Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs

1. Auflage 2018
ISBN: 978-3-319-73328-9
Verlag: Springer

Buch, Englisch, 91 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 180 g

Reihe: SpringerBriefs in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Baúto, João

Neves, Rui

Horta, Nuno

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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

Über Autor(innen)

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.