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)

Book Description

Publication Date: December 15, 1997 | ISBN-10: 155860510X | ISBN-13: 978-1558605107 | Edition: 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.

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

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Product Details

• Hardcover: 512 pages
• Publisher: Morgan Kaufmann; 1 edition (December 15, 1997)
• Language: English
• ISBN-10: 155860510X
• ISBN-13: 978-1558605107
• Product Dimensions: 7.6 x 1.2 x 9.6 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: #711,987 in Books (See Top 100 in Books)
  • #14 in Books > Computers & Technology > Programming > Algorithms > Genetic

Most Helpful Customer Reviews

31 of 38 people found the following review helpful

5.0 out of 5 stars Great Book. It is an original book -- not edited. May 2, 1998

By fdf

Format: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

12 of 13 people found the following review helpful

5.0 out of 5 stars A great introduction! November 18, 2000

By Paul Zimmons

Format: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!

10 of 11 people found the following review helpful

5.0 out of 5 stars Excellent, comprehensive and easy to read. January 28, 2002

By Steven Burns VINE™ VOICE

Format:Hardcover|Amazon Verified Purchase

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.

10 of 11 people found the following review helpful

5.0 out of 5 stars Excellent introduction to Genetic Programming June 23, 1998

By A Customer

Format:Hardcover

It may be the rich subject matter, but this is the first time I've been happy to pay this much for a textbook. It's also the first textbook I've ever actually read through.

It's a well written introduction to a very cool field of study. Now I wish I had gone straight into computer science after I graduated biology in 1993. Even though I'm not doing any work with GP or machine learning, this book lets me feel like an insider. Who knows, maybe it'll mark the start of a new career.

I particularly liked the fact that the content is all very current and very relevant (for 1998). The book provides a good starting point for getting into the scientific papers.

Anyone coming from first year biology or computer science should have no trouble picking up on the major themes here.

10 of 11 people found the following review helpful

3.0 out of 5 stars Good as an overall, not for the details May 10, 2003

By A Customer

Format:Hardcover

This book is good for getting a general view of genetic programming. Nevertheless, I think it neglects many details. For example, it is very hard to from the book how a simple selection strategy (tournament selection) works in practice.

I do not think this book is useful for someone intending to code a genetic programming algorithm.

8 of 9 people found the following review helpful

1.0 out of 5 stars Almost a popularization. August 16, 2008

By Justin Van Winkle

Format:Hardcover|Amazon Verified Purchase

I have no idea how this is marketed as a college level text on the subject. It is just a 'high level' text suitable for non programmers interested in learning some of the terminology regarding Genetic Programming, with little or no practical information. This book was published in 1998, there are many free texts with far more practical information. I bought A Field Guide to Genetic Programming, which is also available as a free pdf, with this book, and the Field Guide is a far far better book on all counts.

7 of 8 people found the following review helpful

5.0 out of 5 stars This book is well written and comprehensive. September 4, 1998

By Una-May O'Reilly ([e-mail])

Format:Hardcover

The authors are to be congratulated on presenting a volume which:

- factually and clearly elucidates the relationship between GP and machine learning, or biology.

- places GP in the perspective of Evolutionary Algorithms.

The volume details the research issues of GP and reviews in a thorough and unbiased manner the current state of the art. It serves well as an introduction and, for the more dedicated reader, it introduces advanced topics such as representation options, optimizing GP performance and the implementation of a practical GP system.

The book also contains a useful source of references to printed and electronic materials and internet information regarding GP.

I recommend it highly. Read it and enjoy!

6 of 7 people found the following review helpful

5.0 out of 5 stars terrific textbook April 17, 2003

By M. Joya

Format:Hardcover

I skimmed the Koza books (GP: I & II) and this one at the store. Using the layout, chapter names, and the introductory chapters as my guide, I decided to buy this book to introduce me to the current state of the art in GP. The strengths of this book are its textbook format and the informal exercises that are presented for the reader at the end of every chapter. There is also a great deal of compilation from other relevant gp works presented in a localized, intra-chapter basis. The book is thus highly digestable to a newcomer, and is a far less time-consuming way to learn about GP than through the "expert" papers on the web. Having now almost finished the book, I feel that I am ready and able to author and apply GP techniques in a wide variety of applications and languages, having spent less than 20 hours in study time. A terrific achievement by Banzhaf and company, highly recommended.