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  2. Hill climbing - Wikipedia

    en.wikipedia.org/wiki/Hill_climbing

    In simple hill climbing, the first closer node is chosen, whereas in steepest ascent hill climbing all successors are compared and the closest to the solution is chosen. Both forms fail if there is no closer node, which may happen if there are local maxima in the search space which are not solutions.

  3. Min-conflicts algorithm - Wikipedia

    en.wikipedia.org/wiki/Min-conflicts_algorithm

    In fact, Constraint Satisfaction Problems that respond best to a min-conflicts solution do well where a greedy algorithm almost solves the problem. Map coloring problems do poorly with Greedy Algorithm as well as Min-Conflicts. Sub areas of the map tend to hold their colors stable and min conflicts cannot hill climb to break out of the local ...

  4. Local search (constraint satisfaction) - Wikipedia

    en.wikipedia.org/wiki/Local_search_(constraint...

    A drawback of hill climbing with moves that do not decrease cost is that it may cycle over assignments of the same cost. Tabu search [1] [2] [3] overcomes this problem by maintaining a list of "forbidden" assignments, called the tabu list. In particular, the tabu list typically contains only the most recent changes.

  5. Iterated local search - Wikipedia

    en.wikipedia.org/wiki/Iterated_local_search

    Iterated Local Search [1] [2] (ILS) is a term in applied mathematics and computer science defining a modification of local search or hill climbing methods for solving discrete optimization problems. Local search methods can get stuck in a local minimum, where no improving neighbors are available.

  6. Mean shift - Wikipedia

    en.wikipedia.org/wiki/Mean_shift

    Mean-shift is a hill climbing algorithm which involves shifting this kernel iteratively to a higher density region until convergence. Every shift is defined by a mean shift vector. The mean shift vector always points toward the direction of the maximum increase in the density.

  7. Beam search - Wikipedia

    en.wikipedia.org/wiki/Beam_search

    Conversely, a beam width of 1 corresponds to a hill-climbing algorithm. [3] The beam width bounds the memory required to perform the search. Since a goal state could potentially be pruned, beam search sacrifices completeness (the guarantee that an algorithm will terminate with a solution, if one exists).

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  9. Simulated annealing - Wikipedia

    en.wikipedia.org/wiki/Simulated_annealing

    Simulated annealing searching for a maximum. The objective here is to get to the highest point. In this example, it is not enough to use a simple hill climb algorithm, as there are many local maxima. By cooling the temperature slowly the global maximum is found.