An Introduction to Genetic Algorithms

by Melanie Mitchell and Melanie Mitchell

Book Overview

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in...

• Format: Paperback
• Language: English
• ISBN: 0262631857
• ISBN13: 9780262631853
• Release Date: February 1998
• Publisher: MIT Press
• Length: 221 Pages
• Weight: 0.80 lbs.
• Dimensions: 0.5" x 7.0" x 9.9"
• Age Range: 18 years and up
• Grade Range: Postsecondary and higher

Customer Reviews

An introduction and much more

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.

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. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.

Excellent book

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.

Brief and to the Point

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.

Great introduction for the uninitiated!

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).