Genetic Algorithms (GAs) have become a highly effective tool  for solving hard optimization problems. As their popularity has  increased, the number of GA applications has grown in more than equal  measure. Genetic Algorithm theory, however, has not kept pace with the  growing use and application of GAs. Most book-length treatments of GAs  provide only a cursory discussion of theory and this discussion  primarily focuses on the traditional view, which depends heavily on  the concept of a "schema".   Genetic Algorithms: Principles and Perspectives: A Guide to GA  Theory is a survey of some important theoretical contributions,  many of which have been proposed and developed in the Foundations of  Genetic Algorithms series of workshops. However, this theoretical work  is still rather fragmented, and the authors believe that it is the  right time to provide the field with a systematic presentation of the  current state of theory in the form of a set of theoretical  perspectives. The authors do this in the interest of providing  students and researchers with a balanced foundational survey of some  recent research on GAs. The scope of the book includes chapter-length  discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs  and Markov Processes, Dynamical Systems Model, Statistical Mechanics  Approximations, Predicting GA Performance, Landscapes and Test  Problems.   The authors have worked hard to make the book as accessible as  possible for students and researchers. An undergraduate-level  mathematical understanding of linear algebra and stochastic processes  is assumed. For those readers who have not encountered GAs before, a  comprehensive survey of GA concepts is provided and the variety of  ways in which GAs can be implemented is outlined. Exercises are  provided at the ends of the chapters with the express purpose of  aiding understanding of the concepts discussed and to whet the  reader's appetite for pursuing theoretical research in GAs.

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