000			03858nam a22004455i 4500
001			85768
003			ES-MaUEC
005			20240111050124.0
007			cr nn 008mamaa
008			160523s2016 gw a s 001 0 eng d
020			_a9783319308838
024	7		_a10.1007/978-3-319-30883-8 _2doi
040			_aES-MaUEC
050		4	_aT57.84 _b.B58 2016 EB
082	0	4	_a006.3
100	1		_aBlum, Christian _998960 _0Local
245	1	0	_aHybrid Metaheuristics : _bPowerful Tools for Optimization _cby Christian Blum, Günther R Raidl
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XVI, 157 p.) _b20 ilustraciones, 9 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aArtificial Intelligence: Foundations, Theory, and Algorithms _x2365-3051
505	0		_aIntroduction -- Incomplete Solution Representations and Decoders -- Hybridization Based on Problem Instance Reduction -- Hybridization Based on Large Neighborhood Search -- Making Use of a Parallel, Non-independent, Construction of Solutions Within Metaheuristics -- Hybridization Based on Complete Solution Archives -- Further Hybrids and Conclusions.
520			_aThis book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives. The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650	0	4	_aProgramación metaheurística _9467159
650		7	_9145705 _aOptimización matemática _0comprobar BNE19922522034 _2embne
650		7	_aInteligencia artificial _0comprobar BNE19900997218 _2embne _9413115
650		7	_aToma de decisiones _0comprobar BNE19900995115 _2embne _9141176
700	1		_aRaidl, Günther R. _998961 _0Local
830		0	_aArtificial Intelligence: Foundations, Theory, and Algorithms _x2365-3051 _9134252 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-30883-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319308838
907			_a.b12950464 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b23-09-17 _cm _dz _eo _feng _ggw _h0
945			_aT57.84 .B58 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i1159357x _z06-04-17
999			_c85768 _d85768 _x1