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Computer programs are among the most complex structures created by man. The purpose of this book is to apply the notion that structure arises from fitness to one of the central questions in computer science. This book addresses the problem of getting computers to learn to program themselves by providing a domain-independent way to search the space of possible computer programs for a program that solves a given problem. The two main points that will be made in this book are these: Point 1 - A wide variety of seemingly different problems from many different fields can be recast as requiring the discovery of a computer program that produces some desired output when presented with particular inputs. That is, many seemingly different problems can be reformulated as problems of program induction. Point 2 - The recently developed genetic programming paradigm described in this book provides a way to do program induction. That is, genetic programming can search the space of possible computer programs for an individual computer program that is highly fit in solving (or approximately solving) the problem at hand. The computer program (i.e., structure) that emerges from the genetic programming paradigm is a consequence of fitness. That is, fitness begets the needed program structure. This book organized by: Chapter 1 introduces the two main points to be made. Chapter 2 shows that a wide variety of seemingly different problems in a number of fields can be viewed as problems of program induction. chapter 3 describes the conventional genetic algorithm and introduces certain terms common to the conventional genetic algorithm and genetic programming. Chapter 4 discusses the representation problem for the conventional genetic algorithm operating on fixed-length character strings and variations of the conventional genetic algorithm dealing with structures more complex and flexible than fixed-length character strings. Chapter 5 provides an informal overview of the genetic programming paradigm, chapter 6 provides a detailed description of the techniques of genetic programming. Chapter 7 provides a detailed description of how to apply genetic programming to four introductory examples. Chapter 8 discusses the amount of computer processing required by the genetic programming paradigm to solve certain problems. Chapter 9 shows that the results obtained from genetic programming are not the fruits of random search. Chapters 10 through 21 illustrate how to use genetic programming to solve a wide variety of problems from a wide variety of fields. chapter 10 symbolic regression; error driven evolution chapter 11 control and optimal control; cost-driven evolution chapter 12 evolution of emergent behavior chapter 13 evolution of subsumption chapter 14 entropy driven evolution chapter 15 evolution of strategies chapter 16 co-evolution chapter 17 evolution of classification chapter 18 evolution of iteration and recursion chapter 19 evolution of programs with syntactic structure chapter 20 evolution of building blocks by means of automatic function definition Chapter 21 evolution of hierarchical building blocks by means of hierarchical automatic function definition Chapter 22 discusses implementation of genetic programming on parallel computer architectures. Chapter 23 discusses the ruggedness of genetic programming with respect to noise, sampling, change, and damage. Chapter 24 discusses the role of extraneous variables and functions. Chapter 25 presents the results of some experiments relating to operational issues in genetic programming. Chapter 26 summarizes the five major steps in preparing to use genetic programming. Chapter 27 compares genetic programming to other machine learning paradigms. Chapter 28 discusses the spontaneous emergence of self-replicating, sexually-reproducing, and self-improving computer programs. Chapter 29 is the conclusion