Genetic Algorithms in Search, Optimization, and Machine Learning
Genetic Algorithms in Search, Optimization, 0201157675

Genetic Algorithms in Search, Optimization, and Machine Learning
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Addison-Wesley

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978020115767

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Genetic Algorithms in Search, Optimization, and Machine Learning Specs:
Product NameGenetic Algorithms in Search, Optimization, and Machine Learning
ManufacturerAddison-Wesley
Product Number MPN0201157675
Retail Price $64.99
UPC978020115767
Specifications 
TitleGenetic Algorithms in Search, Optimization, and Machine Learning
ISBN0201157675
Author(s)David E. Goldberg, David E. Goldberg
Release Date01 January, 1989, 1989-01-01
FormatHardcover
Num of Pages432
Num. of Items1
EAN9780201157673
Weight1.8 lbs.
Deal first added on:26-January-2004

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Computers Computer Bks - General Information discrete mathematics Artificial Intelligence - General Optimization (Mathematical Theory) Combinatorics Genetic algorithms Machine Learning
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Latest 6 Reviews
Here is what people are saying about the Genetic Algorithms in Search, Optimization, and Machine Learning
5 Star Rating  "Great start to your journey in Genetic Algorithms."2007-03-09
- Reviewed By User: A18GG84RF7J2B
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 algorithms.
 
5 Star Rating  "Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg"2006-07-06
- Reviewed By wilkiebj
Excellent book for Graduate students and instructors. Highly recommend!
 
4 Star Rating  "Not the only paradigm for evolutionary computation"2005-07-19
- Reviewed By some_nickname
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!


 
2 Star Rating  "Read a review article instead!"2004-11-05
- Reviewed By tomva
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.
 
3 Star Rating  "Needs updating"2004-09-03
- Reviewed By wfsacco
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.
 
5 Star Rating  "The Best Book in AI so far"2004-07-13
- Reviewed By edmreviewer
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.
 
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