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.
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.
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.
5.7 Alternative Crossovers for Binary GAs.
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.