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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. This book was written for the practicing scientist, engineer, economist, artist, and whoever might possibly become interested in learning the basics of GAs. 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.