Exploitation of Linkage Learning in Evolutionary Algorithms
Language: English
Published by Springer, 2010
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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US$ 179.34
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Druck auf Anfrage Neuware - Printed after ordering - One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues.
Seller Inventory # 9783642128332
- Title
- Exploitation of Linkage Learning in Evolutionary Algorithms
- Author
- Ying-Ping Chen
- Publisher
- Springer
- Publication year
- 2010
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3642128335
- ISBN 13
- 9783642128332
- Item weight
- 600 grams
- Dimensions
- 241x160x21 mm
One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues.
"Synopsis" may belong to another edition of this title.
From the Back Cover
One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues.
"About the title" may belong to another edition of this title.
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