Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs

• Κύριοι συγγραφείς: Baúto, João (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Neves, Rui (http://id.loc.gov/vocabulary/relators/aut), Horta, Nuno (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: SpringerBriefs in Computational Intelligence,
• Θέματα: Computational intelligence.; Financial engineering.; Economics, Mathematical .; Computational Intelligence.; Financial Engineering.; Quantitative Finance.
• Φυσική περιγραφή: XIV, 91 p. 50 illus. online resource.
• ISBN: 9783319733296
• ISSN: 2625-3704
• DOI: 10.1007/978-3-319-73329-6

This 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. .