- Series: Complex Adaptive Systems
- Hardcover: 840 pages
- Publisher: A Bradford Book; 1 edition (December 11, 1992)
- Language: English
- ISBN-10: 0262111705
- ISBN-13: 978-0262111706
- Product Dimensions: 7 x 2.2 x 10 inches
- Shipping Weight: 3.4 pounds
- Average Customer Review: 10 customer reviews
- Amazon Best Sellers Rank: #1,344,901 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems) 1st Edition
Use the Amazon App to scan ISBNs and compare prices.
Fulfillment by Amazon (FBA) is a service we offer sellers that lets them store their products in Amazon's fulfillment centers, and we directly pack, ship, and provide customer service for these products. Something we hope you'll especially enjoy: FBA items qualify for FREE Shipping and Amazon Prime.
If you're a seller, Fulfillment by Amazon can help you increase your sales. We invite you to learn more about Fulfillment by Amazon .
Customers who bought this item also bought
Customers who viewed this item also viewed
The research reported in this book is a tour de force. For the first time, since the idea was bandied about in the '40s and early '50s, we have a non-trivial, nontailored set of examples of automatic programming."--John Holland
John Koza has discovered a general and robust method of evolving computer programs that is effective over a breathtaking range of problems in applied mathematics, control engineering, and artificial intelligence.―Stewart W. Wilson, The Rowland Institute for Science (Endorsement)
The research reported in this book is a tour de force. For the first time, since the idea was bandied about in the '40s and early '50s, we have a non-trivial, nontailored set of examples of automatic programming.―John Holland, Professor of Psychology and Professor of Computer Science and Engineering, University of Michigan; External Professor, Santa Fe Institute (Endorsement)
Top customer reviews
There was a problem filtering reviews right now. Please try again later.
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. Since this book assumes no prior knowledge of the LISP programming language, section 4.2 describes LISP and section 4.3 outlines the reasons behind the choice of LISP for the implementation of solutions in this book. 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 hold off on 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 thus laying the groundwork for all of 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 a random search. Chapters 10 through 21 illustrate how to use genetic programming to solve a wide variety of problems from varying disciplines and are defined by the table of contents. The examples in these 12 chapters make up the heart of the book.
The final eight chapters discuss aspects of genetic algorithms common to all implementations. Chapter 22 discusses the 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, and 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 while chapter 27 compares genetic programming to other machine learning paradigms. Chapter 28 is an interesting one in which the spontaneous emergence of self-replicating and self-improving computer programs is discussed. Chapter 29 attempts to wrap up the book with a conclusion.
This book is best used for its examples and practical viewpoint. There are certain matters, such as how to program in LISP, for which you will need dedicated books since the amount of detail in this book is not enough. I do highly recommend this book as a uniquely practical one on how to implement genetic algorithms via computer programs. I haven't found another with so much practical information.
This first volume in the Genetic Programming series of books by Koza is very well organized and clear in its explanations. I have not tried the techniques presented yet, but I have some good ideas on how to proceed. The author uses LISP as the language of choice in the book, but practically any modern language should be sufficient.
If you have any interest in Genetic Programming, I encourage you to at least pick up this first volume and read through it. This technology is still relatively new and the application of the techniques seems virtually limitless.