Hill climbing is an optimization algorithm that belongs to the family of local search methods. It is often used in artificial intelligence and computer science to find a solution to problems by iteratively making incremental changes to a solution and selecting the best one available. The process can be thought of as climbing a hill: the algorithm starts at a given point (a solution) and explores neighboring points (solutions) in the solution space.
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A local search method that retains one current candidate and repeatedly chooses an improving neighbor of its objective. For a conflict-minimization constraint satisfaction problem, a candidate can assign all variables within their constraint domains while violating some inter-variable constraints; the objective counts those violations. Zero conflicts certifies a solution, while a positive-conflict local minimum need not certify impossibility. Plateaus and local minima motivate sideways moves, randomized choices and restarting. Prescribed variables must remain fixed in every initialization and move.