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008			150903s2009 xxu| o |||| 0|eng d
020			_a9780387096247 _9978-0-387-09624-7
024	7		_a10.1007/9780387096247 _2doi
035			_avtls000329707
039		9	_a201509030451 _bVLOAD _c201404121651 _dVLOAD _c201404091428 _dVLOAD _c201401311316 _dstaff _y201401291438 _zstaff _wmsplit0.mrc _x130
100	1		_aBattiti, Roberto. _eautor _9299925
245	1	0	_aReactive Search and Intelligent Optimization / _cby Roberto Battiti, Mauro Brunato, Franco Mascia.
264		1	_aBoston, MA : _bSpringer US, _c2009.
300			_aX, 182páginas, 74 illus. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aOperations Research/Computer Science Interfaces Series, _x1387-666X ; _v45
500			_aSpringer eBooks
505	0		_aIntroduction: Machine Learning for Intelligent Optimization -- Reacting on the neighborhood -- Reacting on the Annealing Schedule -- Reactive Prohibitions -- Reacting on the Objective Function -- Reacting on the Objective Function -- Supervised Learning -- Reinforcement Learning -- Algorithm Portfolios and Restart Strategies -- Racing -- Teams of Interacting Solvers -- Metrics, Landscapes and Features -- Open Problems.
520			_aReactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive search self-tunes and adapts, effectively learning by doing until a solution is found. Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics. Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics. Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities and schemes for the automated tuning of these parameters. Anyone working in decision making in business, engineering, economics or science will find a wealth of information here.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aBrunato, Mauro. _eautor _9299926
700	1		_aMascia, Franco. _eautor _9299927
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387096230
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-09624-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c277289 _d277289