Abstract The hill climbing algorithm is an optimization technique that iteratively improves a solution by evaluating neighboring solutions in search of a local maximum or minimum. Starting with an initial guess, the algorithm examines nearby “neighbor” solutions, moving to a better neighbor if one is found. This process continues until no improvement can be made locally, at which point the algorithm may restart from a new random position.
This implementation focuses on discrete optimization problems, supporting integer and categorical parameters only.