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  2. Best-first search - Wikipedia

    en.wikipedia.org/wiki/Best-first_search

    Best-first search is a class of search algorithms which explores a graph by expanding the most promising node chosen according to a specified rule.. Judea Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function () which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to ...

  3. A* search algorithm - Wikipedia

    en.wikipedia.org/wiki/A*_search_algorithm

    A* is an informed search algorithm, or a best-first search, meaning that it is formulated in terms of weighted graphs: starting from a specific starting node of a graph, it aims to find a path to the given goal node having the smallest cost (least distance travelled, shortest time, etc.).

  4. Beam search - Wikipedia

    en.wikipedia.org/wiki/Beam_search

    Beam search with width 3 (animation) In computer science, beam search is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is a modification of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states ...

  5. Category:Greedy algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Greedy_algorithms

    Download as PDF; Printable version; ... Pages in category "Greedy algorithms" ... Best-first search; G. Greedoid; Greedy algorithm; Greedy algorithm for Egyptian ...

  6. Consistent heuristic - Wikipedia

    en.wikipedia.org/wiki/Consistent_heuristic

    In fact, if the search graph is given cost ′ (,) = (,) + () for a consistent , then A* is equivalent to best-first search on that graph using Dijkstra's algorithm. [3] In the unusual event that an admissible heuristic is not consistent, a node will need repeated expansion every time a new best (so-far) cost is achieved for it.

  7. ID3 algorithm - Wikipedia

    en.wikipedia.org/wiki/ID3_algorithm

    It uses a greedy strategy by selecting the locally best attribute to split the dataset on each iteration. The algorithm's optimality can be improved by using backtracking during the search for the optimal decision tree at the cost of possibly taking longer. ID3 can overfit the training data. To avoid overfitting, smaller decision trees should ...

  8. 3 Cracker Barrel Employees Fired After Refusing to Seat Group ...

    www.aol.com/lifestyle/3-cracker-barrel-employees...

    Three employees at a Maryland Cracker Barrel have reportedly been dismissed after staff refused to seat a group of students with special needs on Dec. 3 Superintendent of Charles County Public ...

  9. Iterative deepening A* - Wikipedia

    en.wikipedia.org/wiki/Iterative_deepening_A*

    It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the remaining cost to get to the goal from the A* search algorithm. Since it is a depth-first search algorithm, its memory usage is lower than in A*, but unlike ordinary iterative deepening search, it ...