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Exploitation of linkage learning in evolutionary algorithms

Author: Ying-ping Chen
Publisher: Berlin : Springer, ©2010.
Series: Adaptation, learning and optimization, 3.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Exploitation of linkage learning in evolutionary algorithms.
Berlin : Springer, ©2010
(DLC) 2010926027
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ying-ping Chen
ISBN: 9783642128349 3642128343 3642128335 9783642128332
OCLC Number: 608318660
Description: 1 online resource (viii, 246 pages) : illustrations.
Contents: Pt. 1. Linkage and problem structures --
pt. 2. Model building and exploiting --
pt. 3. Applications.
Series Title: Adaptation, learning and optimization, 3.
Responsibility: Ying-ping Chen (ed.).

Abstract:

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.

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