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by John H. Holland

John_H._Holland Social_Sciences English
  • ISBN: 0262082136
  • Category: Other
  • Author: John H. Holland
  • Subcategory: Social Sciences
  • Other formats: txt doc lrf mbr
  • Language: English
  • Publisher: The MIT Press (April 29, 1992)
  • Pages: 228 pages
  • FB2 size: 1677 kb
  • EPUB size: 1767 kb
  • Rating: 4.4
  • Votes: 442
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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.

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.

Complex systems should not require complex writing

This book is basically the same text with an additional chapter discussing progress made in this area of modeling adaptation. Complex systems should not require complex writing. JDN 2456420 EDT 16:34. This book styles itself "an introductory analysis", and it is not very long (about 200 pages), and yet it took me enormous effort and time to get through it all.

Adaptation (Biology), Adaptive control systems, Organisms Adaptation Related to Systems Design. Sixth printing, 2001. Includes bibliographical references (p. 203-205) and index. inlibrary; printdisabled; ; china.

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oceedings{nIN, title {Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence}, author {John H. Holland}, year {1992} }. John H. Holland. From the Publisher: 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.

An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence.

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New Biological Books. Complex Adaptive Systems. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence.

University of Michigan Press, 1975 - 183 pagine. Dall'interno del libro. Cosa dicono le persone - Scrivi una recensione. Recensione dell'utente - FlyByPC - LibraryThing. Great ideas from one of the foremost experts in this new field.

Items related to Adaptation in Natural and Artificial Systems: An Introductor. Holland Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. ISBN 13: 9780262581110.

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.
Reviews about Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (Complex Adaptive Systems) (7):
Sirara
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.
Oghmaghma
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.
Cala
it is very good.fast and excellent
Taun
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.
Conjulhala
Need to get into this book to discover any gems. Arrived when it was expected with good packaging for protection.
Modifyn
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.
Cobandis
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.
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

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