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003			ES-MaUEC
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008			210728s2021 sz | s |||| 0|eng d
020			_a9783030720698
024	7		_a10.1007/978-3-030-72069-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
245	0	0	_aAutomated Design of Machine Learning and Search Algorithms _cedited by Nelishia Pillay, Rong Qu
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVIII, 187 páginas) _b42 ilustraciones, 28 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aNatural Computing Series
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_aChapter 1: Recent Developments of Automated Machine Learning and Search Techniques -- Chapter 2: Automated Machine Learning -- Chapter 3: A General Model for Automated Algorithm Design -- Chapter 4: Rigorous Performance Analysis of Hyper-Heuristics -- Chapter 5: AutoMoDe -- Chapter 6: A cross-domain method for generation of constructive and perturbative heuristics -- Chapter 7: Hyper-heuristics -- Chapter 8: Towards Real-time Federated Evolutionary Neural -- Chapter 9: Knowledge Transfer in Genetic Programming -- Chapter 10: Automated Design of Classification Algorithms -- Chapter 11: Automated Design (AutoDes).
520	3		_aThis book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field. The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.
988			_aSpringer_Computer_2021
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9151819 _aAlgoritmos computacionales
700	1		_aPillay, Nelishia _eeditor literario
700	1		_aQu, Rong _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-72069-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _eu _zSI