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001			102174
003			DE-He213
005			20240111050137.0
007			cr nn 008mamaa
008			180410s2018 gw | s |||| 0|eng d
020			_a9783319893099
024	7		_a10.1007/978-3-319-89309-9 _2doi
040			_aES-MaUEC _bspa
050		4	_aQA76.9.A43 2018 EB
100	1		_aCuevas, Erik. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/ns2011000625 _1http://viaf.org/viaf/185441368/ _944351
245	1	0	_aAdvances in Metaheuristics Algorithms: Methods and Applications _cby Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIV, 218 páginas 48 ilustraciones, 13 ilustraciones a color.)
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence, _x1860-949X _v775
505	0		_aIntroduction -- The metaheuristic algorithm of the social-spider -- Calibration of Fractional Fuzzy Controllers by using the Social-spider method -- The metaheuristic algorithm of the Locust-search -- Identification of fractional chaotic systems by using the Locust Search Algorithm -- The States of Matter Search (SMS) -- Multimodal States of Matter search -- Metaheuristic algorithms based on Fuzzy Logic.
520	3		_aThis book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
650		7	_aAlgoritmos computacionales _2embne _9151819
650		7	_9666321 _aIngeniería asistida por ordenador
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aZaldívar, Daniel. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/ns2011000626 _1http://viaf.org/viaf/185035206/ _944352
700	1		_aPérez-Cisneros, Marco. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/ns2011000627 _1http://viaf.org/viaf/184631799/ _944353
776	0	8	_iEdición impresa: _z9783319893082
776	0	8	_iEdición impresa: _z9783319893105
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-89309-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b12/2018 _dz _ea _feng _ggw _h0
999			_c102174 _d102174 _x1