Local Search Algorithms

Local search algorithms explore the search space by making small, incremental moves from the current position. They are efficient for finding local optima but may get stuck without escaping mechanisms.

Algorithm Examples

Algorithm	Example
Hill Climbing	hill_climbing_example.py
Repulsing Hill Climbing	repulsing_hill_climbing_example.py
Simulated Annealing	simulated_annealing_example.py
Downhill Simplex	downhill_simplex_example.py

When to Use Local Search

Local search algorithms are best suited for:

• Smooth search spaces where nearby points have similar scores

• Fine-tuning around a known good region

• Fast convergence when a good starting point is available

• Limited computational budget where few evaluations are possible

See Optimizers for detailed algorithm descriptions.