Exploitation of linkage learning in evolutionary algorithms - Info and Reading Options
By Ying-ping Chen

"Exploitation of linkage learning in evolutionary algorithms" was published by Springer in 2010 - Berlin, it has 246 pages and the language of the book is English.
“Exploitation of linkage learning in evolutionary algorithms” Metadata:
- Title: ➤ Exploitation of linkage learning in evolutionary algorithms
- Author: Ying-ping Chen
- Language: English
- Number of Pages: 246
- Publisher: Springer
- Publish Date: 2010
- Publish Location: Berlin
“Exploitation of linkage learning in evolutionary algorithms” Subjects and Themes:
- Subjects: ➤ Evolutionary programming (Computer science) - Evolutionary computation - Genetic algorithms - Evolutionary programming (computer science)
Edition Specifications:
- Pagination: x, 246 p. :
Edition Identifiers:
- The Open Library ID: OL25321420M - OL16643625W
- Online Computer Library Center (OCLC) ID: 635860791
- Library of Congress Control Number (LCCN): 2010926027
- ISBN-13: 9783642128332 - 9783642128349
- ISBN-10: 3642128335
- All ISBNs: 3642128335 - 9783642128332 - 9783642128349
AI-generated Review of “Exploitation of linkage learning in evolutionary algorithms”:
"Exploitation of linkage learning in evolutionary algorithms" Table Of Contents:
- 1- pt. 1. Linkage and problem structures
- 2- pt. 2. Model building and exploiting
- 3- pt. 3. Applications.
"Exploitation of linkage learning in evolutionary algorithms" Description:
Open Data:
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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