Cargando…

Exploitation of Linkage Learning in Evolutionary Algorithms

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...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Chen, Ying-ping (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.
Edición:1st ed. 2010.
Colección:Adaptation, Learning, and Optimization, 3
Temas:
Acceso en línea:Texto Completo

MARC

LEADER 00000nam a22000005i 4500
001 978-3-642-12834-9
003 DE-He213
005 20220119145907.0
007 cr nn 008mamaa
008 100416s2010 gw | s |||| 0|eng d
020 |a 9783642128349  |9 978-3-642-12834-9 
024 7 |a 10.1007/978-3-642-12834-9  |2 doi 
050 4 |a TA329-348 
050 4 |a TA345-345.5 
072 7 |a TBJ  |2 bicssc 
072 7 |a TEC009000  |2 bisacsh 
072 7 |a TBJ  |2 thema 
082 0 4 |a 620  |2 23 
245 1 0 |a Exploitation of Linkage Learning in Evolutionary Algorithms  |h [electronic resource] /  |c edited by Ying-ping Chen. 
250 |a 1st ed. 2010. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2010. 
300 |a X, 246 p. 30 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Adaptation, Learning, and Optimization,  |x 1867-4542 ;  |v 3 
505 0 |a Linkage and Problem Structures -- Linkage Structure and Genetic Evolutionary Algorithms -- Fragment as a Small Evidence of the Building Blocks Existence -- Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm -- DEUM - A Fully Multivariate EDA Based on Markov Networks -- Model Building and Exploiting -- Pairwise Interactions Induced Probabilistic Model Building -- ClusterMI: Building Probabilistic Models Using Hierarchical Clustering and Mutual Information -- Estimation of Distribution Algorithm Based on Copula Theory -- Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks -- Applications -- Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA -- Sensible Initialization of a Computational Evolution System Using Expert Knowledge for Epistasis Analysis in Human Genetics -- Estimating Optimal Stopping Rules in the Multiple Best Choice Problem with Minimal Summarized Rank via the Cross-Entropy Method. 
520 |a 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. 
650 0 |a Engineering mathematics. 
650 0 |a Engineering-Data processing. 
650 0 |a Artificial intelligence. 
650 0 |a Mathematics. 
650 1 4 |a Mathematical and Computational Engineering Applications. 
650 2 4 |a Artificial Intelligence. 
650 2 4 |a Applications of Mathematics. 
700 1 |a Chen, Ying-ping.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783642128332 
776 0 8 |i Printed edition:  |z 9783642263279 
776 0 8 |i Printed edition:  |z 9783642128356 
830 0 |a Adaptation, Learning, and Optimization,  |x 1867-4542 ;  |v 3 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-642-12834-9  |z Texto Completo 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712)