A* search algorithm is based on?

Search algorithms are classified by how they choose the next node to expand. Blind (uninformed) strategies such as breadth-first search, depth-first search, and uniform-cost search pick the next node using a fixed rule (FIFO order, LIFO order, or lowest path cost) with no estimate of how close a node is to the goal. Best-first search is the general informed strategy: it maintains an evaluation function f(n) and always expands the node judged most promising by that function, which can blend the path cost so far with a heuristic estimate of the remaining distance.

A* search defines its evaluation function as f(n) = g(n) + h(n), where g(n) is the actual cost from the start node and h(n) is a heuristic estimate of the cost to the goal. Because A* selects the next node purely by this evaluation function f(n), it is a direct instance of best-first search: A* is simply best-first search using f(n) = g(n) + h(n) as its guiding function.

• Breadth-first search expands nodes in the fixed order they were generated (FIFO queue) with no evaluation function, so it cannot express A*'s f(n) = g(n) + h(n) rule.

• Depth-first search expands the deepest unexpanded node first (LIFO/stack order) with no evaluation function, so it too cannot express A*'s g(n) + h(n) rule.

• Uniform-cost search is itself an uninformed special case of best-first search that uses only g(n) (path cost), with no heuristic term — it lacks the h(n) component that defines A*.

• Best-first search is the general framework that selects nodes by an evaluation function f(n); A* is its best-known informed instance, using f(n) = g(n) + h(n).

So the A* search algorithm is based on best-first search.