Rating: 
Summary: Great introduction to the field
Review: 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.
Rating:
Summary: Great introduction to the field
Review: 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.
Rating:
Summary: a classic textbook
Review: The examples and code was extremely helpful in clarifying the ideas presented in the text. The treatment I think should appeal to beginners (with some computing experience however) and certainly a pleasure for those advanced programmers who want to learn more about genetic algorithms.
Rating:
Summary: The Best Book in AI so far
Review: 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.
Rating:
Summary: Introduce GA and its applications gradually and clearly.
Review: This book introduces GA from simple to advance.It gives you an overview of GA applications on search,optimization and machine learning.
Rating: 
Summary: An academic textbook
Review: This is an academic textbook rather than an industrial handbook, (which, as an engineer I prefer). The early chapters present the theory of how and why genetic algorithms work. While covering the theory of why GAs work and the various dos and don'ts relating to their application, there is little practical help in the book on how to implement a GA to solve real life problems. The textbook feel is continued by the presence of questions and programming tasks (without answers). The book includes many examples of problems solved with GAs, however no details are given of the implementation and the examples are presented mainly to describe the evolution of GAs. One the plus side, book includes the code, (in Pascal) for a Simple Genetic Algorithm, (SGA), and a Simple Classifier System, (SCS). The full code is presented in the appendices, but the key sections are developed and explained in the main body.
Rating:
Summary: I wish all books were like this !!
Review: This is one of the best books I've read for genetic algorithms and AI. I wish all books were like this. It is not pedagogical in its style (unlike Computational Intelligence - Engelbrecht), it is well written, very insightful and slowly takes you into the depths of GA/AI, so it's great to follow. This book contains source codes in Pascal (which is easy to translate to any other language - although you'd want to write your own based on OOP), pseudo codes, examples, and plenty of ways to understand the way GA's work. BUY THIS BOOK and you'll save yourself a lot of sweat and mind boggling wierd explanations from supposedly good authors. I'll never sell this book. One reader wrote a comment about how this book could be cut in half, and is not suitable for CS majors, my response to that: "I'm a CS major, doing my Ph.D., my professor, my colleagues and almost everyone in the field has a copy of this book, maybe you never got past chapter2 in his book. If you want proof of theorems, there's lots of research papers available, almost all of which refer to Goldberg's book."
Rating: 
Summary: Could be cut down to a third without loosing information
Review: 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.
Rating: 
Summary: I didn't like it
Review: 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
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