000			05422nam a2200913 i 4500
001			5237910
003			IEEE
005			20191218152114.0
006			m o d
007			cr |n|||||||||
008			151221s2006 njua ob 001 eng d
020			_a9780471749219 _qebook
020			_z9780780334816 _qprint
020			_z0471749214 _qelectronic
024	7		_a10.1002/0471749214 _2doi
035			_a(CaBNVSL)mat05237910
035			_a(IDAMS)0b00006481095dcf
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.9.C65 _bF64 2006eb
100	1		_aFogel, David B., _eauthor.
245	1	0	_aEvolutionary computation : _btoward a new philosophy of machine intelligence / _cDavid B. Fogel.
250			_a3rd ed.
264		1	_aHoboken, New Jersey : _bJohn Wiley & Sons, _cc2006.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2006]
300			_a1 PDF (xvii, 274 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIEEE Press series on computational intelligence ; _v1
500			_a"IEEE Neural Networks Council, sponsor."
504			_aIncludes bibliographical references and index.
505	0		_aDefining artificial intelligence -- Natural evolution -- Computer simulation of natural evolution -- Theoretical and empirical properties of evolutionary computation -- Intelligent behavior -- Perspective.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aThis Third Edition provides the latest tools and techniques that enable computers to learn The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience. Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers. As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation. The Third Edition also features new knowledge-building aids. Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well. This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aComputer simulation.
650		0	_aArtificial intelligence.
650		0	_aEvolutionary computation.
655		0	_aElectronic books.
695			_aAdaptation model
695			_aAlgorithm design and analysis
695			_aArtificial neural networks
695			_aBiographies
695			_aBiological system modeling
695			_aCalculators
695			_aCentral Processing Unit
695			_aComputational efficiency
695			_aComputational modeling
695			_aComputer simulation
695			_aComputers
695			_aConvergence
695			_aCorrelation
695			_aEncoding
695			_aEvolution (biology)
695			_aEvolutionary computation
695			_aFeeds
695			_aGames
695			_aGenetics
695			_aHumans
695			_aIndexes
695			_aLead
695			_aLearning systems
695			_aMachine learning
695			_aMarkov processes
695			_aOptimization
695			_aOrganisms
695			_aPhysics
695			_aProblem-solving
695			_aProgramming
695			_aTerminology
710	2		_aIEEE Neural Networks Council.
710	2		_aJohn Wiley & Sons, _epublisher.
710	2		_aIEEE Xplore (Online service), _edistributor.
776	0	8	_iPrint version: _z9780780334816
830		0	_aIEEE Press series on computational intelligence ; _v1
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5237910
999			_c41961 _d41961