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Evolutionary Computation: Toward a New Philosophy of Machine Intelligence
 
 
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Evolutionary Computation: Toward a New Philosophy of Machine Intelligence [Hardcover]

David B. Fogel (Author)
4.5 out of 5 stars  See all reviews (2 customer reviews)


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Hardcover, August 18, 1999 --  

Book Description

078035379X 978-0780353794 August 18, 1999 2
In this revised and significantly expanded second edition, distinguished scientist Dr. David B. Fogel presents the latest advances in both the theory and practice of evolutionary computation to help you keep pace with the most recent developments in this fast-changing field.

In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence. You will gain current insights into the history of evolutionary computation and the newest theories shaping research today. Fogel thoroughly reviews the "no free lunch" theorem and includes a discussion of findings that challenge the very foundations of simulated evolution. This second edition also presents the latest game-playing techniques that combine evolutionary algorithms with neural networks, including their success in playing competitive checkers. Chapter by chapter, this comprehensive book highlights the relationship between learning and intelligence.

Evolutionary Computationfeatures an unparalleled integration of history with state-of-the-art theory and practice for engineers, professors, and graduate students of evolutionary computation and computer science who need to keep up to date in this developing field.


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Editorial Reviews

Review

"...a major contribution to the evolutionary computation literature...recommended reading for experienced researchers, as well as novice students…" (Computing Reviews.com, May 26, 2006)

From the Back Cover

Computer Science/Artificial Intelligence Evolutionary Computation Toward a New Philosophy of Machine Intelligence Second Edition In this revised and significantly expanded second edition, distinguished scientist David B. Fogel presents the latest advances in both the theory and practice of evolutionary computation to help you keep pace with developments in this fast-changing field. In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence. You will gain current insights into the history of evolutionary computation and the newest theories shaping research. Fogel carefully reviews the "no free lunch theorem" and discusses new theoretical findings that challenge some of the mathematical foundations of simulated evolution. This second edition also presents the latest game-playing techniques that combine evolutionary algorithms with neural networks, including their success in playing competitive checkers. Chapter by chapter, this comprehensive book highlights the relationship between learning and intelligence. Evolutionary Computation features an unparalleled integration of history with state-of-the-art theory and practice for engineers, professors, and graduate students of evolutionary computation and computer science who need to keep up-to-date in this developing field.

Product Details

  • Hardcover: 290 pages
  • Publisher: Wiley-IEEE Press; 2 edition (August 18, 1999)
  • Language: English
  • ISBN-10: 078035379X
  • ISBN-13: 978-0780353794
  • Product Dimensions: 9.4 x 6.3 x 0.9 inches
  • Shipping Weight: 1.3 pounds
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #3,103,672 in Books (See Top 100 in Books)

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Average Customer Review
4.5 out of 5 stars (2 customer reviews)
 
 
 
 
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10 of 12 people found the following review helpful:
5.0 out of 5 stars The book provides a solid foundation on the subject., March 31, 2000
This is an introductory text useful for teaching at graduate levels in Computer/ Information Sciences. The first two chapters provide an overview of the subject and its relationship with other relevant areas. Chapter 4 covers the analysis of the GA with special emphasis to convergence of the algorithm.This is the main chapter of the book.

The presentation style of the book is very beautiful.The book should be read by everyone interested in the disciplines of genetic algorithms and/or soft computing.

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0 of 2 people found the following review helpful:
4.0 out of 5 stars for researchers and students, November 11, 2006
Ignore the gushing blurb on the back cover about the book having the latest tools and techniques to let computers learn. While the methods are indeed state of the art when they were written, true learning by computers is still elusive. But so long as you keep that reality in mind, the text can indeed be useful.

We see the span of ideas in evolutionary computing. Aided in no small part by the massive and continued increase in computational power. Fogel laments that the book's ideas are still typically outside what is generally taken to be Artificial Intelligence.

The book strives to be both a text for researchers and for students. Though of course the two groups overlap. For researchers, each chapter has a long list of references to journal papers and monographs, so that you can go directly to many of the original sources. While for students, the chapters come with a non-trivial set of exercises, that usually involve some programming.
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Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
evolved player, piece differential, evolved neural network, global best solution, minimizing expected losses, tionary algorithm, uniform crossover, empirical properties, evolutionary computation, free lunch theorem, proportional selection, fitness distributions, bit mutation, schema theorem, variation operators, random crossover, absorbing state, chess programs, first hidden layer
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