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710	2		_aSpringerLink (Online service) _9106996
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008			181218s2019 gw a o |||| 0|eng d
020			_a9783030027292
024	7		_a10.1007/978-3-030-02729-2 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQ325.5 _b2019 EB
100	1		_aGupta, Abhishek _eautor _9671485
245	1	0	_aMemetic Computation : _bThe Mainspring of Knowledge Transfer in a Data-Driven Optimization Era _cby Abhishek Gupta, Yew-Soon Ong.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (XI, 104 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aAdaptation Learning and Optimization _x1867-4534 _v21
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction: Rise of Memetics in Computing -- Canonical Memetic Algorithms -- Data-Driven Adaptation in Memetic Algorithms -- The Memetic Automaton -- Sequential Knowledge Transfer across Problems -- Multitask Knowledge Transfer across Problems -- Future Direction: Meme Space Evolutions.
520	3		_aThis book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC - beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solving prowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly - thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _9145705 _aOptimización matemática
700	1		_aOng, Yew-Soon. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030027285
776	0	8	_iPrinted edition: _z9783030027308
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-02729-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b11/2019 _ek _zSI