000			03697nam a22005175i 4500
001			978-3-642-16544-3
003			DE-He213
005			20140220084549.0
007			cr nn 008mamaa
008			101109s2010 gw | s |||| 0|eng d
020			_a9783642165443 _9978-3-642-16544-3
024	7		_a10.1007/978-3-642-16544-3 _2doi
050		4	_aQA76.9.A43
072		7	_aUMB _2bicssc
072		7	_aCOM051300 _2bisacsh
082	0	4	_a005.1 _223
100	1		_aNeumann, Frank. _eauthor.
245	1	0	_aBioinspired Computation in Combinatorial Optimization _h[electronic resource] : _bAlgorithms and Their Computational Complexity / _cby Frank Neumann, Carsten Witt.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aXII, 216 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aNatural Computing Series, _x1619-7127
505	0		_aBasics -- Combinatorial Optimization and Computational Complexity -- Stochastic Search Algorithms -- Analyzing Stochastic Search Algorithms -- Single-objective Optimization -- Minimum Spanning Trees -- Maximum Matchings -- Makespan Scheduling -- Shortest Paths -- Eulerian Cycles -- Multi-objective Optimization -- Multi-objective Minimum Spanning Trees -- Minimum Spanning Trees Made Easier -- Covering Problems -- Cutting Problems.
520			_aBioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these search heuristics. This is the first book to explain the most important results achieved in this area. The authors show how runtime behavior can be analyzed in a rigorous way. in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single-objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems. This book will be valuable for graduate and advanced undergraduate courses on bioinspired computation, as it offers clear assessments of the benefits and drawbacks of various methods. It offers a self-contained presentation, theoretical foundations of the techniques, a unified framework for analysis, and explanations of common proof techniques, so it can also be used as a reference for researchers in the areas of natural computing, optimization and computational complexity.
650		0	_aComputer science.
650		0	_aInformation theory.
650		0	_aComputer software.
650		0	_aArtificial intelligence.
650		0	_aMathematical optimization.
650	1	4	_aComputer Science.
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aOptimization.
650	2	4	_aTheory of Computation.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aWitt, Carsten. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642165436
830		0	_aNatural Computing Series, _x1619-7127
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-16544-3
912			_aZDB-2-SCS
999			_c112840 _d112840