Creating Stock Trading Rules Using Graph-Based Estimation of Distribution Algorithm

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@InProceedings{Li:2014:CECc,

• title = "Creating Stock Trading Rules Using Graph-Based Estimation of Distribution Algorithm",
• author = "Xianneng Li and Wen He and Kotaro Hirasawa",
• pages = "731--738",
• booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation",
• year = "2014",
• month = "6-11 " # jul,
• editor = "Carlos A. {Coello Coello}",
• address = "Beijing, China",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, genetic network programming, Evolutionary Algorithms with Statistical and Machine Learning Techniques, Estimation of distribution algorithms",
• DOI = "doi:10.1109/CEC.2014.6900421",
• abstract = "Though there are numerous approaches developed currently, exploring the practical applications of estimation of distribution algorithm (EDA) has been reported to be one of the most important challenges in this field. This paper is dedicated to extend EDA to solve one of the most active research problems, stock trading, which has been rarely revealed in the EDA literature. A recent proposed graph-based EDA called reinforced probabilistic model building genetic network programming (RPMBGNP) is investigated to create stock trading rules. With its distinguished directed graph-based individual structure and the reinforcement learning-based probabilistic modelling, we demonstrate the effectiveness of RPMBGNP for the stock trading task through real-market stock data, where much higher profits are obtained than traditional non-EDA models.",
• notes = "WCCI2014",

}

Genetic Programming entries for Xianneng Li Wen He Kotaro Hirasawa

Citations