文件名称：Genetic Programming Theory and Practice II - John Koza.pdf
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Genetic programming Complex adaptive systems
Preface Organization of the Book 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. No prior knowledge of conventional genetic algorithms is assumed. Accordingly, chapter 3 describes the conventional genetic algorithm and introduces certain terms common to the conventional genetic algorithm and genetic programming. The reader who is already familiar with genetic algorithms may wish to skip this chapter. 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. This book assumes no prior knowledge of the LISP programming language. Accordingly, section 4.2 describes LISP. Section 4.3 outlines the reasons behind the choice of LISP for the work described herein. Chapter 5 provides an informal overview of the genetic programming paradigm, and chapter 6 provides a detailed description of the techniques of genetic programming. Some readers may prefer to rely on chapter 5 and to defer reading the detailed discussion in chapter 6 until they have read chapter 7 and the later chapters that contain examples. Chapter 7 provides a detailed description of how to apply genetic programming to four introductory examples. This chapter lays the groundwork for all the problems to be described later in the book. 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. These chapters are divided as follows: • symbolic regression; error- driven evolution—chapter 10 • control and optimal control; cost-driven evolution—chapter 11 Page x • evolution of emergent behavior—chapter 12 • evolution of subsumption—chapter 13 • entropy- driven evolution—chapter 14 • evolution of strategies—chapter 15 • co- evolution—chapter 16 • evolution of classification—chapter 17 • evolution of iteration and recursion—chapter 18 • evolution of programs with syntactic structure—chapter 19 • evolution of building blocks by means of automatic function definition—chapter 20 • evolution of hierarchical building blocks by means of hierarchical automatic function definition—Chapter 21. 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. Ten appendixes discuss computer implementation of the genetic programming paradigm and the results of various experiments related to operational issues. Appendix A discusses the interactive user interface used in our computer implementation of genetic programming. Appendix B presents the problem-specific part of the simple LISP code needed to implement genetic programming. This part of the code is presented for three different problems so as to provide three different examples of the techniques of genetic programming. Appendix C presents the simple LISP code for the kernel (i.e., the problem-independent part) of the code for the genetic programming paradigm. It is possible for the user to run many different problems without ever modifying this kernel. Appendix D presents possible embellishments to the kernel of the simple LISP code. Appendix E presents a streamlined version of the EVAL function. Appendix F presents an editor for simplifying S-expressions.