Dehypgpols: a genetic programming with evolutionary hyper-parameter optimization and its application for stock market trend prediction

HIGHLIGHTS

• who: nas9983 from the Bitlis Eren University https://orcidorg/0000-0001-6439, Bitlis Eren University, Department of Computer Engineering, Bitlis, Turkey have published the research: DEHypGpOls: A Genetic Programming with Evolutionary Hyper-Parameter Optimization and its Application for Stock Market Trend Prediction, in the Journal: (JOURNAL)
• what: The main reason is that user parameters of a GP algorithm are manually configured for a limited number of set and trial efforts, and it does not ensure the hyper-parameter optimality of the GP algorithm for the training dataset. In the current study, to alleviate this shortcoming . . .
  

   

  

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