Hardcover: 192 pages
Publisher: Wiley-Interscience; 1 edition (January 5, 1998)
Product Dimensions: 6.4 x 0.7 x 9.5 inches
Shipping Weight: 1.1 pounds
Average Customer Review: 4.0 out of 5 stars See all reviews (10 customer reviews)
Best Sellers Rank: #2,275,090 in Books (See Top 100 in Books) #34 in Books > Computers & Technology > Programming > Algorithms > Genetic #347 in Books > Science & Math > Mathematics > Applied > Linear Programming #768 in Books > Textbooks > Computer Science > Artificial Intelligence
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.
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.
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.
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.
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