000 03602nam a22004935i 4500
001 978-3-642-12834-9
003 DE-He213
005 20140220084536.0
007 cr nn 008mamaa
008 100416s2010 gw | s |||| 0|eng d
020 _a9783642128349
_9978-3-642-12834-9
024 7 _a10.1007/978-3-642-12834-9
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aChen, Ying-ping.
_eeditor.
245 1 0 _aExploitation of Linkage Learning in Evolutionary Algorithms
_h[electronic resource] /
_cedited by Ying-ping Chen.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _a265p. 30 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aEvolutionary Learning and Optimization,
_x1867-4534 ;
_v3
505 0 _aLinkage 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 _aOne 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 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aMathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aApplications of Mathematics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642128332
830 0 _aEvolutionary Learning and Optimization,
_x1867-4534 ;
_v3
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-12834-9
912 _aZDB-2-ENG
999 _c112147
_d112147