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Parallel genetic algorithms for financial pattern discovery using GPUs

Author: João Baúto; Rui César das Neves; Nuno C G Horta
Publisher: Cham, Switzerland : Springer, 2018.
Series: SpringerBriefs in applied sciences and technology., Computational intelligence.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Baúto, João.
Parallel genetic algorithms for financial pattern discovery using GPUs.
Cham, Switzerland : Springer, 2018
(OCoLC)1013942367
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: João Baúto; Rui César das Neves; Nuno C G Horta
ISBN: 9783319733296 331973329X
OCLC Number: 1021882290
Description: 1 online resource (xiv, 91 pages) : illustrations
Contents: Intro; Preface; Contents; Acronyms; 1 Introduction; 1.1 Motivation; 1.2 Goals; 1.3 Book Outline; References; 2 Background; 2.1 Time Series Analysis; 2.1.1 Euclidean Distance; 2.1.2 Dynamic Time Warping; 2.1.3 Piecewise Linear Approximation; 2.1.4 Piecewise Aggregate Approximation; 2.1.5 Symbolic Aggregate approXimation; 2.2 Genetic Algorithm; 2.2.1 Selection Operator; 2.2.2 Crossover Operator; 2.2.3 Mutation Operator; 2.3 Graphics Processing Units; 2.3.1 NVIDIA's GPU Architecture Overview; 2.3.2 NVIDIA's GPU Architectures; 2.3.3 CUDA Architecture; 2.4 Conclusions; References 3 State-of-the-Art in Pattern Recognition Techniques3.1 Middle Curve Piecewise Linear Approximation; 3.2 Perceptually Important Points; 3.3 Turning Points; 3.4 Symbolic Aggregate approXimation; 3.5 Shapelets; 3.6 Conclusions; References; 4 SAX/GA CPU Approach; 4.1 SAX/GA CPU Approach; 4.1.1 Population Generation; 4.1.2 Fitness Evaluation; 4.1.3 Population Selection; 4.1.4 Chromosome Crossover; 4.1.5 Individual Mutation; 4.2 SAX/GA Performance Analysis; 4.3 Conclusions; References; 5 GPU-Accelerated SAX/GA; 5.1 Parallel SAX Representation; 5.1.1 Prototype 1: SAX Transformation On-Demand 5.1.2 Prototype 2: Speculative FSM5.1.3 Solution A: SAX/GA with Speculative GPU SAX Transformation; 5.2 Parallel Dataset Training; 5.2.1 Prototype 3: Parallel SAX/GA Training; 5.2.2 Solution B: Parallel SAX/GA Training with GPU Fitness Evaluation; 5.3 Fully GPU-Accelerated SAX/GA; 5.3.1 Population Generation Kernel; 5.3.2 Population Selection; 5.3.3 Gene Crossover Kernel; 5.3.4 Gene Mutation Kernel; 5.3.5 Execution Flow; 5.4 Conclusions; Reference; 6 Results; 6.1 SAX/GA Initial Constraints; 6.2 Study Case A: Execution Time; 6.2.1 Solution A: SAX/GA with Speculative FSM 6.2.2 Solution B: Parallel Dataset Training6.2.3 Solution C: Fully GPU-Accelerated SAX/GA; 6.3 Study Case B: FSM Prediction Rate; 6.4 Study Case C: Quality of Solutions; 6.5 Conclusions; 7 Conclusions and Future Work; 7.1 Future Work
Series Title: SpringerBriefs in applied sciences and technology., Computational intelligence.
Responsibility: João Baúto, Rui Neves, Nuno Horta.

Abstract:

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  Read more...

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