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Genetic Algorithms in Search, Optimization, and Machine Learning
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Author: David E. Goldberg
List Price: $59.99
Our Price: Click to see the latest and low price
ISBN: 0201157675
Publisher: Addison-Wesley Pub Co (01 January, 1989)
Edition: Hardcover
Sales Rank: 27,792
Average Customer Rating: 4.5 out of 5
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Customer ReviewsRating: 5 out of 5
Provided me with the elements of a solution
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!
Rating: 3 out of 5
I didn't like it
Well... The book is not bad but chapter III lacks clarity...
Chapter III is supposed to give mathematical insights into genetic algorithms. It starts by proving the schema theorem (which is OK) and then tries to cover the math related to GA's. This chapter is very difficult to follow. Unless you are familar with GA's and the math related to them this chapter is difficult to understand
Rating: 2 out of 5
Could be cut down to a third without loosing information
This is the only book I have read about Genetic algorithms, but it seems that it covers the field pretty well.In the preface it says that it is aimed a beginning graduate students, and since I have a M.Sc. in Computer Science and I just wanted to read it for fun I thought it would be for me. But I found that it uses way to many words to explain very basic things (e.g. almost a page to explain binary numbers) while many of the difficult equations just was presented without proper proof. So the book could have better if it had been cut down to a third and then supplemented with the proper proofs. So if you are a Computer Science graduate I cannot recommend this book. Given the fine books that Addison-Wesley usually publish I was quite disappointed with this one. But if you are a student in other fields and just want an "intuitive" impression of Genetic Algorithms without the mathematical rigor it is probably good. Chapter 1: An introduction to genetic algorithms with examples. This chapter is excellent.
Chapter 2: The mathematical theory behind genetic algorithms. This is not done very well since many of the equations isn't proven or explained properly.
Chapter 3: A Pascal program for the sample in chapter 1. This seems unneccesary since any proficient programmer easily could have implemented the program based on the information in chapter 1.
Chapter 4: The history of genetic algorithms and a number of applications all taken from research. Both seem unneccesary.
Chapter 5: An extension of the techniques presented in chapter 1. This is good.
Chapter 6-7: Introduction to machine learning. Is ok.
Chapter 8: A concluding chapter without any real new information.
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