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Iterative deepening A* (IDA*) is a graph traversal and path search algorithm that can find the shortest path between a designated start node and any member of a set of goal nodes in a weighted graph. It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the ...
Iterative deepening prevents this loop and will reach the following nodes on the following depths, assuming it proceeds left-to-right as above: 0: A; 1: A, B, C, E (Iterative deepening has now seen C, when a conventional depth-first search did not.) 2: A, B, D, F, C, G, E, F (It still sees C, but that it came later.
At each iteration of its main loop, A* needs to determine which of its paths to extend. It does so based on the cost of the path and an estimate of the cost required to extend the path all the way to the goal. Specifically, A* selects the path that minimizes = + ()
For general graphs, replacing the stack of the iterative depth-first search implementation with a queue would also produce a breadth-first search algorithm, although a somewhat nonstandard one. [7] Another possible implementation of iterative depth-first search uses a stack of iterators of the list of neighbors of a node, instead of a stack of ...
To solve this problem, Kociemba devised a lookup table that provides an exact heuristic for . [18] When the exact number of moves needed to reach G 1 {\displaystyle G_{1}} is available, the search becomes virtually instantaneous: one need only generate 18 cube states for each of the 12 moves and choose the one with the lowest heuristic each time.
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An important example of this is a tree: during a traversal it may be assumed that all "ancestor" vertices of the current vertex (and others depending on the algorithm) have already been visited.