Global Search Algorithms

Global search algorithms explore the search space more broadly, using randomization or systematic patterns to avoid getting trapped in local optima.

Algorithm Examples

Algorithm	Example
Random Search	random_search_example.py
Grid Search	grid_search_example.py
Random Restart Hill Climbing	random_restart_hill_climbing_example.py
Stochastic Hill Climbing	stochastic_hill_climbing_example.py
Powell’s Method	powells_method_example.py
Pattern Search	pattern_search_example.py

When to Use Global Search

Global search algorithms are best suited for:

• Multimodal search spaces with multiple local optima

• Initial exploration before fine-tuning with local search

• Unknown search spaces where the landscape is not well understood

• Baseline comparisons (especially Random Search)

See Optimizers for detailed algorithm descriptions.