Download PDF Genetic Algorithms in Search, Optimization, and Machine Learning
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Genetic Algorithms in Search, Optimization, and Machine Learning
Download PDF Genetic Algorithms in Search, Optimization, and Machine Learning
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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.
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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
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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:
7.8 x 0.8 x 9.5 inches
Shipping Weight: 1.9 pounds
Average Customer Review:
3.9 out of 5 stars
27 customer reviews
Amazon Best Sellers Rank:
#807,577 in Books (See Top 100 in Books)
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!
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.
Good book, explains many algorithms in more plain, easier to understand English than the Introduction to Algorithms by Cormen, Lieserson, Rivest, and Stein. Although it is different in the amount and focus of material. Both books used in conjunction is helpful.
This book absolutely delivers more than I ever wanted to know about genetic algorithms. Worth it just for the first few chapters.
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.
We are using this book as a text in our Computer Science II course. Unfortunately, this is a somewhat difficult book to try and learn from. The descriptions are very dense, filled with many proofs, and little in the way of an explanation as to what an algorithm does, or what it's purpose is. In other words, rather than simply stating what an algorithm is used for and the general ideas behind it in a couple clear paragraphs, it spreads this information out between unclear and hard to follow proofs (that skip over important steps), using difficult language, and between multiple "failed attempts" which try to show how to arrive at the optimal way of implementing an algorithm without clearly stating so.I want to state now that I spend a lot of time reading dense texts from other computer science books, to Microsoft documentation, to even some Wikipedia articles. While I can bear through this book, it is one of the more difficult things I've had to deal with. In my opinion, its worse than the level of perceived greatness from teachers, and practical uselessness to students as is found in many math books that can prove everything and explain almost nothing.However, I have not had a chance to check out any other computer science texts, so perhaps as the last reviewer stated, this *IS* one of the better ones...If you're a professor, before choosing this book, or any other really, try to see if it at least has clear explanations in addition to whatever else one might want. Because, as a student who often relies on the textbook for learning things, this one is near impossible.
THANK YOU
I' satified;I' knew this book in your sitIt arrived at many time agoItis a interesting and serious book
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