Practical Genetic Algorithms

* 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

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.

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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Practical Genetic Algorithms

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