Genetic Algorithms in Search, Optimization, and Machine Learning (Hardcover)

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Editorial Reviews

Amazon.com Review

David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.

Product Description

This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required. 0201157675B07092001

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Product Details

• Hardcover: 432 pages
• Publisher: Addison-Wesley Professional; 1 edition (January 11, 1989)
• Language: English
• ISBN-10: 0201157675
• ISBN-13: 978-0201157673
• Product Dimensions: 9.3 x 7.6 x 0.9 inches
• Shipping Weight: 1.9 pounds (View shipping rates and policies)
• Average Customer Review: 4.4 out of 5 stars  See all reviews (19 customer reviews)
• Amazon.com Sales Rank: #240,005 in Books (See Bestsellers in Books)

  Popular in these categories: (What's this?)

  #4 in	Books > Computers & Internet > Programming > Algorithms > Genetic
  #28 in	Books > Computers & Internet > Computer Science > Artificial Intelligence > Machine Learning
  #50 in	Books > Computers & Internet > Computer Science > Artificial Intelligence > Theory of Computing

Customer Reviews

Most Helpful Customer Reviews

34 of 37 people found the following review helpful:

5.0 out of 5 stars Great introduction to the field, August 15, 1999

By	Robert D. C. Shearer (USA) - See all my reviews (REAL NAME)

One seldom finds a book as well-written as this one. The underlying mathematics are explained in a very accessible manner, yet with enough rigor to fully explain the "partial schemata" theory which is so important to understanding when and where GenAlgs can be applied. It is the lack of coverage of this theory which causes so much misunderstanding and disappointment in the power of genetic algorithms.

But beyond the background math (which makes up a small part of the book) this is really a tutorial on implementing GenAlgs, and it is an excellent one. The sample code is great, and the implementations are developed throughout the book, allowing the reader to implement simple (but functional) algorithms after reading only the first few chapters, but building to very sophisticated and modern techniques by the end of the book.

A great find.

6 of 6 people found the following review helpful:

5.0 out of 5 stars The definitive introduction to genetic algorithms, September 26, 1996

By A Customer

More than seven years after publication, David Goldberg's clear prose, straightforward code examples, and solid theoretical coverage keeps "the blue book" head-and-shoulders above any other text on this most intriguing of algorithmic directions. This is the book that lifted genetic algorithms from obscurity to one of the most discussed (and misunderstood) of emerging technologies. Goldberg did not invent genetic algorithms (that honor goes to either Nature or John Holland, depending on your personal belief system), but he did make sure that they could be understood by any interested programmer. The source code is in Pascal, which may not be to everyone's taste, but is certainly readable by anyone with a programming background. - Larry O'Brien (Editor, AI Expert Magazine 1990-1994

46 of 61 people found the following review helpful:

2.0 out of 5 stars Could be cut down to a third without loosing information, June 1, 2002

By	Jacob Marner (Denmark) - See all my reviews (REAL NAME)

This is the only book I have read about Genetic algorithms, but it seems that it covers the field pretty well.

In the preface it says that it is aimed a beginning graduate students, and since I have a M.Sc. in Computer Science and I just wanted to read it for fun I thought it would be for me. But I found that it uses way to many words to explain very basic things (e.g. almost a page to explain binary numbers) while many of the difficult equations just was presented without proper proof. So the book could have better if it had been cut down to a third and then supplemented with the proper proofs. So if you are a Computer Science graduate I cannot recommend this book. Given the fine books that Addison-Wesley usually publish I was quite disappointed with this one.

But if you are a student in other fields and just want an "intuitive" impression of Genetic Algorithms without the mathematical rigor it is probably good.

Chapter 1: An introduction to genetic algorithms with examples. This chapter is excellent.
Chapter 2: The mathematical theory behind genetic algorithms. This is not done very well since many of the equations isn't proven or explained properly.
Chapter 3: A Pascal program for the sample in chapter 1. This seems unneccesary since any proficient programmer easily could have implemented the program based on the information in chapter 1.
Chapter 4: The history of genetic algorithms and a number of applications all taken from research. Both seem unneccesary.
Chapter 5: An extension of the techniques presented in chapter 1. This is good.
Chapter 6-7: Introduction to machine learning. Is ok.
Chapter 8: A concluding chapter without any real new information.