Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (Paperback)

~ (Author) "Though these questions come from very different areas, it is striking how much they have in common..." (more)
Key Phrases: Iron Ore, Santa Fe Institute

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.

Product Description

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. John H. Holland is Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan. He is also Maxwell Professor at the Santa Fe Institute and is Director of the University of Michigan/Santa Fe Institute Advanced Research Program.

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Product Details

• Paperback: 228 pages
• Publisher: The MIT Press (April 29, 1992)
• Language: English
• ISBN-10: 0262581116
• ISBN-13: 978-0262581110
• Product Dimensions: 8.8 x 7 x 0.6 inches
• Shipping Weight: 14.9 ounces (View shipping rates and policies)
• Average Customer Review: 4.2 out of 5 stars  See all reviews (4 customer reviews)
• Amazon.com Sales Rank: #611,128 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

16 of 16 people found the following review helpful:

4.0 out of 5 stars Not an Introductory book, April 12, 2005

By	Carlos L. Castillo (FL, USA) - See all my reviews (REAL NAME)

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.

28 of 34 people found the following review helpful:

4.0 out of 5 stars Genetic Algorithms Classic for Engineering, March 30, 2000

By	Prof David T Wright (Vancouver, BC) - See all my reviews

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.

4.0 out of 5 stars Heavily mathematical, October 14, 2008

By	Mark O. Caldwell - See all my reviews (REAL NAME)

Good, however, the Amazon.com listing did not say that this text was geared for Ph.D.'s in Mathematics.