Exploitation of Linkage Learning in Evolutionary Algorithms

Ying-Ping Chen

ISBN 10: 3642128335 / ISBN 13: 9783642128332

Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2010

About this Item

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

Bibliographic Details

• Title: Exploitation of Linkage Learning in Evolutionary Algorithms
• Publisher: Springer Berlin Heidelberg, Springer Berlin Heidelberg
• Publication Date: 2010
• Language: English
• ISBN 10: 3642128335
• ISBN 13: 9783642128332
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 21D millimeters 241 millimeters width by 160 millimeters height by 21 millimeters depth
• Weight: 600 grams

About this title

Synopsis

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.