Customer Reviews

Practical Genetic Algorithms

5 star:		(6)
4 star:		(2)
3 star:		(1)
2 star:		(1)
1 star:		(1)

 

 

This book is a good one for beginning GA programmers to start with. It provides sufficient detail for implementing both binary and continuous GAs, and compares their performance throughout the book. The applications described are diverse and interesting, although some of the engineering applications were hard for me to follow. In short, I found this book interesting and full of good ideas to experiment with.

I found this book to be an extremely clear and easy to understand introduction to genetic algorithms. It was particularly helpful to see examples of both binary and continuous versions of genetic algorithms, and to see explanations of examples on both elementary and advanced levels. This is a good book to read before buying one of the more comprehensive, rigorous technical books on the subject.

Presents non-standard techniques without pointing out the standard ones. The non-standard techniques were recommended strongly based only on author's personal opinions, without comparison to other standard techniques on broad spectrum.

For starters, it is much better to look into "An Introduction to Genetic Algorithms" by Melanie Michell.

This is a very simple introductory book to read on genetic algorithm. It provides a good overview of the main mechanisms involved. There is no theoretical treatment of the subject. The Pesudcodes provided in the appendixes have some mistakes. I recommend it for people who want quick general outline of what genetic algorithms means and how to apply it.

In my opinion to well understand a process/method you have to follow an example in every little detail. This book does exactly this and once read allows to write your own code easily. I highly recommend this book!

From the word "practical" in the title, I expected more concrete examples of software source code. There are no such examples in this book. In particular, Appendix A starts with, "We don't provide computer code in this book". (Thanks for waiting until Appendix A to tell me!)

Otherwise, the book is well written and an easy read. It's just more theoretical and abstract than I expected.

This is a really good 'hands on' introduction to genetic algorithms. Mitchell gives a great introduction as well, but this book gets into the 'practical' side of it.

Haupt^2's book starts off with an introduction. The ideas behind optimization are covered briefly (~20pgs). The book then proceeds into the ideas behind binary genetics (also introducing the evolutionary ideas ~25 pgs). The book then goes into developing the code itself, while describing the trade offs. Very good examples under applications are given (both basic and advanced ~40pgs). More sophisticated techniques are developed, with a wrap up on current trends in GA.

A great intro book (undergrad *even high school*), and a friend to the working scientist/engineer. This is a book that'll help get the job done.

This book is well written, with good examples and insights. However, I think that there should be many more examples and theory to warrent the price of this book. Therefore, better take this book from a library or wait for a softcover.

The text and accompanying CD can get you usefully started in understanding and manipulating genetic algorithms. Despite what the back of the book says, there is still a fair amount of maths background you need. Especially in such things as predator-prey modelling and the coupled differential equations that arise in such efforts.

The book also shows a natural fit between GAs and neural networks. One passage discusses how to optimise a net with feedbacks from a GAs. Along the way, the book also gives an exposure to various biological ideas. Like how artificial neural nets are inspired by actual biological neurons.

The authors have thoughtfully included exercises with each chapter, for you to extend the ideas for yourself. The book can be used as a university textbook for a graduate course.

Before I read this book, I had heard about GA and had an idea about how it works, but had no clue to actually coding it. One day I bought this book and spent a day to read it. On the following day, I was able to write my own GA driver code!