Haupt R.L, Haupt S.E. Practical Genetic Algorithms

Издательство John Wiley, 2004,-272 pp.When we agreed to edit this book for a second edition, we looked forward to a bit of updating and including some of our latest research results. However, the effort grew rapidly beyond our original vision. The use of genetic algorithms (GAs) is a quickly evolving field of research, and there is much new to recommend. Practitioners are constantly dreaming up new ways to improve and use GAs. Therefore this book differs greatly from the first edition.
We continue to emphasize the Practical part of the title. This book was written for the practicing scientist, engineer, economist, artist, and whoever might possibly become interested in learning the basics of GAs. We make no claims of including the latest research on convergence theory: instead, we refer the reader to references that do. We do, however, give the reader a flavor for how GAs are being used and how to fiddle with them to get the best performance.
The biggest addition is including code—both MatLAB and a bit of High- Performance Fortran. We hope the readers find these a useful start to their own applications. There has also been a good bit of updating and expanding. Chapter 1 has been rewritten to give a more complete picture of traditional optimization. Chapters 2 and 3 remain dedicated to introducing the mechanics of the binary and continuous GA. The examples in those chapters, as well as throughout the book, now reflect our more recent research on choosing GA parameters. Examples have been added to Chapters 4 and 6 that broaden the view of problems being solved. Chapter 5 has greatly expanded its recommendations of methods to improve GA performance. Sections have been added on hybrid GAs, parallel GAs, and messy GAs. Discussions of parameter selection reflect new research. Chapter 7 is new. Its purpose is to give the reader a flavor for other artificial intelligence methods of optimization, like simulated annealing, ant colony optimization, and evolutionary strategies. We hope this will help put GAs in context with other modern developments. We included code listings and test functions in the appendixes. Exercises appear at the end of each chapter. There is no solution manual because the exercises are open-ended. These should be helpful to anyone wishing to use this book as a text.
In addition to the people thanked in the first edition, we want to recognize the students and colleagues whose insight has contributed to this effort. Bonny Haupt did the work included in Section 4.6 on horse evolution. Jaymon Knight translated our GA to High-Performance Fortran. David Omer and Jesse Warrick each had a hand in the air pollution problem of Section
6.8.We’ve discussed our applications with numerous colleagues and appreciate their feedback.
We wish the readers well in their own forays into using GAs. We look forward to seeing their interesting applications in the future.Introduction to Optimization
The Binary Genetic Algorithm
The Continuous Genetic Algorithm
Basic Applications
An Added Level of Sophistication
Advanced Applications
More Natural Optimization Algorithms
I Test Functions
II MatLAB Code
III High-Performance Fortran Code