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Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs [electronic resource] / by João Baúto, Rui Neves, Nuno Horta.

By: Baúto, João [author.].
Contributor(s): Neves, Rui [author.] | Horta, Nuno [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computational Intelligence: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XIV, 91 p. 50 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319733296.Subject(s): Computational intelligence | Financial engineering | Social sciences—Mathematics | Computational Intelligence | Financial Engineering | Mathematics in Business, Economics and FinanceAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- State-of-the-Art in Pattern Recognition Techniques -- SAX/GA CPU Approach -- GPU-accelerated SAX/GA -- Conclusions and Future Work in the Field.
In: Springer Nature eBookSummary: 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. .
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Introduction -- State-of-the-Art in Pattern Recognition Techniques -- SAX/GA CPU Approach -- GPU-accelerated SAX/GA -- Conclusions and Future Work in the Field.

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. .

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