000			03647nam a22004815i 4500
001			978-3-642-28900-2
003			DE-He213
005			20140220083314.0
007			cr nn 008mamaa
008			120418s2012 gw | s |||| 0|eng d
020			_a9783642289002 _9978-3-642-28900-2
024	7		_a10.1007/978-3-642-28900-2 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aShakya, Siddhartha. _eeditor.
245	1	0	_aMarkov Networks in Evolutionary Computation _h[electronic resource] / _cedited by Siddhartha Shakya, Roberto Santana.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2012.
300			_aXX, 244p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdaptation, Learning, and Optimization, _x1867-4534 ; _v14
505	0		_aFrom the content: Probabilistic Graphical Models and Markov Networks -- A review of Estimation of Distribution Algorithms and Markov networks -- MOA - Markovian Optimisation Algorithm -- DEUM - Distribution Estimation Using Markov Networks -- MN-EDA and the use of clique-based factorisations in EDAs -- Convergence Theorems of Estimation of Distribution Algorithms -- Adaptive Evolutionary Algorithm based on a Cliqued Gibbs Sampling over Graphical Markov Model Structure.
520			_aMarkov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs).  EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered.  The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aEconomics, Mathematical.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aGame Theory/Mathematical Methods.
700	1		_aSantana, Roberto. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642288999
830		0	_aAdaptation, Learning, and Optimization, _x1867-4534 ; _v14
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-28900-2
912			_aZDB-2-ENG
999			_c102915 _d102915