Genetic Algorithms in Search, Optimization, and Machine Learning

by David Edward Goldberg

Book Overview

This book describes the theory, operation, and application of genetic algorithms-search algorithms based on the mechanics of natural selection and genetics. This description may be from another edition of this product.

• Format: Hardcover
• Language: English
• ISBN: 0201157675
• ISBN13: 9780201157673
• Release Date: January 1989
• Publisher: Addison-Wesley Professional
• Length: 432 Pages
• Weight: 1.86 lbs.
• Dimensions: 0.9" x 7.7" x 9.5"

Customer Reviews

I wish all books were like this !!

This is one of the best books I've read for genetic algorithms and AI. I wish all books were like this. It is not pedagogical in its style (unlike Computational Intelligence - Engelbrecht), it is well written, very insightful and slowly takes you into the depths of GA/AI, so it's great to follow. This book contains source codes in Pascal (which is easy to translate to any other language - although you'd want to write your own based on OOP), pseudo codes, examples, and plenty of ways to understand the way GA's work. BUY THIS BOOK and you'll save yourself a lot of sweat and mind boggling wierd explanations from supposedly good authors. I'll never sell this book. One reader wrote a comment about how this book could be cut in half, and is not suitable for CS majors, my response to that: "I'm a CS major, doing my Ph.D., my professor, my colleagues and almost everyone in the field has a copy of this book, maybe you never got past chapter2 in his book. If you want proof of theorems, there's lots of research papers available, almost all of which refer to Goldberg's book."

Provided me with the elements of a solution

I was looking for an automated approach to finding an optimum run sequence through a changeover matrix. The programming examples gave me the elements I needed to experiment and then fine tune the approach for a working search algorithm. I found the book a good companion in my "voyage of discovery".For me, the book works two levels, the basic pieces to "play with" are presented clearly in chapters 1 and 3, and practical implementation suggestions are spread throughout the text.By developing programs in Visual Basic, experimenting with search parameters and re-reading sections of this book - I learned something new!

Great introduction to the field

One seldom finds a book as well-written as this one. The underlying mathematics are explained in a very accessible manner, yet with enough rigor to fully explain the "partial schemata" theory which is so important to understanding when and where GenAlgs can be applied. It is the lack of coverage of this theory which causes so much misunderstanding and disappointment in the power of genetic algorithms.But beyond the background math (which makes up a small part of the book) this is really a tutorial on implementing GenAlgs, and it is an excellent one. The sample code is great, and the implementations are developed throughout the book, allowing the reader to implement simple (but functional) algorithms after reading only the first few chapters, but building to very sophisticated and modern techniques by the end of the book.A great find.

Good introduction to GAs

A very good introduction to GAs. It shows the theoretical and mathematical foundations of GAs and explains the mechanics in a very simple and affordable way. Easy to read and understand even for newbies.

The definitive introduction to genetic algorithms

More than seven years after publication, David Goldberg's clear prose, straightforward code examples, and solid theoretical coverage keeps "the blue book" head-and-shoulders above any other text on this most intriguing of algorithmic directions. This is the book that lifted genetic algorithms from obscurity to one of the most discussed (and misunderstood) of emerging technologies. Goldberg did not invent genetic algorithms (that honor goes to either Nature or John Holland, depending on your personal belief system), but he did make sure that they could be understood by any interested programmer. The source code is in Pascal, which may not be to everyone's taste, but is certainly readable by anyone with a programming background. - Larry O'Brien (Editor, AI Expert Magazine 1990-1994