Genetic Algorithms in Search, Optimization, and Machine Learning [Hardcover]

David E. Goldberg (Author)

Book Description

Publication Date: January 11, 1989 | ISBN-10: 0201157675 | ISBN-13: 978-0201157673 | Edition: 1

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

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.

From the Back Cover

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

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: 9.5 x 7.7 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 Best Sellers Rank: #161,009 in Books (See Top 100 in Books)
  • #1 in Books > Computers & Technology > Programming > Algorithms > Genetic
  • #22 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Machine Learning

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Customer Reviews

Most Helpful Customer Reviews

37 of 39 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)   

This review is from: Genetic Algorithms in Search, Optimization, and Machine Learning (Hardcover)

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.

9 of 9 people found the following review helpful:

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

By A Customer

This review is from: Genetic Algorithms in Search, Optimization, and Machine Learning (Hardcover)

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

8 of 8 people found the following review helpful:

4.0 out of 5 stars Not the only paradigm for evolutionary computation, July 19, 2005

By 

Todd Ebert (Long Beach California) - See all my reviews

This review is from: Genetic Algorithms in Search, Optimization, and Machine Learning (Hardcover)

This book gives a good introduction to genetic algorithms for a general undergraduate audience. However, it is important to note that it does not cover Evolutionary Strategies, an approach to evolutionary computing that I have found quite useful since it is specifically designed for Euclidean space optimization problems where many if not most interesting optimization problems are formulated in (take for example the problem of determining the weights of a neural network that minimizes the network's overall classification error). Nor does it cover evolutionary programming (not to be confused with genetic programming). So after reading this book, I recommend (for the mathematically adventurous) Thomas Back's "Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms"
ISBN: 0195099710

Happy reading and enjoy the fascinating world of evolutionary computation!