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

by Melanie Mitchell (Author)
4.0 4.0 out of 5 stars 38 ratings
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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 machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues.

The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

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  1. ISBN-10
    0262631857
  2. ISBN-13
    978-0262631853
  3. Edition
    Reprint
  4. Publisher
    MIT Press
  5. Publication date
    February 6, 1998
  6. Language
    English
  7. Dimensions
    9.94 x 6.98 x 0.52 inches
  8. Print length
    221 pages
  9. See all details
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Editorial Reviews

Review

An outstanding introduction to a new and important field of computer science.—Tim Watson, The Computer Journal—

This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing.

—Chris Robbins, Computing—

About the Author

Melanie Mitchell is Professor of Computer Science at Portland State University and External Professor at the Santa Fe Institute

Product details

  • Publisher ‏ : ‎ MIT Press; Reprint edition (February 6, 1998)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 221 pages
  • ISBN-10 ‏ : ‎ 0262631857
  • ISBN-13 ‏ : ‎ 978-0262631853
  • Reading age ‏ : ‎ 18 years and up
  • Grade level ‏ : ‎ 12 and up
  • Item Weight ‏ : ‎ 13.8 ounces
  • Dimensions ‏ : ‎ 9.94 x 6.98 x 0.52 inches
  • Customer Reviews:
    4.0 4.0 out of 5 stars 38 ratings

About the author

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Melanie Mitchell

Melanie Mitchell

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Melanie Mitchell is a professor at the Santa Fe Institute. Melanie's book "Complexity: A Guided Tour" won the 2010 Phi Beta Kappa Science Book Award, was named by Amazon.com as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Her newest book is "Artificial Intelligence: A Guide for Thinking Humans".

Melanie originated the Santa Fe Institute's Complexity Explorer project, which offers free online courses related to complex systems. For more information, go to http://complexityexplorer.org.

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4 out of 5 stars
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38 global ratings

Top reviews from the United States

Richard Jones
5.0 out of 5 stars Five Stars
Reviewed in the United States on May 1, 2016
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Thanks! Arrived as advertised.
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Thomas Wikman
4.0 out of 5 stars Looking for a Good Chromosome
Reviewed in the United States on September 9, 2014
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Melanie Mitchell’s book “an introduction to Genetic Algorithms” explains what Genetic Algorithms are and how they work. It is somewhat outdated by now. However, that does not matter a whole lot since the book is focused on the foundations and the theory behind genetic algorithms and is academic in nature. This book is not a “cook-book” for Genetic Algorithms, and it does not have any practical examples or code that you can “borrow”. Being an academic book it goes into the theoretical foundations of Genetic Algorithms, it uses a fair amount of mathematics, and it backs up claims and discussions with references to research articles. At times the mathematics gets a little bit complex and you better know something about probability, functions, matrix algebra, vector/matrix notation, and infinite series.

For those who do not know; Genetic Algorithms imitate aspects of the evolutionary process observed in nature to solve engineering problems and scientific problems. As such it can also shed light on the natural evolutionary processes (punctuated equilibria, the Baldwin effect, etc). In Genetic Algorithms you have a genetic representation, for example, a “chromosome” (bit string) and you simulate cross over (chromosome blending), mutations, fitness criteria, etc. Melanie Mitchell describes the use of Genetic Algorithms in scientific models, and how they can be used to simulate and explain evolution in nature, she describes different approaches to Genetic Algorithms, automatic programming, using Genetic Algorithms for prediction, and she explains how to use them to solve problems in Artificial Intelligence/Computer Science, and she also describes how to use them with evolving Neural Networks.

What I liked about the book is that despite the fact that it is only 200 pages it covers a lot of ground. The book is well organized, well written, interesting, and concise. I read this book because I was interested in finding out whether I could use some form of Genetic Algorithm to solve some optimization problems at work. Therefore it might not have been exactly the right book for me. At the same time I found the book to be quite interesting and I liked the learning experience. You should understand Genetic Algorithms before you use them anyway. The book is 16 years old by now, perhaps too academic for some people’s taste, and I believe I found a term that was left out in a derivation, so I’ll give a four star rating, but I enjoyed reading it.
5 people found this helpful
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JM Gustafson
3.0 out of 5 stars Good Theoretical GA Textbook
Reviewed in the United States on May 5, 2005
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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. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.

There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.

To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.
15 people found this helpful
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HMH
5.0 out of 5 stars Five Stars
Reviewed in the United States on October 13, 2014
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Great.
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dean_from_sa
5.0 out of 5 stars An introduction and much more
Reviewed in the United States on January 25, 2004
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.
11 people found this helpful
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Placeholder
3.0 out of 5 stars Little above average
Reviewed in India on January 17, 2020
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Mr. C. J. Leaver
5.0 out of 5 stars I found it to be a useful reference for my job in
Reviewed in the United Kingdom on October 27, 2016
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Note that this is an academic text. I found it to be a useful reference for my job in Engineering
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Luca
5.0 out of 5 stars Ottimo libro
Reviewed in Italy on November 7, 2015
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Interessante, piuttosto dettagliato e non troppo difficile (non c'è molta matematica). Mi è stato molto utile per approfondire i genetici.
One person found this helpful
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Daniel O.
3.0 out of 5 stars Good book
Reviewed in the United Kingdom on August 13, 2017
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This is a good enough book for learning about genetic algorithms. However, I would not recommend it as an "introduction" due to it being relatively hard to understand throughout.

More diagrams might be helpful, along with more self-assessment activities.
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cosmicsurfer
2.0 out of 5 stars 浅い。
Reviewed in Japan on December 24, 2003
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もう少しつっこんだ解説をした方が初心者にとっても親切なのでは？
David Goldbergの”Genetic Algorithms in Search, Optimization and Machine Learning”の方をおすすめします。
2 people found this helpful
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