Overview
- The recent progress of linkage learning
- Demonstrates a new connection between optimization methodologies and natural evolution mechanisms
- Written by experts in the field
Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 3)
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Table of contents (11 chapters)
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Front Matter
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Linkage and Problem Structures
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Front Matter
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Model Building and Exploiting
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Front Matter
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Applications
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Exploitation of Linkage Learning in Evolutionary Algorithms
Editors: Ying-ping Chen
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-642-12834-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2010
Hardcover ISBN: 978-3-642-12833-2Published: 03 May 2010
Softcover ISBN: 978-3-642-26327-9Published: 28 June 2012
eBook ISBN: 978-3-642-12834-9Published: 16 April 2010
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: X, 246
Number of Illustrations: 30 illustrations in colour
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Applications of Mathematics