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020			_a9783030762919
024	7		_a10.1007/978-3-030-76291-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
245	1	0	_aAdvances in Learning Automata and Intelligent Optimization _cedited by Javidan Kazemi Kordestani, Mehdi Razapoor Mirsaleh, Alireza Rezvanian, Mohammad Reza Meybodi.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XX, 340 páginas) _b153 ilustraciones, 151 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aIntelligent Systems Reference Library _x1868-4408 _v208
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aAn Introduction to learning automata and optimization -- Learning automaton and its variants for optimization: a bibliometric analysis -- Cellular automata, learning automata, and cellular learning automata for optimization -- Learning automata for behavior control in evolutionary computation -- A memetic model based on fixed structure learning automata for solving NP-Hard problems.
520	3		_aThis book is devoted to the leading research in applying learning automaton (LA) and heuristics for solving benchmark and real-world optimization problems. The ever-increasing application of the LA as a promising reinforcement learning technique in artificial intelligence makes it necessary to provide scholars, scientists, and engineers with a practical discussion on LA solutions for optimization. The book starts with a brief introduction to LA models for optimization. Afterward, the research areas related to LA and optimization are addressed as bibliometric network analysis. Then, LA's application in behavior control in evolutionary computation, and memetic models of object migration automata and cellular learning automata for solving NP hard problems are considered. Next, an overview of multi-population methods for DOPs, LA's application in dynamic optimization problems (DOPs), and the function evaluation management in evolutionary multi-population for DOPs are discussed. Highlighted benefits • Presents the latest advances in learning automata-based optimization approaches. • Addresses the memetic models of learning automata for solving NP-hard problems. • Discusses the application of learning automata for behavior control in evolutionary computation in detail. • Gives the fundamental principles and analyses of the different concepts associated with multi-population methods for dynamic optimization problems.
988			_aSpringer_Robotics_2021
650		7	_2embne _9166090 _aAprendizaje automático
700			_aKazemi Kordestani, Javidan _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682683
700			_aMirsaleh, Mehdi Razapoor _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682684
700			_aRezvanian, Alireza _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9670967
700			_aMeybodi, Mohammad Reza. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9670831
776	0	8	_iPrinted edition: _z9783030762902
776	0	8	_iPrinted edition: _z9783030762926
776	0	8	_iPrinted edition: _z9783030762933
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-76291-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE