000			04117nam a22005295i 4500
001			978-0-387-09624-7
003			DE-He213
005			20250710083924.0
007			cr nn 008mamaa
008			110401s2009 xxu| s |||| 0|eng d
020			_a9780387096247 _a99780387096247
024	7		_a10.1007/978-0-387-09624-7 _2doi
100	1		_aBattiti, Roberto. _eauthor.
245	1	0	_aReactive Search and Intelligent Optimization _h[recurso electrónico] / _cby Roberto Battiti, Mauro Brunato, Franco Mascia.
264		1	_aBoston, MA : _bSpringer US, _c2009.
300			_aX, 182p. 74 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aOperations Research/Computer Science Interfaces Series, _x1387-666X ; _v45
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.
650		0	_aMATHEMATICS.
650		0	_aELECTRONIC DATA PROCESSING.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aOPERATIONS RESEARCH.
650		0	_aENGINEERING MATHEMATICS.
650		0	_aINDUSTRIAL ENGINEERING.
650	1	4	_aMATHEMATICS.
650	2	4	_aOPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING.
650	2	4	_aOPERATIONS RESEARCH/DECISION THEORY.
650	2	4	_aCOMPUTING METHODOLOGIES.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aAPPL.MATHEMATICS/COMPUTATIONAL METHODS OF ENGINEERING.
650	2	4	_aINDUSTRIAL AND PRODUCTION ENGINEERING.
700	1		_aBrunato, Mauro. _eauthor.
700	1		_aMascia, Franco. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387096230
830		0	_aOperations Research/Computer Science Interfaces Series, _x1387-666X ; _v45
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-09624-7 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c55996 _d55996