Abstract
Grammatical evolution is an evolutionary method that is used for the automated generation of programs. Over the years, different studies have proven the relevance and efficiency of this method in a wide array of fields. This method can substitute various other machine learning algorithms and older architectures to provide good efficiency and performance for optimization of algorithms. The paper aims to apply GE to predict the price of various stock market indices. An open source implementation PonyGE2 that was developed by the Natural Computing and Applications group at UCD has been employed in this paper. With the help of an objective function and a search space defined by the grammar, the evolutionary computation of the optimum solution is achieved. The effect of tweaking the grammar rules to provide different production options helped visualize the difference in the fitness of the functions generated and the consequential effect on the output produced.
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D’Mello, L., Jeswani, A., Johnson, J. (2020). Stock Price Prediction Using Grammatical Evolution. In: Vasudevan, H., Michalas, A., Shekokar, N., Narvekar, M. (eds) Advanced Computing Technologies and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3242-9_36
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DOI: https://doi.org/10.1007/978-981-15-3242-9_36
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