Abstract
This work presents a detailed study of the parallelization of a Genetic Algorithm (GA) for pattern discovery using the Symbolic Aggregate approXimation (SAX). The main purpose was to understand the structure of the SAX/GA and if it could benefit from the highly parallel computation that the GPU provides. Each step of the genetic algorithm was analysed thoroughly and, based on a benchmark analysis applied to the sequential SAX/GA, three distinct solutions are proposed, each with its own benefits.
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Baúto, J., Neves, R., Horta, N. (2018). Conclusions and Future Work. In: Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-73329-6_7
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DOI: https://doi.org/10.1007/978-3-319-73329-6_7
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73328-9
Online ISBN: 978-3-319-73329-6
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