, engineers, economists, artists, and others interested in the basics of genetic algorithms (GAs). This edition contains code, in both MATLAB and High-Performance Fortran, on the CD-ROM, plus expanded information on methods for improving GA performance, and a new chapter on other artificial intelligence methods of optimization such as simulated annealing and ant colony optimization. Chapter exercises allow the book to be used as a text. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com) Teach Like a Champion 2.0 is a complete update to the international bestseller. This teaching guide is a must-have for new and experienced teachers alike. Over 700,000 teachers around the world already know how the techniques in this book turn educators into classroom champions. With ideas for everything from classroom management to inspiring student engagement, you will be able to perfect your teaching practice right away.

* 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

Deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science. This book includes the MATLAB codes and provides a discussion of hybrid genetic algorithms, as well as more examples. It also gives an introduction to the subject of genetic algorithms.