- 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 x 10 inches
- Shipping Weight: 3.4 pounds
- Average Customer Review: 4.4 out of 5 stars See all reviews (11 customer reviews)
- Amazon Best Sellers Rank: #1,201,332 in Books (See Top 100 in Books)
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Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems) 1st Edition
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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)
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)
From the Back Cover
Genetic programming may be more powerful than neural networks and other machine learning techniques; it may be able to solve problems in a wider range of disciplines. In this groundbreaking book, the author shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs.
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Top Customer Reviews
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.
had two major concerns: the production of programs that are
provably efficient, and the production of programs that are
provably correct. "Genetic Programming" is, possibly, the beginning
of a third stream in CS, the production of programs that are possibly
neither efficient nor correct, but
"fit" to perform a given task.
A strange idea to computer scientists, perhaps, but consider
the analogy with living creatures. Is a shark, a bee, or a
turtle either "efficient" or "correct"? Perhaps, perhaps
not; there doesn't seem to be a way to measure these concepts
for something as complex as a living species. But they are
"fit." They've been successful, as species, in their respective
ecological niches for millions of years.
Koza's big idea is the automatic generation of programs
via mutation and selection, by analogy with living systems,
and he's written a big book to go with the big idea (819 pages).
Demonstrating creation of non-trivial programs by means of
simulated mutation & selection is a major accomplishment.
I'd rate the promise of this line of research as high, given
that compute power becomes cheaper every year while human
brain power becomes more expensive. Also, natural systems
are resilient and adaptive to changes in the environment,
while man-made software systems are all too fragile. This
observation leads to the hope that "fit" programs may increase
the robustness of the the computer networks on which so
much now depends.
One quibble: there is a thin book inside this fat book, trying to get out.
The thin book would make the research more accessible to
the average practicing programmer. Until such a "reader's
edition" comes out, "Genetic Programming" is a unique