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Genetic Algorithms in Search, Optimization and Machine Learning 1st Edition

by David E. Goldberg (Author)
4.3 4.3 out of 5 stars 37 ratings
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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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  1. ISBN-10
    0201157675
  2. ISBN-13
    978-0201157673
  3. Edition
    1st
  4. Publisher
    Addison-Wesley Professional
  5. Publication date
    January 1, 1989
  6. Language
    English
  7. Dimensions
    7.75 x 1 x 9.75 inches
  8. Print length
    412 pages
  9. See all details
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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.

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

  • Publisher ‏ : ‎ Addison-Wesley Professional; 1st edition (January 1, 1989)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 412 pages
  • ISBN-10 ‏ : ‎ 0201157675
  • ISBN-13 ‏ : ‎ 978-0201157673
  • Item Weight ‏ : ‎ 1.92 pounds
  • Dimensions ‏ : ‎ 7.75 x 1 x 9.75 inches
  • Customer Reviews:
    4.3 4.3 out of 5 stars 37 ratings

About the authors

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  1. David E. Goldberg

    David E. Goldberg

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  2. David E. Goldberg

    David E. Goldberg

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    DAVID E. GOLDBERG is president of Big Beacon, a nonprofit organization founded as a movement for the transformation of engineering education. He is known as an author, educator, entrepreneur, and artificial intelligence researcher. Author of the widely cited bestseller Genetic Algorithms in Search, Optimization, and Machine Learning and co-founder of ShareThis, in 2007 he co-founded the Illinois Foundry for Innovation in Engineering Education (iFoundry). In 2010 he resigned his tenure and professorship at the University of Illinois to work full time for the transformation of engineering education. As a movement leader, leadership coach, and change management consultant, Dave now works with individuals, organizations, and networks around the world to collaboratively disrupt the status quo.

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Customer reviews

4.3 out of 5 stars
4.3 out of 5
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37 global ratings

Top reviews from the United States

Matthew J. Faulkner
5.0 out of 5 stars Provided me with the elements of a solution
Reviewed in the United States on July 21, 2003
Verified Purchase
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!
11 people found this helpful
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Joey The Tap Water Buffalo
5.0 out of 5 stars This book absolutely delivers more than I ever wanted to ...
Reviewed in the United States on October 8, 2015
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This book absolutely delivers more than I ever wanted to know about genetic algorithms. Worth it just for the first few chapters.
2 people found this helpful
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Malodorous
4.0 out of 5 stars Old-school book
Reviewed in the United States on May 19, 2013
Verified Purchase
I took an AI class and bought this. The professor is very old-school and still uses overhead projectors and hands out paper notes instead of something like PDF.

The book is definitely dated here in 2013, but the ideas presented therein are valid. I would look elsewhere for a modern genetic algorithms book, though. Unless your professor is old-school and has textbooks older than you are.

The code examples are largely irrelevant: nobody uses Pascal anymore, not even for teaching. So if you want to play along and run the code you either need to locate an old 386 and CRT monitor, or translate the code into something that actually runs in this century.
10 people found this helpful
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Wagner F. Sacco
3.0 out of 5 stars Needs updating
Reviewed in the United States on September 3, 2004
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.
3 people found this helpful
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Severino P. Santos
5.0 out of 5 stars genetic Algorithms
Reviewed in the United States on January 29, 2013
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I' satified;
I' knew this book in your sit
It arrived at many time ago
Itis a interesting and serious book
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Dr. Tom
2.0 out of 5 stars Read a review article instead!
Reviewed in the United States on November 5, 2004
Verified Purchase
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.
13 people found this helpful
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SeriouslySatisfied
5.0 out of 5 stars TY
Reviewed in the United States on October 8, 2014
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THANK YOU
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Joao
5.0 out of 5 stars This is a very good introduction book
Reviewed in the United States on December 6, 2017
This is a very good introduction book. If you are starting in Genetic Algorithms this is the book. Afterwards there are many others...
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Norliza Abdullah
5.0 out of 5 stars Five Stars
Reviewed in the United Kingdom on November 8, 2016
Verified Purchase
Recommended. Fast shipping and I really need one for my study.
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cosmicsurfer
5.0 out of 5 stars わかりやすく、実用的
Reviewed in Japan on December 25, 2003
Verified Purchase
初心者にとってもわかりやすく、それでいて十分詳しく書かれた本です。遺伝的アルゴリズムに関する本は、特に数学的な考察に関する記述が難しく、その割に歯切れの悪い本が多いのですが、この本に関しては特にスキーマの説明など、驚くほど明快に説明されています。
著者はミシガン大学大学院で土木工学を専攻していたときに、遺伝的アルゴリズム（ＧＡ）の考案者である Hollandのもとに押しかけて博士論文の共同指導教官になってくれるように直訴したところからこの分野に入り、今や ＧＡの機械学習への応用に関しては第一人者なのだそうです。
5 people found this helpful
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