Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs

João Baúto,

Rui Neves,

Nuno Horta

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This Brief presents a study of SAX/GA, an algorithm to optimize market trading strategies, to understand how the sequential implem...

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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.