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An Introduction to Genetic Algorithms (Complex Adaptive Systems Series]

An Introduction to Genetic Algorithms (Complex Adaptive Systems Series]

An Introduction to Genetic Algorithms (Complex Adaptive Systems Series]
Stock photo: cover may vary

An Introduction to Genetic Algorithms (Complex Adaptive Systems Series] Paperback - 1999

by Mitchell, Melanie

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Genetic algorithms are used in science and engineering for problem solving and as computational models. This brief introduction enables readers to implement and experiment with genetic algorithms on their own. The descriptions of applications and modeling projects stretch beyond the boundaries of computer science to include systems theory, game theory, biology, ecology, and population genetics. 20 illustrations.

Used - Near Fine

Description

MIT Press / a Bradford Book, 1999. No remainder marks. A stock image is an accurate representation of the listed book's cover design. Pages [vii, 208 including index] clean, unmarked. Covers with minimal display indications, no reading creases at spine, one small spot at top of front page edges. Illustrated with graphs and drawings. All books are in my smoke free & climate controlled shop and will have qualities and/or flaws described. Domestic & international shipping includes tracking information. . Fifth Printing. Trade Paperback. Near Fine. Illus. by Unidentified. 8vo - over 7 3/4 to 9 3/4".
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Details

  • Title An Introduction to Genetic Algorithms (Complex Adaptive Systems Series]
  • Author Mitchell, Melanie
  • Illustrator Unidentified
  • Binding Paperback
  • Edition Fifth Printing
  • Condition Used - Near Fine
  • Pages 221
  • Volumes 1
  • Language ENG
  • Publisher MIT Press / a Bradford Book, Cambridge, Massachusetts, U.S.A.
  • Publication date 1999
  • Features Bibliography, Index
  • Bookseller's Inventory # 016190
  • ISBN 9780262631853 / 0262631857
  • Weight 1.03 lbs (0.47 kg)
  • Dimensions 9.94 x 6.98 x 0.52 in (25.25 x 17.73 x 1.32 cm)
  • Size 8vo - over 7 3/4 to 9 3/4"
  • Age range 18 to UP years
  • Grade levels 13 - UP
  • Category Computers - General Information
  • Library of Congress subjects Genetic algorithms, Genetics - Mathematical models
  • Dewey Decimal Code 575.101
  • Bookseller catalogues Science & Technology; Computers & IT

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From the publisher

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.

The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

About the author

Melanie Mitchell, Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, is a Fellow of the Michigan Society of Fellows. She is also Director of the Adaptive Computation Program at the Santa Fe Institute.

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