Cryptocurrencies have become a prominent financial asset, but their prices are highly volatile and challenging to predict. Therefore, there is a need for robust models capable of accurately forecasting cryptocurrency prices. ML offers a promising approach for this prediction based on historical price datasets. This study compares several techniques, including Long Short Term Memory and neural networks. Hyperparameters play a crucial role in the performance of these models, and their optimization is essential for achieving high accuracy. To this end, we employ an evolutionary programming approach for hyperparameter optimization. This approach evaluates the performance of these models before and after hyperparameter optimization, aiming to identify the most effective model for cryptocurrency price prediction.