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710	2		_aSpringerLink (Online service) _9106996
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008			181109s2019 gw a o |||| 0|eng d
020			_a9783030015480
024	7		_a10.1007/978-3-030-01548-0 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA76.618 _b2019 EB
100	1		_aObuchowicz, Andrzej _eautor _9670885
245	1	0	_aStable mutations for evolutionary algorithms _cby Andrzej Obuchowicz
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (XIV, 164 páginas) _b69 ilustraciones, 11 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aStudies in Computational Intelligence _x1860-949X _v797
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aChapter 1. Introduction -- Chapter 2. Foundations of evolutionary algorithms -- Chapter 3. Stable distributions -- Chapter 4. Non-isotropic stable mutation etc.
520	3		_aThis book presents a set of theoretical and experimental results that describe the features of the wide family of α-stable distributions (the normal distribution also belongs to this class) and their various applications in the mutation operator of evolutionary algorithms based on real-number representation of the individuals, and, above all, equip these algorithms with features that enrich their effectiveness in solving multi-modal, multi-dimensional global optimization problems. The overall conclusion of the research presented is that the appropriate choice of probabilistic model of the mutation operator for an optimization problem is crucial. Mutation is one of the most important operations in stochastic global optimization algorithms in the n-dimensional real space. It determines the method of search space exploration and exploitation. Most applications of these algorithms employ the normal mutation as a mutation operator. This choice is justified by the central limit theorem but is associated with a set of important limitations. Application of α-stable distributions allows more flexible evolutionary models to be obtained than those with the normal distribution. The book presents theoretical analysis and simulation experiments, which were selected and constructed to expose the most important features of the examined mutation techniques based on α-stable distributions. It allows readers to develop a deeper understanding of evolutionary processes with stable mutations and encourages them to apply these techniques to real-world engineering problems.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aProgramación de ordenadores _9139821
650		7	_2embne _aAlgoritmos genéticos _9667357
650		7	_2embne _aComputación evolutiva _9667195
776	0	8	_iPrinted edition: _z9783030015473
776	0	8	_iPrinted edition: _z9783030015497
776	0	8	_iPrinted edition: _z9783030131845
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-01548-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b09/2019 _eel _zSI