Use a random search algorithm with a broad search space and focus on training metrics to avoid overfitting.

Select a Bayesian optimization algorithm, define a narrow search space to limit exploration, and use validation metrics to monitor overfitting.

Avoid using Hyperopt for models sensitive to overfitting as it cannot prevent overfitting.

Increase the number of trials to ensure comprehensive coverage of the hyperparameter space, regardless of overfitting risks.