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Evolutionary Algorithms for Single and Multicriteria Design Optimization (Studies in Fuzziness and Soft Computing)
 
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Evolutionary Algorithms for Single and Multicriteria Design Optimization (Studies in Fuzziness and Soft Computing) [Hardcover]

Andrzej Osyczka (Author)
5.0 out of 5 stars  See all reviews (1 customer review)


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Book Description

October 25, 2001 3790814180 978-3790814187 1
Many design optimization problems are of a very complex nature and quite hard to solve by conventional optimization techniques. Genetic and evolutionary algorithms have recently received considerable attention because of their potential of being a very effective design optimization technique. The book starts with an introduction to design optimization which is followed by a description of genetic and evolutionary algorithms. Then the advanced evolutionary algorithm techniques are provided. These techniques are used in the single and multicriteria optimization methods described in this book. Finally three real-life design optimization problems are formulated and solved by means of these methods. The book is designed as a self-study guide for researchers and students in all engineering departments, especially in mechanical, civil and industrial engineering. The book may also be useful as a comprehensive text for operations researchers, artifical intelligence researchers.

Product Details

  • Hardcover: 218 pages
  • Publisher: Physica-Verlag HD; 1 edition (October 25, 2001)
  • Language: English
  • ISBN-10: 3790814180
  • ISBN-13: 978-3790814187
  • Product Dimensions: 9.4 x 6.3 x 0.7 inches
  • Shipping Weight: 1.1 pounds
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #8,717,843 in Books (See Top 100 in Books)

 

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5.0 out of 5 stars Excellent book, July 16, 2003
This review is from: Evolutionary Algorithms for Single and Multicriteria Design Optimization (Studies in Fuzziness and Soft Computing) (Hardcover)
It is very good book for people who works in structural optimization. It has many nice examples. I will buy it for my library.
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