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Parallel genetic algorithms for fina...

Bauto, Joao.

Parallel genetic algorithms for financial pattern discovery using GPUs

Record Type:	Electronic resources : Monograph/item
Title/Author:	Parallel genetic algorithms for financial pattern discovery using GPUsby Joao Bauto, Rui Neves, Nuno Horta.
Author:	Bauto, Joao.
other author:	Neves, Rui.
Published:	Cham :Springer International Publishing :2018.
Description:	xiv, 91 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Genetic algorithms.
Online resource:	http://dx.doi.org/10.1007/978-3-319-73329-6
ISBN:	9783319733296$q(electronic bk.)

Parallel genetic algorithms for financial pattern discovery using GPUs
Bauto, Joao.

Parallel genetic algorithms for financial pattern discovery using GPUs[electronic resource] /by Joao Bauto, Rui Neves, Nuno Horta. - Cham :Springer International Publishing :2018. - xiv, 91 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

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

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.

ISBN: 9783319733296$q(electronic bk.)

Standard No.: 10.1007/978-3-319-73329-6doiSubjects--Topical Terms:

182939
Genetic algorithms.

LC Class. No.: QA76.623

Dewey Class. No.: 006.3823

Parallel genetic algorithms for financial pattern discovery using GPUs
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520 $a 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.
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