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

By: Baúto, João, autor
Contributor(s): Neves, Rui | Horta, Nuno C. G., autor
Material type: materialTypeLabelE-bookSeries: (SpringerBriefs in Computational Intelligence, 2625-3704); (Engineering (Springer-11647)).Publisher: Cham : Springer International Publishing, 2018Description: 1 recurso en línea (XIV, 91 páginas 50 ilustraciones).ISBN: 9783319733296.Subject: Algoritmos genéticosOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
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.
Abstract: 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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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería QA76.623 2018 EB (Browse shelf(Opens below)) Acceso electrónico eBook.15112965
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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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