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Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems) 1st Edition

11 customer reviews
ISBN-13: 978-0262111706
ISBN-10: 0262111705
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Editorial Reviews

Review

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)

About the Author

John R. Koza is Consulting Associate Professor in the Computer Science Department at Stanford University.

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Product Details

  • 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 (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (11 customer reviews)
  • Amazon Best Sellers Rank: #927,548 in Books (See Top 100 in Books)

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22 of 22 people found the following review helpful By "smokey_joe" on July 4, 2002
Format: Hardcover
Yeah, its a big book...weighs a ton. However, only the first few chapters are concerned with the basic mechanisms of GP (should be familiar to anyone with a background in genetic algorithms or evolutionary computation). The rest of the book is chock full of examples on how to apply GP. These examples are essential and very welcome. I've found that I can usually find a solved problem in Koza that is similar to what I'm after, then I adapt it to my needs. This is a great reference, but don't be fooled into thinking this book is a tutorial. Think of it more as an exposition of GP with examples. For a tutorial, look somewhere else.
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24 of 26 people found the following review helpful By A Customer on October 7, 1996
Format: Hardcover
The short history of computer science as a discipline has
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.
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10 of 11 people found the following review helpful By Casey Wireman on July 11, 2006
Format: Hardcover
I became interested in Genetic Programming after hearing one of the professors at our university lecture on it to a small group of students and other professors. I asked what book might be a good starting point and he pointed me here and i'm glad he did.

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.
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7 of 9 people found the following review helpful By Howard Schneider on November 26, 2000
Format: Hardcover
Genetic algorithms refer to computer programs that 'evolve' in ways similar to biological organisms. 'Natural selection' specifies the features of the solution to look for, strings of binary numbers (or other similar structures) are mated, with the combination of strings containing partial solutions often producing the most 'fit' results. Generation after generation of this process continues towards the 'evolution' of the desired features. Although this reference is quite long, it is quite readable, and can be shortened significantly by omitting a number of subsections as well as chapters not essential to the core concepts, as well as the detailed appendices. This reference shows that a variety of problems from different fields can be solved in terms of a computer program, of which genetic programming can be the means to find one or more such valid computer programs. It is relevant in that genetic programming is another way to effect computation, as well as providing insight with respect to evolution in nature.
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4 of 5 people found the following review helpful By calvinnme HALL OF FAMETOP 1000 REVIEWERVINE VOICE on June 30, 2007
Format: Hardcover
This is a great "how to" book loaded with examples of how to implement genetic algorithms. The two main points this book makes is that many seemingly different problems can be reformulated as problems of program induction and that the genetic programming paradigm described in this book provides a way to do that program induction. No prior knowledge of conventional genetic algorithms is assumed. Thus the first three chapters are introductory material. In particular, chapter three describes the conventional genetic algorithm and introduces certain terms common to the conventional genetic algorithm and genetic programming. If you are already familiar with genetic algorithms you can skip ahead.

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.
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