theory

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."

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.

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.