Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs

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Beschreibung

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.

Describes in deep the efficient implementation of SAX/GA algorithm in GPU

Presents an algorithm useful to optimize market trading strategies

Useful for computational finance applications



Autorentext
João Baúto works at Fundacao Champalimaud in Lisbon, Portugal. He implements high performance computing tools applied to neuroscience and cancer research.

Rui Ferreira Neves is a professor at Instituto Superior Técnico, Portugal. His research activity comprises evolutionary computation and pattern matching applied to the financial markets, sensor networks, embedded systems and mixed signal integrated circuits.

Nuno Horta is the Head of the Integrated Circuits Group, Instituto de Telecomunicacoes, Portugal. His reseach interests are mainly in analog and mixed-sgnal IC design, analog IC design automation, soft computing and data science.


Klappentext

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.



Zusammenfassung
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. 

Inhalt
Introduction.- State-of-the-Art in Pattern Recognition Techniques.- SAX/GA CPU Approach.- GPU-accelerated SAX/GA.- Conclusions and Future Work in the Field.

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Produktinformationen

Titel
Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs
Autor
EAN
9783319733289
ISBN
978-3-319-73328-9
Format
Kartonierter Einband
Herausgeber
Springer, Berlin
Genre
Technik
Anzahl Seiten
91
Gewicht
178g
Größe
H236mm x B235mm x T157mm
Jahr
2018
Untertitel
Englisch
Auflage
1st ed. 2018
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