Exploitation of Linkage Learning in Evolutionary Algorithms (Hardcover, 2010 Ed.)


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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Product Description

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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Product Details

General

Imprint

Springer-Verlag

Country of origin

Germany

Series

Adaptation, Learning, and Optimization, 3

Release date

May 2010

Availability

Expected to ship within 10 - 15 working days

First published

2010

Editors

Dimensions

235 x 155 x 20mm (L x W x T)

Format

Hardcover - Cloth over boards

Pages

246

Edition

2010 Ed.

ISBN-13

978-3-642-12833-2

Barcode

9783642128332

Categories

LSN

3-642-12833-5



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