Run a fixed number of trials regardless of the computational cost, as the exploration of the hyperparameter space is more important than optimizing the computational resources.

Limit the number of trials to a small fixed value to minimize the computational cost, even if it means not exploring the hyperparameter space thoroughly.

Use Hyperopt's adaptive algorithms, such as Tree-structured Parzen Estimator (TPE), to intelligently select the next set of hyperparameters to evaluate based on the results of previous trials, allowing for more efficient exploration of the hyperparameter space.

Focus on manual hyperparameter tuning instead of using Hyperopt, as it provides more control over the computational cost while sacrificing the exploration of the hyperparameter space.