000			03900nam a22005415i 4500
001			978-3-642-40137-4
003			DE-He213
005			20200420220214.0
007			cr nn 008mamaa
008			131002s2013 gw | s |||| 0|eng d
020			_a9783642401374 _9978-3-642-40137-4
024	7		_a10.1007/978-3-642-40137-4 _2doi
050		4	_aQA76.9.A43
072		7	_aUMB _2bicssc
072		7	_aCOM051300 _2bisacsh
082	0	4	_a005.1 _223
100	1		_aB�ack, Thomas. _eauthor.
245	1	0	_aContemporary Evolution Strategies _h[electronic resource] / _cby Thomas B�ack, Christophe Foussette, Peter Krause.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aXIII, 90 p. 33 illus., 31 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		_aNatural Computing Series, _x1619-7127
505	0		_aChap. 1 - Introduction -- Chap. 2 - Evolution Strategies -- Chap. 3 - Taxonomy of Evolution Strategies -- Chap. 4 - Empirical Analysis -- Chap. 5 - Summary -- List of Figures -- List of Algorithms -- Bibliography.
520			_aEvolution strategies have more than 50 years of history in the field of evolutionary computation. Since the early 1990s, many algorithmic variations of evolution strategies have been developed, characterized by the fact that they use the so-called derandomization concept for strategy parameter adaptation. Most importantly, the covariance matrix adaptation strategy (CMA-ES) and its successors are the key representatives of this group of contemporary evolution strategies.   This book provides an overview of the key algorithm developments between 1990 and 2012, including brief descriptions of the algorithms, a unified pseudocode representation of each algorithm, and program code which is available for download. In addition, a taxonomy of these algorithms is provided to clarify similarities and differences as well as historical relationships between the various instances of evolution strategies. Moreover, due to the authors' focus on industrial applications of nonlinear optimization, all algorithms are empirically compared on the so-called BBOB (Black-Box Optimization Benchmarking) test function suite, and ranked according to their performance. In contrast to classical academic comparisons, however, only a very small number of objective function evaluations is permitted. In particular, an extremely small number of evaluations, such as between one hundred and one thousand for high-dimensional functions, is considered. This is motivated by the fact that many industrial optimization tasks do not permit more than a few hundred evaluations. Our experiments suggest that evolution strategies are powerful nonlinear direct optimizers even for challenging industrial problems with a very small budget of function evaluations.   The book is suitable for academic and industrial researchers and practitioners.
650		0	_aComputer science.
650		0	_aAlgorithms.
650		0	_aArtificial intelligence.
650		0	_aMathematical optimization.
650		0	_aComputational intelligence.
650	1	4	_aComputer Science.
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aOptimization.
700	1		_aFoussette, Christophe. _eauthor.
700	1		_aKrause, Peter. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642401367
830		0	_aNatural Computing Series, _x1619-7127
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-40137-4
912			_aZDB-2-SCS
942			_cEBK
999			_c51482 _d51482