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001 978-3-319-30883-8
003 DE-He213
005 20200420221249.0
007 cr nn 008mamaa
008 160523s2016 gw | s |||| 0|eng d
020 _a9783319308838
_9978-3-319-30883-8
024 7 _a10.1007/978-3-319-30883-8
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aBlum, Christian.
_eauthor.
245 1 0 _aHybrid Metaheuristics
_h[electronic resource] :
_bPowerful Tools for Optimization /
_cby Christian Blum, G�unther R. Raidl.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXVI, 157 p. 20 illus., 9 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
650 0 _aComputer science.
650 0 _aOperations research.
650 0 _aDecision making.
650 0 _aComputers.
650 0 _aArtificial intelligence.
650 0 _aMathematical optimization.
650 0 _aComputational intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aTheory of Computation.
650 2 4 _aComputational Intelligence.
650 2 4 _aOperation Research/Decision Theory.
650 2 4 _aOptimization.
700 1 _aRaidl, G�unther R.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319308821
830 0 _aArtificial Intelligence: Foundations, Theory, and Algorithms,
_x2365-3051
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-30883-8
912 _aZDB-2-SCS
942 _cEBK
999 _c52468
_d52468