- Hardcover: 272 pages
- Publisher: MIT Press (3 Mar 2006)
- Language English
- ISBN-10: 0262041944
- ISBN-13: 978-0262041942
- Product Dimensions: 23.7 x 18.3 x 2 cm
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Amazon Bestsellers Rank:
675,166 in Books (See Top 100 in Books)
- #45 in Books > Computers & Internet > Computer Science > Algorithms > Machine Learning
- #57 in Books > Computers & Internet > Computer Science > Artificial Intelligence > Machine Learning
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Product Description
Product Description
Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artifacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, Evolutionary Computation is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.
About the Author
Kenneth A. De Jong is Professor of Computer Science at George Mason University and the founding editor of the journal Evolutionary Computation (MIT Press).
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3 reviews
A Unified Approach-At Last!
9 Jan 2007
Format:Hardcover
I have read some excellent books and survey papers describing particular domains of the evolutionary computation field, but this book try to give us a "Unified Approach" of this field.
I find very interesting the author's idea to model the evolutionary process with a dynamic (nonlinear!)system so that "...given particular initial conditions the system follows a trajectory over time through a complex evolutionary state space. One can than study various aspects of these processes such as their convergence properties, their sensitivity to initial conditions, their transient behavior and so on."[pg. 2 of the book]. And, I can add, why not "chaotically behaviors".
Prof. Alexandru Serbanescu, Ph.D.,
IEEE Senior Member of CAS and Computational Intelligence Societies
I find very interesting the author's idea to model the evolutionary process with a dynamic (nonlinear!)system so that "...given particular initial conditions the system follows a trajectory over time through a complex evolutionary state space. One can than study various aspects of these processes such as their convergence properties, their sensitivity to initial conditions, their transient behavior and so on."[pg. 2 of the book]. And, I can add, why not "chaotically behaviors".
Prof. Alexandru Serbanescu, Ph.D.,
IEEE Senior Member of CAS and Computational Intelligence Societies
5 of 8 people found the following review helpful
how to apply biological evolution in other areas
12 Mar 2006
Format:Hardcover
De Jong shows how to apply the metaphor of biological evolution to many problems outside biology. He shows that in some other context, there are a few crucial issues to be determined. Firstly, what is an individual? Perhaps a particular algorithm, with certain parameter values. Then, what does it mean to have competing individuals? What is the system within which you are determining the "fitness" of an individual. Finally, how to successful individuals pass on their traits, and can these be mixed between such individuals? The latter is of course the analog of sexual reproduction.
The text describes evolutionary algorithms can be deployed as problem solvers, if you can settle the issues in the previous paragraph. These set the stage for the bulk of your computations.
The text describes evolutionary algorithms can be deployed as problem solvers, if you can settle the issues in the previous paragraph. These set the stage for the bulk of your computations.
1 of 2 people found the following review helpful
Great book for a theoretical overview
11 Nov 2007
Format:Hardcover
This book shows a unified view of genetic algorithms, genetic programming, and related fields. It includes some discussion of the historical development of the disciplines and presents the theory behind them.
However, while it does fit an undergrad course, it is not a book for practitioners, since most algorithms are not explained in detail.
However, while it does fit an undergrad course, it is not a book for practitioners, since most algorithms are not explained in detail.
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