Message from Dean - May 8th 2007
I am currently testing out a new version of the APF Bridge Component - If you notice any errors within this demo store please drop me a line.
List Price: $69.99Amazon.com's Price: $55.99 You Save: $14.00 (20%)Prices subject to change.
Availability: Usually ships in 24 hours
This item ships for FREE with Super Saver Shipping.
Binding: Hardcover
Dewey Decimal Number: 006.31
EAN: 9780201157673
Edition: 1
ISBN: 0201157675
Label: Addison-Wesley Professional
Manufacturer: Addison-Wesley Professional
Number Of Items: 1
Number Of Pages: 432
Publication Date: January 11, 1989
Publisher: Addison-Wesley Professional
Studio: Addison-Wesley Professional
Related Items:
Browse for similar items by category:
Editorial Review:
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
Average Rating: 
Rating:  -
This is a great book to begin your journey on Genetic Algorithms (GA). The author is a pioneering authority on the subject and has explained the basics of a GA in a very gentle and easy to understand manner. The book has a great variety of specific but diverse examples, which may not be useful at first glance, but gives an insight to where all the technique has been applied!
However, some aspects of the book perhaps need an edition, like the more recent advances in GA operators, specifics of chromosomal representation schemes, non-linear optimization functions, etc. I have read several, well written books on the subject, but this one has a very distinct and sometimes interesting style of writing! The best would be to quickly read this one to get a fairly good understanding of the basics and then take up a recent book that addresses other aspects like Mitchell's book, for example.
Having said that, I think the book is a great and inspiring start to using genetic ... Read More
Rating: -
Excellent book for Graduate students and instructors. Highly recommend!
Rating:  -
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!
Rating:  -
I agree with another reviewer who said the book was unnecessarily long. Genetic Algorithms are a great programming tool, and there are some tips and tricks that can help your programs converge faster and more accurately, but this book had a lot of redundant information.
If you are interested in using GA for solution-finding, I doubt you'll find much useful in this book beyond the first chapter or so. Many of the examples later in the book were so specific that I couldn't see how they could be usefully generalized. Really optimizing a GA approach for a specific problem domain takes a fair amount of tuning, and this book won't help much with that.
I think time spent surfing siteseer or other publication sites would be better spent than reading this book.
Rating:  -
OK, I agree with the previous reviewers: it's the classical textbook for GAs. But it definitely needs updating, as it's a 15-year old book and much has been done in the area. Niching methods, for example, are just outlined. I'd recommend Melanie Mitchell's book instead of this one.
Availability: Usually ships in 24 hours
|
Amazon Store 