Rating: 
Summary: Good as an overall, not for the details
Review: This book is good for getting a general view of genetic programming. Nevertheless, I think it neglects many details. For example, it is very hard to from the book how a simple selection strategy (tournament selection) works in practice.I do not think this book is useful for someone intending to code a genetic programming algorithm.
Rating: 
Summary: Excellent, comprehensive and easy to read.
Review: We all know that kind of books where the author likes to show how much he knows making things intentionally complex....well...this is the opposite side of the spectrum.
The book is very complete and detailed yet easy to read, even after a day of work.
The first part of the book contains introductory information on background areas like probability, biology and computer science as a general discipline.
Getting into the topic, it clarifies some of the differences between evolutionary systems and genetic algorithms and shows how all this contributes to the theory of genetic programming and the evolution of computer programs.
It explains how things are done with different types of individuals (tree, linear, graph, etc) and gives valuable insight about the implementation process.
Although you may need other sources for formal treatment of some topics, this book is a very good acquisition.
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