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 | | Practical Genetic Algorithms by Randy L. Haupt, Sue Ellen Haupt ISBN-10: 9780471188735 ISBN-10: 0-471-18873-5 ISBN-13: 9780471188735 ISBN-13: 978-0-471-18873-5 Hardcover 1997-12-19 Wiley-Interscience Find Lowest Price |
Editorials | ||
Product Description A tutorial on genetic algorithms with an emphasis on practical applications The rapidly expanding field of genetic algorithms has given rise to many new applications in a variety of disciplines. However, most of the existing books on the subject concentrate on theory. Practical Genetic Algorithms is the first introductory-level book to emphasize practical applications through the use of example problems. In an accessible style, the authors explain why the genetic algorithm is superior in many real-world applications, cover continuous parameter genetic algorithms, and provide in-depth trade-off analysis of genetic algorithm parameter selection. Written for the end user in engineering, science, and computer programming, as well as upper-level undergraduate and graduate students, Practical Genetic Algorithms: * Provides numerous practical example problems * Contains over 80 illustrations * Features many figures and tables * Includes three appendices: a glossary of terms, a list of genetic algorithm routines in pseudocode, and a list of symbols used in the book. | ||
Reviews | ||
An Excellent book-for learning GA theory as well as programming Based on the literature I have explored, I can unequivocally say that this is the best book that I have found on GA theory and programming. In a simple but effective manner, the book explains the intricate concepts. For any one thinking of learning GA theory this is a good starting point. Also, if you want to write programs to create your own genetic algorithms, this is a must-read. In my humble opinion, this is 'THE BEST' book particularly for those without much GA experience. I hereby express my heartfelt gratitude to the authors and congratulate them on their effort. | ||
easy introduction 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. | ||
Good, though not good value for money 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. | ||
Not a good place to start 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. | ||
Great 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! | ||