Abstract
This research uses a Niche Genetic Algorithm (NGA) called Dynamic-radius Species-conserving Genetic Algorithm (DSGA) to select stocks to purchase from the Dow Jones Index. DSGA uses a set of training data to produce a set of rules. These rules are then used to predict stock prices. DSGA is an NGA that uses a clustering algorithm enhanced by a tabu list and radial variations. DSGA also uses a shared fitness algorithm to investigate different areas of the domain. This research applies the DSGA algorithm to training data which produces a set of rules. The rules are applied to a set of testing data to obtain results. The DSGA algorithm did very well in predicting stock movement.
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Brown, M.S., Pelosi, M.J., Dirska, H. (2013). Dynamic-Radius Species-Conserving Genetic Algorithm for the Financial Forecasting of Dow Jones Index Stocks. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_3
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DOI: https://doi.org/10.1007/978-3-642-39712-7_3
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