Abstract
This chapter presents a brief description on the scope of the problem addressed in the book which is the performance and optimization of algorithms based on pattern discovery. Additionally, the main goals to be achieved by this work are discussed along with a breakdown of the document’s structure.
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References
E.F. Fama, The behavior of stock-market prices. J. Bus. 38(1), 34–105 (1965)
A. Canelas, R. Neves, N. Horta, A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques. Expert Syst. Appl. 40(5), 1579–1590 (2013), http://www.sciencedirect.com/science/article/pii/S0957417412010561. https://doi.org/10.1016/j.eswa.2012.09.002
A. Canelas, R. Neves, N. Horta, Multi-dimensional pattern discovery in financial time series using sax-ga with extended robustness, in GECCO (2013). https://doi.org/10.1145/2464576.2464664
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Baúto, J., Neves, R., Horta, N. (2018). Introduction. 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_1
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DOI: https://doi.org/10.1007/978-3-319-73329-6_1
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Online ISBN: 978-3-319-73329-6
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