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An Introduction to Genetic Algorithms (Complex Adaptive Systems) Reprint Edition

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Melanie Mitchell (Author)
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Melanie Mitchell (Author)
4 out of 5 stars 20 customer reviews
ISBN-13: 978-0262631853
ISBN-10: 0262631857
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Product Details

  • Series: Complex Adaptive Systems
  • Paperback: 221 pages
  • Publisher: A Bradford Book; Reprint edition (February 6, 1998)
  • Language: English
  • ISBN-10: 0262631857
  • ISBN-13: 978-0262631853
  • Product Dimensions: 6.8 x 0.5 x 9.9 inches
  • Shipping Weight: 1.1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (20 customer reviews)
  • Amazon Best Sellers Rank: #199,065 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

88 of 90 people found the following review helpful
Good introduction for such a short book
By Dr. Lee D. Carlson HALL OF FAMEVINE VOICE on April 6, 2002
Format: Paperback
Although short, this book gives a good introduction to genetic algorithms for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. It was first published in 1995, and considerable work has been done in genetic algorithms since then, but it could still serve as an adequate introduction. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets at the end of each chapter, these being divided up into "thought exercises" and "computer exercises", and in the latter she includes some challenge problems for the ambitious reader.
Chapter 1 is an overview of the main properties of genetic algorithms, along with a brief discussion of their history. The role of fitness landscapes and fitness functions is clearly outlined, and the author defines genetic algorithms as methods for searching fitness landscapes for highly fit strings. An elementary example of a genetic algorithm is given, and the author compares genetic algorithms with more traditional search methods. The author emphasizes the unique features of genetic algorithms that distinguish them from other search algorithms, namely the roles of parallel population-based search with stochastic selection of individuals, and crossover and mutation. A list of applications is given, and two explicit examples of applications are given that deal with the Prisoner's Dilemna and sorting networks. The author also gives a brief discussion as to how genetic algorithms work from a more mathematical standpoint, emphasizing the role of Holland schemas.Read more ›
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33 of 35 people found the following review helpful
Brief and to the Point
By Chris McKinstry on February 20, 2000
Format: Paperback
This book is brief and to the point. You won't find here pages of source code that you could have easily ftp'd yourself. What you will find is solid theory in a mere 224 pages. This is the quickest and best way to get up to speed on GA's there is. Which is why it is a standard textbook in the field.
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21 of 21 people found the following review helpful
Excellent book
By Mark on June 15, 2000
Format: Paperback
This is an excellent introductory book on genetic algorithms. It's very concisely written and there are a ton of interesting projects and programs to do. I've done a few of them myself and learned a lot. This book is one of those that I keep going back to and I always find some new idea or thing to try out.

If you're a programmer and have been thinking of getting into genetic algorithms, you won't go wrong with this book. Very highly recommended.
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34 of 38 people found the following review helpful
Not for beginners
By John Dalesandro on February 4, 2004
Format: Paperback Verified Purchase
I have an engineering degree, and I found this to be a little tough to follow for two reasons:
1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.
2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.
When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!
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12 of 12 people found the following review helpful
An introduction and much more
By dean_from_sa on January 25, 2004
Format: Paperback
First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.
Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.
The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.
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8 of 8 people found the following review helpful
Great introduction for the uninitiated!
By A Customer on August 13, 1998
Format: Hardcover
This book is ideal for someone totally new to the field of GAs. Mitchell begins with the fundamental concepts of the simple GA and proceeds to survey a wide variety of applications. I especially enjoyed the coverage of topics related to machine intelligence, which are sometimes left out in books that focus solely on optimization. The book contains enough information for someone with programming experience to code their own GA (including suggested computer exercises), although no source code is presented. However, the background gained from reading Mitchell's book will enable an easier read of more technical books (which may include source code implementations).
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Most Recent Customer Reviews

Five Stars

Great.

Published 14 months ago by HMH
Looking for a Good Chromosome

Melanie Mitchell’s book “an introduction to Genetic Algorithms” explains what Genetic Algorithms are and how they work. It is somewhat outdated by now. Read more

Published 15 months ago by Thomas Wikman
Don't buy this book...

This book is NOT an "Introduction" to Genetic Algorithms. It is an academia blathering about genetic algorithms citing mostly other people's work with no practical examples. Read more

Published on November 3, 2011 by DvG
Introduction ... for Researchers Maybe

I have to agree with all of johnnied7 criticisms. This book is pitched at a level too advanced for an introduction. It also reads and is structured like a research paper. Read more

Published on May 29, 2008 by wooks
Good Theoretical GA Textbook

This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. Read more

Published on May 5, 2005 by JM Gustafson
A Great Introduction to Genetic Algorithms

This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. Read more

Published on December 6, 2002 by Brian K. Schmidt
Good Overview

I found this book to cover many of the aspects of GAs. Had I not read David Goldberg's work before hand, however, I wouldn't have been able to put it to use (I'm using GAs for my... Read more

Published on January 4, 2002 by CWD
Mad Scientists everywhere, repeat after me, "IT'S ALIVE!!"

I used this book to host a "brown bag" discussion group at my company a year or so ago. Like everyone else's review, I have to say this is a really clear and concise... Read more

Published on January 3, 2002 by Necron2.0
Start here to program your own GA

Everybody refers to this as the best general book on genetic algorithms written to date. It's definitely a great place to start if you know nothing, as I did. Read more

Published on December 21, 2001 by Brint Montgomery
Great Introductory Book

This book provides solid background in general GA principles and theory. In addition, the author does a good job of pointing out many interesting topics of future research. Read more

Published on August 21, 2000

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