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007			cr nn 008mamaa
008			180314s2018 gw | s |||| 0|eng d
020			_a9783319708515
024	7		_a10.1007/978-3-319-70851-5 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ342 _b2018 EB
100	1		_aOlivas, Frumen _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aDynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic _cby Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (VII, 105 páginas 25 ilustraciones)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aSpringerBriefs in Computational Intelligence _x2625-3704
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results.
520	3		_aIn this book, a methodology for parameter adaptation in meta-heuristic op-timization methods is proposed. This methodology is based on using met-rics about the population of the meta-heuristic methods, to decide through a fuzzy inference system the best parameter values that were carefully se-lected to be adjusted. With this modification of parameters we want to find a better model of the behavior of the optimization method, because with the modification of parameters, these will affect directly the way in which the global or local search are performed. Three different optimization methods were used to verify the improve-ment of the proposed methodology. In this case the optimization methods are: PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization) and GSA (Gravitational Search Algorithm), where some parameters are se-lected to be dynamically adjusted, and these parameters have the most im-pact in the behavior of each optimization method. Simulation results show that the proposed methodology helps to each optimization method in obtaining better results than the results obtained by the original method without parameter adjustment.
988			_aEBSPRINGER_2018
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aValdez, Fevrier _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _999482
700	1		_aCastillo, Oscar _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n79011941 _1http://viaf.org/viaf/120712882/ _1http://dbpedia.org/resource/Pali_Blues_(PDL)__Oscar_Castillo__1 _937054
700	1		_aMelin, Patricia _d1962- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/85213429/ _997926
776	0	8	_iEdición impresa: _z9783319708508
776	0	8	_iEdición impresa: _z9783319708522
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-70851-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cMSA
998			_b01/2019 _dz _ep _feng _ggw _h0