The process of automated parameter tuning typically involves several steps:
Data Collection: Gathering initial data through experiments or simulations. Algorithm Selection: Choosing appropriate optimization algorithms such as genetic algorithms, Bayesian optimization, or neural networks. Model Training: Using collected data to train the chosen model. Optimization: Running the model to predict optimal parameters. Validation: Conducting experiments to validate the model's predictions.
