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Genetic Algorithms in Search, Optimization, and Machine Learning | 
 enlarge | Author: David E. Goldberg
Publisher: Addison-Wesley Professional
Category: Book
List Price: $69.99
Buy New: $39.98
You Save: $30.01 (43%)
New (18) Used (16) from $25.00
Rating:  19 reviews
Sales Rank: 35797
Media: Hardcover
Edition: 1
Pages: 432
Number Of Items: 1
Shipping Weight (lbs): 1.9
Dimensions (in): 9.3 x 7.6 x 0.9
ISBN: 0201157675
Dewey Decimal Number: 006.31
UPC: 785342157673
EAN: 9780201157673
Publication Date: January 11, 1989
Availability: Usually ships in 1-2 business days
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| Editorial Reviews:
Amazon.com
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.
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| Customer Reviews: Read 14 more reviews...
 Great introduction to the field August 16, 1999
Robert D. C. Shearer (USA)
38 out of 43 found this review helpful
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.
Explains *and* entertains April 24, 1999
Kate Sherwood (Palo Alto, CA United States)
9 out of 15 found this review helpful
I bought this book while I was a working professional. It is one of the few textbooks that I have ever read straight through, like a novel. In addition to making everything clear and interesting, the book was even funny at times! I didn't think that was allowed in textbooks. ;-)
a classic textbook January 1, 2000
De Paoli Andrea (Rome Italy)
4 out of 10 found this review helpful
The examples and code was extremely helpful in clarifying the ideas presented in the text. The treatment I think should appeal to beginners (with some computing experience however) and certainly a pleasure for those advanced programmers who want to learn more about genetic algorithms.
The Best Book in AI so far July 13, 2004
Edwin W. Meier (Springfield, OH)
3 out of 4 found this review helpful
This book got me so excited that I was not able to continue reading. I had to put it down and walk about. The power of the learning classifier system (SCS) has yet to be fully explored. A system that organizes data (classifies) and learns new rules (generate new rules via the genetic algorithm) is a combination that still takes my breath away. The only negative to this book are the trivial problems the algorithms solve. There is none for the "bucket brigade" version of the SCS. Overall though it is an awesome book presenting a very powerful algorithm that has yet to be fully explored.
Provided me with the elements of a solution July 22, 2003
Matthew Faulkner (Oakland, CA United States)
5 out of 5 found this review helpful
I was looking for an automated approach to finding an optimum run sequence through a changeover matrix. The programming examples gave me the elements I needed to experiment and then fine tune the approach for a working search algorithm. I found the book a good companion in my "voyage of discovery".For me, the book works two levels, the basic pieces to "play with" are presented clearly in chapters 1 and 3, and practical implementation suggestions are spread throughout the text. By developing programs in Visual Basic, experimenting with search parameters and re-reading sections of this book - I learned something new!
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