Exploitation of Linkage Learning in Evolutionary Algorithms. Questo articolo non è disponibile.
Lingua: inglese
Editore: Springer Berlin Heidelberg, 2010
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The recent progress of linkage learningDemonstrates a new connection between optimization methodologies and natural evolution mechanismsWritten by experts in the fieldOne major branch of enhancing the performance of evolutiona.
Codice articolo 5049995
- Titolo
- Exploitation of Linkage Learning in Evolutionary Algorithms
- Autore
- Chen, Ying-ping
- Editore
- Springer Berlin Heidelberg
- Anno di pubblicazione
- 2010
- Condizione
- New
- Rilegatura
- Gebunden
- Lingua
- inglese
- ISBN 10
- 3642128335
- ISBN 13
- 9783642128332
- Cataloghi dei venditori
- Mathematik/Naturwissenschaften/Technik/Medizin
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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Dalla quarta di copertina
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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