000			02845nam a22005415i 4500
999			_c13387 _d13387
001			978-3-319-73329-6
003			DE-He213
005			20211216111416.0
008			180204s2018 gw | s |||| 0|eng d
020			_a9783319733296
040			_cAIKTC-KRRC
041			_aENG
072		7	_aUYQ _2bicssc
072		7	_aTEC009000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
100	1		_aBaúto, João. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aParallel Genetic Algorithms for Financial Pattern Discovery Using GPUs _h[electronic resource] /
250			_a1st ed. 2018.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018.
300			_aXIV, 91 p. 50 illus. _bCard Paper
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computational Intelligence, _x2625-3704
520			_aThis Brief presents a study of SAX/GA, an algorithm to optimize market trading strategies, to understand how the sequential implementation of SAX/GA and genetic operators work to optimize possible solutions. This study is later used as the baseline for the development of parallel techniques capable of exploring the identified points of parallelism that simply focus on accelerating the heavy duty fitness function to a full GPU accelerated GA. .
650		0	_aComputer Engineering _94622
653			_aComputational Intelligence.
653			_aFinancial Engineering.
653			_aQuantitative Finance.
700	1		_aNeves, Rui. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aHorta, Nuno. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783319733289
776	0	8	_iPrinted edition: _z9783319733302
830		0	_aSpringerBriefs in Computational Intelligence, _x2625-3704
856	4	0	_uhttps://doi.org/10.1007/978-3-319-73329-6 _zClick here to access eBook in Springer Nature platform. (Within Campus only.)
942			_cEBOOKS _2ddc