Abstract
Abstract: Various researches and studies have shown that machine learning techniques like neural network have the ability to learn and predict the general trend of stock market. Artificial intelligence and different machine learning techniques have been widely implemented in the field of forecasting stock prices for a long time. However, selecting the best model and best hyperparameters for these models is highly necessary for better accuracy in prediction. Given the huge number of architecture types and hyper-parameters for each model, it is not practical to find the best combination for best accuracy. Therefore, in this research we used evolution algorithm to optimize model architecture and hyper-parameters. Promising results are found in stock prediction. Keywords: Neural Network, Long Short-Term Memory, Recurrent Neural Network, Dense Neural Network, Gated Recurrent Unit, Stock Prediction, Evolution Algorithm.
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