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Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence) [Hardcover]

Wolfgang Banzhaf (Author), Peter Nordin (Author), Robert E. Keller (Author), Frank D. Francone (Author)
4.4 out of 5 stars  See all reviews (10 customer reviews)

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Book Description

December 15, 1997 155860510X 978-1558605107 1
Since the early 1990s, genetic programming (GP)-a discipline whose goal is to enable the automatic generation of computer programs-has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks.

This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.

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Editorial Reviews

Amazon.com Review

Imagine a world in which computers program other computers based on strategies borrowed from biology and natural selection. Genetic Programming: An Introduction explores fascinating possibilities like these in a thriving area of computer-science research. This research-quality book is for anyone who wants to see what genetic programming is and what it can offer the future of computing.

This text begins by situating genetic programming in terms of the history of computing and machine learning. Early sections show the links between Darwinism, molecular biology, and genetic programming. (Genetic programming uses the strategy of natural selection by solving a problem in successive iterations, which produces the "fittest" solution, much like new species evolve in the natural world.)

The authors present a lot of molecular-biology background since it is central to the genetic-programming project. (There are interesting parallels here. Just as our DNA contains inert information, programs developed using genetic algorithms usually contain many "extra" instructions, too--which often leads to bloated, though effective, code in the final product.) Even though this is extremely technical material, the authors do manage to engage the reader in the imaginative leap from Darwin and DNA to computers and the world of genetic programming.

Later chapters define what genetic programming is and what strategies it uses to let computers program themselves. The authors also examine the state of the art of genetic programming and define what problems need to be solved before it can be widely adopted. The amount of research in this section will mostly benefit specialists in the genetic-programming field.

A later chapter on applications that use genetic programming offers dozens of papers, with applications of this approach from a wide variety of fields, including biology, industry, and computers (and some impressive technologies such as robotics and data mining). Though the authors exaggerate somewhat on how "real world" these applications are, it's clear that genetic programming will continue to improve and find its way into more areas of computing--with even more productive results. Though coding by humans is safe for the foreseeable future, genetic programming offers an appealing alternative to some kinds of problems. --Richard V. Dragan

Review

"[The authors] have performed a remarkable double service with this excellent book on genetic programming. First, they give an up-to-date view of the rapidly growing field of automatic creation of computer programs by means of evolution and, second, they bring together their own innovative and formidable work on evolution of assembly language machine code and linear genomes."
--John R. Koza

Product Details

  • Hardcover: 512 pages
  • Publisher: Morgan Kaufmann; 1 edition (December 15, 1997)
  • Language: English
  • ISBN-10: 155860510X
  • ISBN-13: 978-1558605107
  • Product Dimensions: 9.6 x 7.7 x 1.2 inches
  • Shipping Weight: 2.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (10 customer reviews)
  • Amazon Best Sellers Rank: #566,223 in Books (See Top 100 in Books)

 

Customer Reviews

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12 of 12 people found the following review helpful:
5.0 out of 5 stars A great introduction!, November 18, 2000
By 
Paul Zimmons (Chapel Hill, North Carolina United States) - See all my reviews
This review is from: Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence) (Hardcover)
This book is a great introduction to genetic programming and should be a model for textbook authors in other fields. Knowing little about genetic programming to begin with, this book guides the reader through the various topics and problems associated with genetic programming in a very logical and understandable way. Highly recommended! I wish more technical books were like this!
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31 of 37 people found the following review helpful:
5.0 out of 5 stars Great Book. It is an original book -- not edited., May 2, 1998
This review is from: Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence) (Hardcover)
I am one of the authors, so I will let others do the reviewing. We have spent some time trying to get Amazon.com to remove the "editor" title from Dr. Banzhaf's name because this is NOT an edited volume. It is an entirely original work.

Alas, to no avail.

We spent three years on this book and are very pleased with the results. The reactions we have received from the professionals in the GP community have been much more than we had hoped for. We hope you enjoy it.

Frank Francone

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10 of 11 people found the following review helpful:
5.0 out of 5 stars Excellent, comprehensive and easy to read., January 28, 2002
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This review is from: Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence) (Hardcover)
We all know that kind of books where the author likes to show how much he knows making things intentionally complex....well...this is the opposite side of the spectrum.
The book is very complete and detailed yet easy to read, even after a day of work.
The first part of the book contains introductory information on background areas like probability, biology and computer science as a general discipline.
Getting into the topic, it clarifies some of the differences between evolutionary systems and genetic algorithms and shows how all this contributes to the theory of genetic programming and the evolution of computer programs.
It explains how things are done with different types of individuals (tree, linear, graph, etc) and gives valuable insight about the implementation process.
Although you may need other sources for formal treatment of some topics, this book is a very good acquisition.
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Inside This Book (learn more)
First Sentence:
Automatic programming will be one of the most important areas of computer science research over the next twenty years. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
brood recombination, intron equivalents, biological introns, improving crossover, destructive crossover, other machine learning systems, trait mining, chicken crossover, replicase experiments, intron growth, macromutation operator, neutral crossover, biological crossover, parsimony pressure, defined introns, fitness cases, effective fitness, using genetic programming, indexed memory, genetic programming system, homologous exchange, variable length structures, subtree crossover, absolute complexity, homologous crossover
Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, The Foundation, Dick Tracy, Morgan Kaufmann, Further Reading, Implementations Using, Flavors of Evolutionary Algorithms, New Directions, San Francisco, San Mateo, Empirical Evidence of Crossover Effects, Executable Program Structures, Implementations With Arrays, Parallel Problem Solving, Representing the Problem, Some Necessary Data Structures, Consider Figure, John Wiley, Other Genomes, Oxford University Press, The Effects of Introns, The Emergence of Introns, The Primitives of Genetic Programs, Lecture Notes, Minimalist Evolution
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