Try Optuna which is relatively faster than GridSearchCV. Also n_jobs = -1 further reduces time. Another point is to tune parameters that matter. Not all parameters will give you maximum improvement in results. Read this blog for further info:
To extend my comment:
As I mentioned you can set the parameter n_jobs to -1 or instead using RandomizedGridSearch (which also receives n_jobs parameter)
Regarding to the parameter grid, I always select my grid so that the default values are included and from there, some values less and greater than the default (for continuous parameters) and the same logic ...
RIPPER (Repeated Incremental Pruning to Produce Error Reduction) is a method to automatically learn rule sets.
It has shown to perform better than other decision tree algorithms (e.g., C4.5). However, it performs work than Random Forest.
Gaussian processes are a way of interpolating between data using a kernel fit, but with a covariance matrix. Think of it as kernel regression, but with confidence intervals that can be drawn at every point.
This diagram from this medium article is explains how a combination of a Gaussian process and an acquisition function works in Bayesian optimization: