Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs

João Baúto, Rui Neves, Nuno Horta (Autoren)

Buch | Softcover

XIV, 91 Seiten

2018 | 1st ed. 2018
Springer International Publishing (Verlag)
9783319733289 (ISBN)

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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.

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.

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

Erscheinungsdatum	18.02.2018
Reihe/Serie	SpringerBriefs in Applied Sciences and Technology
Reihe/Serie	SpringerBriefs in Computational Intelligence
Zusatzinfo	XIV, 91 p. 50 illus.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	178 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik
	Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
Schlagworte	Computational Finance • Data Science • High Performance Computing • market trading strategies • pattern recognition techniques • Quantitative Finance • SAX/GA algorithm
ISBN-13	9783319733289 / 9783319733289
Zustand	Neuware