Hill-climbing search and branch-and-bound are two heuristic search algorithms used in artificial intelligence. What is the difference between these two approaches?
Question posted in Artificial Intelligence
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Answers
Hill-climbing searches work by starting off with an initial guess of a solution, then iteratively making local changes to it until either the solution is found or the heuristic gets stuck in a local maximum. There are many ways to try to avoid getting stuck in local maxima, such as running many searches in parallel, or probabilistically choosing the successor state, etc. In many cases, hill-climbing algorithms will rapidly converge on the correct answer. However, none of these approaches are guaranteed to find the optimal solution.
Branch-and-bound solutions work by cutting the search space into pieces, exploring one piece, and then attempting to rule out other parts of the search space based on the information gained during each search. They are guaranteed to find the optimal answer eventually, though doing so might take a long time. For many problems, branch-and-bound based algorithms work quite well, since a small amount of information can rapidly shrink the search space.
In short, hill-climbing isn’t guaranteed to find the right answer, but often runs very quickly and gives good approximations. Branch-and-bound always finds the right answer, but might take a while to do so.
Hope this helps!
Hill climbing works like this:
Depth-first search with pruning (which is a simple form of branch and bound) works like this:
Branch and bound generally doesn’t scale to 1000+ variables and 1000+ values. Hill climbing does, but it gets stuck in local optima which can be fixed by adding Tabu Search.