Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs

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. 

Sommario

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

Info autore

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.

Riassunto

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. 

Dettagli sul prodotto

• Autori: Joã Baúto, Joao Baúto, João Baúto, Nuno Horta, Ru Neves, Rui Neves
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 01.01.2018
• EAN: 9783319733289
• ISBN: 978-3-31-973328-9
• Pagine: 91
• Dimensioni: 157 mm x 235 mm x 236 mm
• Peso: 178 g
• Illustrazioni: XIV, 91 p. 50 illus.
• Serie: Computational Intelligence; SpringerBriefs in Applied Sciences and Technology; SpringerBriefs in Computational Intelligence; SpringerBriefs in Applied Sciences and Technology; Computational Intelligence; SpringerBriefs in Computational Intelligence
• Categorie: Scienze naturali, medicina, informatica, tecnica > Tecnica > Tematiche generali, enciclopedie; C, Anlagen und Wertpapiere, Finance, Angewandte Mathematik, engineering, Applications of Mathematics, Finance & accounting, Economics, Mathematical, Financial Engineering, Quantitative Finance, Computational Intelligence, Economics, finance, business and management