Random search optimization strategy.
Random search samples configurations randomly from the search space. Often surprisingly effective and much faster than grid search for high-dimensional spaces.
Options
:n_trials- Number of trials to run (default: 100):timeout- Total timeout in ms (default: 3600000 = 1 hour):early_stop- Stop if score reaches threshold:verbose- Print progress (default: true):latin_hypercube- Use Latin Hypercube Sampling for better coverage (default: false)
Example
Optimizer.optimize(suite, params,
strategy: :random,
n_trials: 50,
metric: :score
)When to Use
Random search is recommended when:
- Search space is large (many parameters or wide ranges)
- Some parameters are more important than others (random search explores all)
- You have limited time/budget for optimization
- Grid search would take too long
Latin Hypercube Sampling
Enable latin_hypercube: true for better coverage of the search space.
LHS ensures samples are spread evenly across each parameter's range.