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Rating: 
Summary: Good book
Review: ...This is one of the better treatments of problem solving withevolutionary algorithms. Based on Michalewicz new book -- How toSolve It: Modern Heuristics -- also good and more entertaining -- I think that the "genetic" algorithm that is described here is now more of an "evolutionary" algorithm, where you don't have to worry about binary strings and other hinderances to solving problems. If you want a decent introduction to how evolutionary algorithms work this is good. If you want a lot of theory, you won't find it here, but most of the theory hasn't been very useful anyway.
Rating: 
Summary: One of the best book on genetic algorithms
Review: A very good vision of the evolutionary optimisation techniques not only GA. As well there is an excellent chapter on constraints handling. Maybe it is not one of the easiest book on GA but it is definitely the most useful.
Rating: 
Summary: pretty bad
Review: I agree with the previous reviewer: books should be clear and get to the point. Forget about this one. Get Michalewicz and Fogel's "How to solve it" book. It is MUCH better than this one in all levels: it is better written and the content is more authorative and helpful to novices and experts.This book is supposed to be a textbook. Maybe that's why it sells so well. I guess I am lucky I didn't have to take a class with this thing.
Rating:
Summary: pretty bad
Review: I agree with the previous reviewer: books should be clear and get to the point. Forget about this one. Get Michalewicz and Fogel's "How to solve it" book. It is MUCH better than this one in all levels: it is better written and the content is more authorative and helpful to novices and experts. This book is supposed to be a textbook. Maybe that's why it sells so well. I guess I am lucky I didn't have to take a class with this thing.
Rating:
Summary: Not a good overview text of the subject.
Review: I used this book as the primary text for a graduate course on evolutionary computation. I was looking for a book that provided a good introduction to genetic algorithms and provided a wide cross-section of related algorithms and applications. At the time, this was the only book of its type on the market (other than Goldberg's book). The first couple of chapters (What is a GA, Why Does a GA Work) were pretty good, but then the strong focus of the remainder of the book on numerical optimization resulted in a loss of interest on the part of my students. Since then, I have had to re-organize the class to provide topics such as genetic programming, evolving neural systems, co-evolution, and artificial life -- none of which are covered adequately (if at all) in this book.
Rating:
Summary: it is one of the best books about genetic algorithms
Review: It is as good for people who now start learning about genetic algorithms as for experience users.It includes many problems with its solution using genetic algorithm and many results
Rating:
Summary: It offer a useful way for numerical optimization
Review: It is not a textbook for genetic algorithm but is useful to those whose research domain is numerical optimization which is widely appeared in engineering area. It is the only book describing a float-number based GA whose importance has not been noticed by many researchers. I think it is a natural way for problem of engineering optimization.
Rating:
Summary: Good introduction to the topic of evolutionary algorithms.
Review: Note that a genetic algorithm is different from a genetic algorithm(Holland is responsible for the latter, Michalwicz for the former). The text is meant as an introduction to successful strategies for implementing EA's. As such, the text doesn't really cover any other areas(save a brief introduction to genetic algorithms at the beginning). Good as a primer and textbook. Not meant as a handbook of applications.
Rating: 
Summary: Awful, unreadable book.
Review: This man needs to invest in a good editor. Many times I'd read through half a page or so, stop to think about it and then rephrase it into one or two sentences. Blobs of math appear to be thrown in with little justification, and the book isn't improved by them. But this book is not only unreadable, it's also not useful. It's more an overview of the area than anything else; it doesn't give adequate information about genetic programming or neural networks. It skims many areas in a close to incomprehensible fashion without covering any in what I would consider to be good detail. Finally, I'm not dim. I have a PhD myself and am used to ploughing through gibberish. But save your money and don't buy this book (Unless you have a wobbly table that needs fixing).
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