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Genetic Algorithms in Search, Optimization, and Machine Learning, by David E. Goldberg
PDF Download Genetic Algorithms in Search, Optimization, and Machine Learning, by David E. Goldberg
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This book describes the theory, operation, and application of genetic algorithms-search algorithms based on the mechanics of natural selection and genetics.
- Sales Rank: #211355 in Books
- Published on: 1989-01-11
- Original language: English
- Number of items: 1
- Dimensions: 9.53" h x .81" w x 7.75" l, 1.82 pounds
- Binding: Hardcover
- 432 pages
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
About the Author
DAVID E. GOLDBERG is Jerry S. Dobrovolny Distinguished Professor in Entrepreneurial Engineering at the University of Illinois at Urbana-Champaign, where he also serves as the Director of the Illinois Genetic Algorithms Laboratory. He received his BSE, MSE, and PhD, all in civil engineering, from the University of Michigan. From 1976 to 1980 he held a number of positions at Stoner Associates of Carlisle, Pennsylvania, including project engineer and marketing manager.?In 2004, Dr. Goldberg cofounded Nextumi, Inc. (www.nextumi.com), a Web-infrastructure firm, and now serves as Nextumi's Chief Scientist.
Most helpful customer reviews
44 of 46 people found the following review helpful.
Great introduction to the field
By Rob Shearer
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.
16 of 17 people found the following review helpful.
Not the only paradigm for evolutionary computation
By Todd Ebert
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!
10 of 10 people found the following review helpful.
Provided me with the elements of a solution
By Matthew J. Faulkner
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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