Customer Reviews

Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence)

5 star:		(8)
4 star:		(0)
3 star:		(1)
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1 star:		(1)

 

 

This book is a great introduction to genetic programming and should be a model for textbook authors in other fields. Knowing little about genetic programming to begin with, this book guides the reader through the various topics and problems associated with genetic programming in a very logical and understandable way. Highly recommended! I wish more technical books were like this!

I am one of the authors, so I will let others do the reviewing. We have spent some time trying to get Amazon.com to remove the "editor" title from Dr. Banzhaf's name because this is NOT an edited volume. It is an entirely original work.

Alas, to no avail.

We spent three years on this book and are very pleased with the results. The reactions we have received from the professionals in the GP community have been much more than we had hoped for. We hope you enjoy it.

Frank Francone

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.

It may be the rich subject matter, but this is the first time I've been happy to pay this much for a textbook. It's also the first textbook I've ever actually read through.

It's a well written introduction to a very cool field of study. Now I wish I had gone straight into computer science after I graduated biology in 1993. Even though I'm not doing any work with GP or machine learning, this book lets me feel like an insider. Who knows, maybe it'll mark the start of a new career.

I particularly liked the fact that the content is all very current and very relevant (for 1998). The book provides a good starting point for getting into the scientific papers.

Anyone coming from first year biology or computer science should have no trouble picking up on the major themes here.

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.

I have no idea how this is marketed as a college level text on the subject. It is just a 'high level' text suitable for non programmers interested in learning some of the terminology regarding Genetic Programming, with little or no practical information. This book was published in 1998, there are many free texts with far more practical information. I bought A Field Guide to Genetic Programming, which is also available as a free pdf, with this book, and the Field Guide is a far far better book on all counts.

The authors are to be congratulated on presenting a volume which:

- factually and clearly elucidates the relationship between GP and machine learning, or biology.

- places GP in the perspective of Evolutionary Algorithms.

The volume details the research issues of GP and reviews in a thorough and unbiased manner the current state of the art. It serves well as an introduction and, for the more dedicated reader, it introduces advanced topics such as representation options, optimizing GP performance and the implementation of a practical GP system.

The book also contains a useful source of references to printed and electronic materials and internet information regarding GP.

I recommend it highly. Read it and enjoy!

I skimmed the Koza books (GP: I & II) and this one at the store. Using the layout, chapter names, and the introductory chapters as my guide, I decided to buy this book to introduce me to the current state of the art in GP. The strengths of this book are its textbook format and the informal exercises that are presented for the reader at the end of every chapter. There is also a great deal of compilation from other relevant gp works presented in a localized, intra-chapter basis. The book is thus highly digestable to a newcomer, and is a far less time-consuming way to learn about GP than through the "expert" papers on the web. Having now almost finished the book, I feel that I am ready and able to author and apply GP techniques in a wide variety of applications and languages, having spent less than 20 hours in study time. A terrific achievement by Banzhaf and company, highly recommended.

Genetic Programming is an exciting field, and as the desktop computers become more powerful, more applications are occuring. This book prepares both the practitioner and researcher for this field. It covers much material that is needed not only to deploy GP, but to explain it to management and customers. The book also comes with the web address for sample demonstration software that I found easy to learn, and deploy on a project that had previously been analyzed via neural networks.

I recommend this book highly, as well as works by Dr. Koza and Dr. Bentley for anyone interested in working in this field.

It's rare to find an advanced computer science textbook that's both so engaging and so informative. I've only read the first seven chapters so far, but when I sat down to write my first genetic algorithm (for real research use), the book had already prepared me well.

It's hard to imagine a better introductory textbook for this topic.