Practical genetic algorithms / Randy L. Haupt, Sue Ellen Haupt.

Format:

Book

Author/Creator:

Haupt, Randy L.

Contributor:

Haupt, S. E.

Haupt, Randy L.

Language:

English

Subjects (All):

Genetic algorithms.

Algorithms.

Genetic Phenomena.

Evolution.

Medical Subjects:

Algorithms.

Genetic Phenomena.

Evolution.

Physical Description:

xvii, 253 pages : illustrations ; 24 cm + 1 CD-ROM (4 3/4 in.)

Edition:

Second edition.

Place of Publication:

Hoboken, N.J. : John Wiley, [2004]

Summary:

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. 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.

Contents:

1 Introduction to Optimization 1

1.1 Finding the Best Solution 1

1.1.1 What Is Optimization? 2

1.1.2 Root Finding versus Optimization 3

1.1.3 Categories of Optimization 3

1.2 Minimum-Seeking Algorithms 5

1.2.1 Exhaustive Search 5

1.2.2 Analytical Optimization 7

1.2.3 Nelder-Mead Downhill Simplex Method 10

1.2.4 Optimization Based on Line Minimization 13

1.3 Natural Optimization Methods 18

1.4 Biological Optimization: Natural Selection 19

1.5 The Genetic Algorithm 22

2 The Binary Genetic Algorithm 27

2.1 Genetic Algorithms: Natural Selection on a Computer 27

2.2 Components of a Binary Genetic Algorithm 28

2.2.1 Selecting the Variables and the Cost Function 30

2.2.2 Variable Encoding and Decoding 32

2.2.3 The Population 36

2.2.4 Natural Selection 36

2.2.5 Selection 38

2.2.6 Mating 41

2.2.7 Mutations 43

2.2.8 The Next Generation 44

2.2.9 Convergence 47

2.3 A Parting Look 47

3 The Continuous Genetic Algorithm 51

3.1 Components of a Continuous Genetic Algorithm 52

3.1.1 The Example Variables and Cost Function 52

3.1.2 Variable Encoding, Precision, and Bounds 53

3.1.3 Initial Population 54

3.1.4 Natural Selection 54

3.1.5 Pairing 56

3.1.6 Mating 56

3.1.7 Mutations 60

3.1.8 The Next Generation 62

3.1.9 Convergence 64

3.2 A Parting Look 65

4 Basic Applications 67

4.1 "Mary Had a Little Lamb" 67

4.2 Algorithmic Creativity

Genetic Art 71

4.3 Word Guess 75

4.4 Locating an Emergency Response Unit 77

4.5 Antenna Array Design 81

4.6 The Evolution of Horses 86

5 An Added Level of Sophistication 95

5.1 Handling Expensive Cost Functions 95

5.2 Multiple Objective Optimization 97

5.2.1 Sum of Weighted Cost Functions 99

5.2.2 Pareto Optimization 99

5.3 Hybrid GA 101

5.4 Gray Codes 104

5.5 Gene Size 106

5.6 Convergence 107

5.7 Alternative Crossovers for Binary GAs 110

5.8 Population 117

5.9 Mutation 121

5.10 Permutation Problems 124

5.11 Selecting GA Parameters 127

5.12 Continuous versus Binary GA 135

5.13 Messy Genetic Algorithms 136

5.14 Parallel Genetic Algorithms 137

5.14.1 Advantages of Parallel GAs 138

5.14.2 Strategies for Parallel GAs 138

5.14.3 Expected Speedup 141

5.14.4 An Example Parallel GA 144

5.14.5 How Parallel GAs Are Being Used 145

6 Advanced Applications 151

6.1 Traveling Salesperson Problem 151

6.2 Locating an Emergency Response Unit Revisited 153

6.3 Decoding a Secret Message 155

6.4 Robot Trajectory Planning 156

6.5 Stealth Design 161

6.6 Building Dynamic Inverse Models

The Linear Case 165

6.7 Building Dynamic Inverse Models

The Nonlinear Case 170

6.8 Combining GAs with Simulations

Air Pollution Receptor Modeling 175

6.9 Optimizing Artificial Neural Nets with GAs 179

6.10 Solving High-Order Nonlinear Partial Differential Equations 182

7 More Natural Optimization Algorithms 187

7.1 Simulated Annealing 187

7.2 Particle Swarm Optimization (PSO) 189

7.3 Ant Colony Optimization (ACO) 190

7.4 Genetic Programming (GP) 195

7.5 Cultural Algorithms 199

7.6 Evolutionary Strategies 199

7.7 The Future of Genetic Algorithms 200

Appendix I Test Functions 205

Appendix II MATLAB Code 211

Appendix III High-Performance Fortran Code 233.

Notes:

Rev. ed. of: Practical genetic algorithms. c1998.

"A Wiley-Interscience publication."

Includes bibliographical references and index.

Related Work:

Haupt, Randy L. Practical genetic algorithms.

ISBN:

0471455652

OCLC:

54407452

Online:

Contributor biographical information

Publisher description