Abstract
This chapter discusses the sequential implementation of the SAX/GA algorithm which is an algorithm designed for the optimization of market trading solutions. SAX/GA uses the SAX representation to validate the similarity between a possible solution and the training dataset, while the GA optimizes the pool of trading strategies based on a function that defines the quality of each solution. Later on, a benchmark analysis is presented in order to understand the performance of SAX/GA and locate possible regions that can take advantage of a parallel implementation.
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References
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). 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
B.T. Zhang, J.J. Kim, Comparison of selection methods for evolutionary optimization. Evol. Optim. 2, 55–70 (2000), http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.552&rep=rep1&type=pdf
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Baúto, J., Neves, R., Horta, N. (2018). SAX/GA CPU Approach. 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_4
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DOI: https://doi.org/10.1007/978-3-319-73329-6_4
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