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Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence Reprint Edition

by John H. Holland (Author)
4.6 4.6 out of 5 stars 22 ratings
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Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications.

In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics.

Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements.

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  1. ISBN-10
    0262581116
  2. ISBN-13
    978-0262581110
  3. Edition
    Reprint
  4. Publisher
    Bradford Books
  5. Publication date
    April 29, 1992
  6. Language
    English
  7. Dimensions
    7.13 x 0.43 x 9.06 inches
  8. Print length
    232 pages
  9. See all details
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Editorial Reviews

Amazon.com Review

John Holland's Adaptation in Natural and Artificial Systems is one of the classics in the field of complex adaptive systems. Holland is known as the father of genetic algorithms and classifier systems and in this tome he describes the theory behind these algorithms. Drawing on ideas from the fields of biology and economics, he shows how computer programs can evolve. The book contains mathematical proofs that are accessible only to those with strong backgrounds in engineering or science.

About the Author

John H. Holland is Professor of Psychology and Professor of Computer Science and Engineering at the University of Michigan; he is also Trustee and External Professor at the Santa Fe Institute. He is the author of Hidden Order: How Adaptation Builds Complexity and other books.

Product details

  • Publisher ‏ : ‎ Bradford Books; Reprint edition (April 29, 1992)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 232 pages
  • ISBN-10 ‏ : ‎ 0262581116
  • ISBN-13 ‏ : ‎ 978-0262581110
  • Item Weight ‏ : ‎ 13.1 ounces
  • Dimensions ‏ : ‎ 7.13 x 0.43 x 9.06 inches
  • Customer Reviews:
    4.6 4.6 out of 5 stars 22 ratings

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John H. Holland

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4.6 out of 5 stars
4.6 out of 5
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22 global ratings

Top reviews from the United States

Jonathan L.
5.0 out of 5 stars A seminal text in evolutionary computation that set the stage for the 25 years of GA innovation. A must-have for AI researcher.
Reviewed in the United States on December 15, 2017
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The seminal text in evolutionary computation by the founding father of evolutionary and genetic algorithms. This book is an absolute must-have for any artificial intelligence researcher. In this work, Holland introduces the mathematics behind evolutionary algorithms, introduced the much-debated Schema Theorem, and sets the foundation for the next 25 years of evolutionary computation, genetic algorithms, and evolutionary programming.
One person found this helpful
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vac
5.0 out of 5 stars a must-read classic for evolutionary computation researchers
Reviewed in the United States on January 19, 2017
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I am a researcher in evolutionary computation. This is a must read classic for anyone in the field. If you do research, or are otherwise interested in genetic algorithms or more generally evolutionary computation, then you must read this.
2 people found this helpful
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Dan MacKinlay
5.0 out of 5 stars The theory, not the practice.
Reviewed in the United States on November 2, 2010
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There's no source code here. This is the original book from the 70s about the theoretical basis for genetic-style adaptation, and the surprising parallels between evolution, gambling, and learning in general. foundational, clear, mathematical. Probably won't help you pass your assignments though.
2 people found this helpful
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onglee
3.0 out of 5 stars Arrived when it was expected with good packaging for protection
Reviewed in the United States on July 19, 2014
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Need to get into this book to discover any gems. Arrived when it was expected with good packaging for protection.
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GCHEN
5.0 out of 5 stars Five Stars
Reviewed in the United States on March 6, 2015
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it is very good.fast and excellent
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Carlos L. Castillo
4.0 out of 5 stars Not an Introductory book
Reviewed in the United States on April 13, 2005
I am learning by myself the topic of Genetic Algorithms (GA) for my PhD dissertation. Even though this book is written for John H. Holland considered the father of Genetics Algorithms, this is not a basic or easy reading book. The book does not contain any source code and even though it contains some kind of pseudocode, it will not give you a clear idea about how to implement a GA. If you want an introduction book maybe you should look for the Mitchell Melanie's book "An Introduction to Genetic Algorithms" , Fogel's book "Evolutionary Computation vol. 1" or Chamber's book "The Practical Handbook of Genetic Algorithms".

The way the author approaches the development of the framework is sometimes overwhelming because the author does not concentrate in one specific case or concept but he mentions all the different possibilities almost at the same time. I think it is worthwhile to buy the book to have it for advanced understanding of the concepts involved in the study of Complex Adaptive System. My approach to learn GA will be reading the above mentioned books and then study this book in a very detailed and slowly way to digest the huge amount of concepts and information provided by it.
26 people found this helpful
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wiredweird
5.0 out of 5 stars The founder's words
Reviewed in the United States on September 9, 2005
This is a wonderful time. We can read about information theory in Shannon's own words, fuzzy logic in Zadeh's, relativity in Einstein's, and genetic programming in Holland's. He created evolutionary algorithms, and shares his thoughts in this brief work.

1975, when he first published this work, was a long time ago. Since then, computing has advanced, computing demands have advanced, and biology has advanced. Biology, because it functions at all the levels from atoms to worlds, has bottomless potential for insight. Because the atoms, the worlds, and everything between are all unfriendly, biology has many problems to solve. It doesn't matter whether you are an oak tree, a virus, or a whale, the solution (at the species level) is the same: evolve. Holland was the first to harness that incredible problem-solving power to computational use.

A huge literature has built up from Holland's founding thoughts. Those thoughts are here, in their original and purest form. It is hardly surprising that Holland anticipated so many elaborations of his work. One, in particular, struck me: the idea of 'hot spots' for genetic crossover. Or rather the opposite: 'cold spots' where crossover is inhibited. As a computer scientist, Holland's first thoughts were written in binary. When you allow points where crossover can not occur, you allow coherent multibit values - maybe even floating point. It's easy to laugh at Holland's initial naivete now, but he was talking about the foundations, not the structure built up from it.

If you have ever programmed genetic algorithms, you have been stunned by their effectiveness in creating good solutions. 'Good' doesn't mean precisely optimal, but pretty damm good anyway.

If you were a hard core creationist to start with, you still are. But now you know that evolutionary problem solving is powerful, broad, subtle, and effective - so much, that it's hard to believe it could ever have arisen by chance.

//wiredweird
6 people found this helpful
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Prof David T Wright
4.0 out of 5 stars Genetic Algorithms Classic for Engineering
Reviewed in the United States on March 30, 2000
This book presents an inspirational synthesis from mathematics, computer science and systems theory addressing genetic algorithms and their role in intelligent engineering/business systems.
Topics include: background, a formal framework, illustrations (genetics, economics, game playing, searches, pattern recognition and statistical inference, control and function optimization, and central-nervous system), schemata, the optimal allocation of trials, reproductive plans and genetic operators, the robustness of genetic plans, adaptation of coding and representations, and overview, interim and prospectus.
Inclusion of a disk of spreadsheet-based examples would have increased user-friendliness to the sometimes moderately-complex mathematics. Otherwise, this book is a well presented, and useful classic for researchers and software vendors seeking to develop more innovative intelligent products.
32 people found this helpful
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Top reviews from other countries

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matteo
5.0 out of 5 stars Ben fatto
Reviewed in Italy on May 28, 2020
Verified Purchase
Non si può fare a meno di questo libro se si vuole conoscere veramente gli algoritmi genetici. Peccato sia in inglese.
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cosmicsurfer
3.0 out of 5 stars 実用よりアカデミック
Reviewed in Japan on December 24, 2003
Verified Purchase
遺伝的アルゴリズム（GA）の考案者である著者が、１９７０年代に書いた GAのバイブル的１冊を９０年代はじめに改訂したものです。著者はどちらかというと、GAの工学的応用よりも生命の発生の方に興味があるようで、この本は GA考案に至るまでの経緯を追いたい方にとっては５つ星でしょうが、実用面の記述ははっきり言っては弱いです。機械学習等、工学的応用に興味のある方にとっては、弟子の David Goldbergの著作をお勧めします。
6 people found this helpful
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