The first edition of this book (Goldberg, 2002) was welcomed as an important contribution to the understanding and design of scalable genetic algorithms. Goldberg's theory of facetwise models proves invaluable to GA understanding and design, and the core chapters of the book continue to make those important arguments; however, they are brought up to date with the most important recent results, including population timing and sizing results. The chapter on scalable GA design (Chapter 12) gets a thorough overhaul by introducing other key scalable GA techniques, including the DSMGA (Dependency Structure Matrix GA) and others, and discussing how they relate to earlier models. Although the literature tends to emphasize small differences between different methods, the chapter shows the common theoretical and methodological threads running through all scalable methods. The DSMGA results are particularly important because of the light the shed on probabilistic model builders such as the...

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